refs.bib

@book{bynum2021pyomo,
  title     = {Pyomo - Optimization Modeling in Python, 3rd Edition},
  author    = {Bynum, Michael L. and Hackebeil, Gabriel A. and Hart, William E. and Laird, Carl D. and Nicholson, Bethany L. and Siirola, John D. and Watson, Jean-Paul and Woodruff, David L.},
  year      = {2021},
  publisher = {Springer}
}

@article{lee2021idaes,
  title={The IDAES process modeling framework and model library—Flexibility for process simulation and optimization},
  author={Lee, Andrew and Ghouse, Jaffer H and Eslick, John C and Laird, Carl D and Siirola, John D and Zamarripa, Miguel A and Gunter, Dan and Shinn, John H and Dowling, Alexander W and Bhattacharyya, Debangsu and others},
  journal={Journal of advanced manufacturing and processing},
  volume={3},
  number={3},
  pages={e10095},
  year={2021},
  publisher={Wiley Online Library}
}

@misc{meta_open_source_2024, title={React}, url={https://react.dev/}, journal={react.dev}, author={Meta Open Source}, year={2024} }

@article{wuDigitalTwinNetworks2021,
  title      = {Digital {Twin} {Networks}: {A} {Survey}},
  volume     = {8},
  issn       = {2327-4662},
  shorttitle = {Digital {Twin} {Networks}},
  url        = {https://ieeexplore.ieee.org/abstract/document/9429703?casa_token=wxSwkW5hdbsAAAAA:ChznySxCC_PTyPefBSlIHYidz7_iYZzhy47WCwdroN9RenO_chr9mUD8CvqlHbP1S-u-CCpcGg},
  doi        = {10.1109/JIOT.2021.3079510},
  abstract   = {Digital twin network (DTN) is an emerging network that utilizes digital twin (DT) technology to create the virtual twins of physical objects. DTN realizes co-evolution between physical and virtual spaces through DT modeling, communication, computing, data processing technologies. In this article, we present a comprehensive survey of DTN to explore the potentiality of DT. First, we elaborate key features and definitions of DTN. Next, the key technologies and the technical challenges in DTN are discussed. Furthermore, we depict the typical application scenarios, such as manufacturing, aviation, healthcare, 6G networks, intelligent transportation systems, and urban intelligence in smart cities. Finally, the new trends and open research issues related to DTN are pointed out.},
  number     = {18},
  urldate    = {2024-03-12},
  journal    = {IEEE Internet of Things Journal},
  author     = {Wu, Yiwen and Zhang, Ke and Zhang, Yan},
  month      = sep,
  year       = {2021},
  note       = {Conference Name: IEEE Internet of Things Journal},
  keywords   = {Digital twin, Market research, Computational modeling, Data models, Digital twin (DT), digital twin network (DTN), DT modeling, Mirrors, Predictive models, Smart cities},
  pages      = {13789--13804},
  file       = {IEEE Xplore Abstract Record:C\:\\Users\\bd65\\Zotero\\storage\\X5MLNGD8\\9429703.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\KTMQWUVY\\Wu et al. - 2021 - Digital Twin Networks A Survey.pdf:application/pdf}
}


@article{Bikmukhametov_Jäschke_2020,
  title        = {Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models},
  volume       = {138},
  issn         = {0098-1354},
  doi          = {10.1016/j.compchemeng.2020.106834},
  abstractnote = {Machine learning models are often considered as black-box solutions which is one of the main reasons why they are still not widely used in operation of process engineering systems. One approach to overcome this problem is to combine machine learning with first principles models of a process engineering system. In this work, we investigate different methods of combining machine learning with first principles and test them on a case study of multiphase flowrate estimation in a petroleum production system. However, the methods can be applied to any process engineering system. The results show that by adding physics-based models to machine learning, it is possible not only to improve the performance of the purely black-box machine learning models, but also to make them more transparent and interpretable. We also propose a step-by-step procedure for selecting a method for combining physics and machine learning depending on the process engineering system conditions.},
  journal      = {Computers & Chemical Engineering},
  author       = {Bikmukhametov, Timur and Jäschke, Johannes},
  year         = {2020},
  month        = jul,
  pages        = {106834}
}



 @article{Sansana_Joswiak_Castillo_Wang_Rendall_Chiang_Reis_2021,
  title        = {Recent trends on hybrid modeling for Industry 4.0},
  volume       = {151},
  issn         = {0098-1354},
  doi          = {10.1016/j.compchemeng.2021.107365},
  abstractnote = {The chemical processing industry has relied on modeling techniques for process monitoring, control, diagnosis, optimization, and design, especially since the third industrial revolution and the emergence of Process Systems Engineering. The fourth industrial revolution, connected to massive digitization, made it possible to collect and process large volumes of data triggering the development of data-driven frameworks for knowledge extraction. However, one must not leave behind the successful solutions developed over decades based on first principle mechanistic modeling approaches. At present, both industry and researchers are realizing the need for new ways to incorporate process and phenomenological knowledge in big data and machine learning frameworks, leading to more robust and intelligible artificial intelligence solutions, capable of assisting the target stakeholders in their activities and decision processes. In this article, we review hybrid modeling techniques, associated system identification methodologies and model assessment criteria. Applications in chemical and biochemical processes are also referred.},
  journal      = {Computers & Chemical Engineering},
  author       = {Sansana, Joel and Joswiak, Mark N. and Castillo, Ivan and Wang, Zhenyu and Rendall, Ricardo and Chiang, Leo H. and Reis, Marco S.},
  year         = {2021},
  month        = aug,
  pages        = {107365}
}



@article{delporte:hal-00826243,
  title       = {{Accelerometer and Magnetometer Based Gyroscope Emulation on Smart Sensor for a Virtual Reality Application}},
  author      = {Delporte, Baptiste and Perroton, Laurent and Grandpierre, Thierry and Trichet, Jacques},
  url         = {https://hal.science/hal-00826243},
  journal     = {{Sensors \& Transducers.}},
  publisher   = {{IFSA Publishing, S.L.}},
  volume      = {14-1},
  number      = {Special Issue ISSN 1726-5479},
  pages       = {p32-p47},
  year        = {2012},
  month       = Mar,
  keywords    = {Smart sensor ; Sensor fusion ; Accelerometer ; Magnetometer ; Angular velocity ; Gyroscope},
  pdf         = {https://hal.science/hal-00826243/file/AccelerometerAndMagnetometerBasedGyroscopeSensorAndTransducteurJournal.pdf},
  hal_id      = {hal-00826243},
  hal_version = {v1}
}


@article{Shah_2008,
  title        = {Sensor-fusion Strategies for Process and Performance Monitoring},
  abstractnote = {Most of the major plant, factory, process, equipment and tool disruptions are preventable, and yet preventative fault detection and diagnosis section are not the norm in most industries. It is not uncommon to see simple and preventable faults disrupt the operation of an entire integrated manufacturing facility. For examples faults such as malfunctioning sensors or actuators, inoperative alarm systems, poor controller tuning or configuration can render the most sophisticated control systems useless. Such disruptions can cost in the excess of $1 million per day. Over the last decade the fields of multivariate statistics, and controller performance monitoring techniques have merged to develop powerful sensing and condition-based monitoring systems for predictive fault detection and diagnosis. These methods rely on the notion of sensor fusion whereby data from many sensors and units are combined to give a holistic picture of health of an integrated plant. Such methods combined with embedded digital intelligence are at a stage where such strategies are being implemented for off-line and on-line deployment.},
  author       = {Shah, Sirish L},
  year         = {2008},
  language     = {en}
}

@article{udugamaRoleBigData2020,
  title    = {The {Role} of {Big} {Data} in {Industrial} ({Bio})chemical {Process} {Operations}},
  volume   = {59},
  issn     = {0888-5885},
  url      = {https://doi.org/10.1021/acs.iecr.0c01872},
  doi      = {10.1021/acs.iecr.0c01872},
  abstract = {With the emergence of Industry 4.0 and Big Data initiatives, there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this article is to provide a perspective of the current status of Big-Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this article provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of statistical process monitoring and (engineering) process control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored, and a path forward is proposed.},
  number   = {34},
  urldate  = {2024-04-24},
  journal  = {Industrial \& Engineering Chemistry Research},
  author   = {Udugama, Isuru A. and Gargalo, Carina L. and Yamashita, Yoshiyuki and Taube, Michael A. and Palazoglu, Ahmet and Young, Brent R. and Gernaey, Krist V. and Kulahci, Murat and Bayer, Christoph},
  month    = aug,
  year     = {2020},
  note     = {Publisher: American Chemical Society},
  pages    = {15283--15297},
  file     = {Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\EV44T8QC\\Udugama et al. - 2020 - The Role of Big Data in Industrial (Bio)chemical P.pdf:application/pdf}
}


@article{saeidiFusionAirborneLiDAR2014,
  title    = {Fusion of {Airborne} {LiDAR} {With} {Multispectral} {SPOT} 5 {Image} for {Enhancement} of {Feature} {Extraction} {Using} {Dempster}–{Shafer} {Theory}},
  volume   = {52},
  issn     = {1558-0644},
  url      = {https://ieeexplore.ieee.org/abstract/document/6728743?casa_token=rM_Yn8pTeC4AAAAA:WxkwnDwNt-dnuWiqWi61ssbYkFNvRShi69I5RgPzZUP4UPhTGX5Ge_2XqKSkhDa1c12dCSR41tbh},
  doi      = {10.1109/TGRS.2013.2294398},
  abstract = {This paper presents an application of data-driven Dempster-Shafer theory (DST) of evidence to fuse multisensor data for land-cover feature extraction. Over the years, researchers have focused on DST for a variety of applications. However, less attention has been given to generate and interpret probability, certainty, and conflict maps. Moreover, quantitative assessment of DST performance is often overlooked. In this paper, for implementation of DST, two main types of data were used: multisensor data such as Light Detection and Ranging (LiDAR) and multispectral satellite imagery [Satellite Pour l'Observation de la Terre 5 (SPOT 5)]. The objectives are to classify land-cover types from fused multisensor data using DST, to quantitatively assess the accuracy of the classification, and to examine the potential of slope data derived from LiDAR for feature detection. First, we derived the normalized difference vegetation index (NDVI) from SPOT 5 image and the normalized digital surface model (DSM) (nDSM) from LiDAR by subtracting the digital terrain model from the DSM. The two products were fused using the DST algorithm, and the accuracy of the classification was assessed. Second, we generated a surface slope from LiDAR and fused it with NDVI. Subsequently, the classification accuracy was assessed using an IKONOS image of the study area as ground truth data. From the two processing stages, the NDVI/nDSM fusion had an overall accuracy of 88.7\%, while the NDVI/slope fusion had 75.3\%. The result indicates that NDVI/nDSM integration performed better than NDVI/slope. Although the overall accuracy of the former is better than the latter (NDVI/slope), the contribution of individual class reveals that building extraction from fused slope and NDVI performed poorly. This study proves that DST is a time- and cost-effective method for accurate land-cover feature identification and extraction without the need for a prior knowledge of the scene. Furthermore, the ability to generate other products like certainty, conflict, and maximum probability maps for better visual understanding of the decision process makes it more reliable for applications such as urban planning, forest management, 3-D feature extraction, and map updating.},
  number   = {10},
  urldate  = {2024-04-23},
  journal  = {IEEE Transactions on Geoscience and Remote Sensing},
  author   = {Saeidi, Vahideh and Pradhan, Biswajeet and Idrees, Mohammed O. and Abd Latif, Zulkiflee},
  month    = oct,
  year     = {2014},
  note     = {Conference Name: IEEE Transactions on Geoscience and Remote Sensing},
  keywords = {Accuracy, Buildings, Dempster–Shafer theory (DST), Dempster¿Shafer theory (DST), Feature extraction, fusion, GIS, Laser radar, Light Detection and Ranging (LiDAR), remote sensing, Soil, Uncertainty, Vegetation},
  pages    = {6017--6025}
}



@article{alvesdearaujojuniorDigitalTwinsWater2021,
  title     = {Digital {Twins} of the {Water} {Cooling} {System} in a {Power} {Plant} {Based} on {Fuzzy} {Logic}},
  volume    = {21},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  issn      = {1424-8220},
  url       = {https://www.mdpi.com/1424-8220/21/20/6737},
  doi       = {10.3390/s21206737},
  abstract  = {In the search for increased productivity and efficiency in the industrial sector, a new industrial revolution, called Industry 4.0, was promoted. In the electric sector, power plants seek to adapt these new concepts to optimize electric power generation processes, as well as to reduce operating costs and unscheduled downtime intervals. In these plants, PID control strategies are commonly used in water cooling systems, which use fans to perform the thermal exchange between water and the ambient air. However, as the nonlinearities of these systems affect the performance of the drivers, sometimes a greater number of fans than necessary are activated to ensure water temperature control which, consequently, increases energy expenditure. In this work, our objective is to develop digital twins for a water cooling system with auxiliary equipment, in terms of the decision making of the operator, to determine the correct number of fans. This model was developed based on the algorithm of automatic extraction of fuzzy rules, derived from the SCADA of a power plant located in the capital of Paraíba, Brazil. The digital twins can update the fuzzy rules in the case of new events, such as steady-state operation, starting and stopping ramps, and instability. The results from experimental tests using data from 11 h of plant operations demonstrate the robustness of the proposed digital twin model. Furthermore, in all scenarios, the average percentage error was less than 5\% and the average absolute temperature error was below 3 °C.},
  language  = {en},
  number    = {20},
  urldate   = {2024-04-23},
  journal   = {Sensors},
  author    = {Alves de Araujo Junior, Carlos Antonio and Mauricio Villanueva, Juan Moises and Almeida, Rodrigo José Silva de and Azevedo de Medeiros, Isaac Emmanuel},
  month     = jan,
  year      = {2021},
  note      = {Number: 20
               Publisher: Multidisciplinary Digital Publishing Institute},
  keywords  = {cooling system, digital twins, fuzzy logic, power plants},
  pages     = {6737},
  file      = {Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\7ZML52PY\\Alves de Araujo Junior et al. - 2021 - Digital Twins of the Water Cooling System in a Pow.pdf:application/pdf}
}


@article{munirArtificialIntelligenceData2021,
  title    = {Artificial {Intelligence} and {Data} {Fusion} at the {Edge}},
  volume   = {36},
  issn     = {1557-959X},
  url      = {https://ieeexplore.ieee.org/abstract/document/9475883?casa_token=wQUWbbl-ahsAAAAA:ii1zdrxn5PHGJJB2CeWNDLYtkceEv15qrHM7nFJ9BSqadW8Tl3XxXg6h9vQglntpjOzNPlWU56fv},
  doi      = {10.1109/MAES.2020.3043072},
  abstract = {Artificial intelligence (AI), owing to recent breakthroughs in deep learning, has revolutionized applications and services in almost all technology domains including aerospace. AI and deep learning rely on huge amounts of training data that are mostly generated at the network edge by Internet of Things (IoT) devices and sensors. Bringing the sensed data from the edge of a distributed network to a centralized cloud is often infeasible because of the massive data volume, limited network bandwidth, and real-time application constraints. Consequently, there is a desire to push AI frontiers to the network edge toward utilizing the enormous amount of data generated by IoT devices near the data source. The merger of edge computing and AI has engendered a new discipline, that is, AI at the edge or edge intelligence. To help AI make sense of gigantic data at the network edge, data fusion is of paramount significance and goes hand in hand with AI. This article focuses on data fusion and AI at the edge. In this article, we propose a framework for data fusion and AI processing at the edge. We then provide a comparative discussion of different data fusion and AI models and architectures. We discuss multiple levels of fusion and different types of AI, and how different types of AI align with different levels of fusion. We then highlight the benefits of combining data fusion with AI at the edge. The methods of AI and data fusion at the edge detailed in this article are applicable to many application domains including aerospace systems. We evaluate the effectiveness of combined data fusion and AI at the edge using convolutional neural network models and multiple hardware platforms suitable for edge computing. Experimental results reveal that combining AI with data fusion can impart a speedup of 9.8× while reducing energy consumption up to 88.5\% over AI without data fusion. Furthermore, results demonstrate that data fusion either maintains or improves the accuracy of AI in most cases. For our experiments, data fusion imparts a maximum improvement of 15.8\% in accuracy to AI.},
  number   = {7},
  urldate  = {2024-04-23},
  journal  = {IEEE Aerospace and Electronic Systems Magazine},
  author   = {Munir, Arslan and Blasch, Erik and Kwon, Jisu and Kong, Joonho and Aved, Alexander},
  month    = jul,
  year     = {2021},
  note     = {Conference Name: IEEE Aerospace and Electronic Systems Magazine},
  keywords = {Computer architecture, Data integration, Data models, Deep learning, Distributed databases, Training data},
  pages    = {62--78},
  file     = {IEEE Xplore Abstract Record:C\:\\Users\\bd65\\Zotero\\storage\\LNUF626K\\9475883.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\3K3YFZYG\\Munir et al. - 2021 - Artificial Intelligence and Data Fusion at the Edg.pdf:application/pdf}
}


@inproceedings{lermerCreationDigitalTwins2019,
  title     = {Creation of {Digital} {Twins} by {Combining} {Fuzzy} {Rules} with {Artificial} {Neural} {Networks}},
  volume    = {1},
  url       = {https://ieeexplore.ieee.org/abstract/document/8926914?casa_token=5q9LH2nCF6cAAAAA:-hfSYPU2QZgPkyCZ6rvf7BTP1iNqxHC5F35jjH2nCO3zK5ht_sR4vzUqgVIAv26OA_9jNCANtL8f},
  doi       = {10.1109/IECON.2019.8926914},
  abstract  = {The rise of digital twins in the manufacturing industry is accompanied by new possibilities, like process automation and condition monitoring, real time simulations and quality and maintenance prediction are just a few advantages which can be realized. This paper takes a novel approach by extracting the fundamental knowledge of a data set from a production process and mapping it to an expert fuzzy rule set. Afterwards, new fundamental augmented data is generated by exploring the feature space of the previously generated fuzzy rule set. At the same time, a high number of artificial neural network (ANN) models with different hyperparameter configurations are created. The best models are chosen, in line with the idea of survival of the fittest, and improved with the additional training data sets, generated by the fuzzy rule simulation. It is shown that ANN models can be improved by adding fundamental knowledge represented by the discovered fuzzy rules. Those models can represent digitized machines as digital twins. The architecture and effectiveness of the digital twin is evaluated within an industry 4.0 use case.},
  urldate   = {2024-04-23},
  booktitle = {{IECON} 2019 - 45th {Annual} {Conference} of the {IEEE} {Industrial} {Electronics} {Society}},
  author    = {Lermer, Matthias and Reich, Christoph},
  month     = oct,
  year      = {2019},
  note      = {ISSN: 2577-1647},
  keywords  = {Artificial Neural Networks, Data models, Digital Twin, Fuzzy Logic, Industry 4.0, Mathematical model, Predictive maintenance, Predictive models, Production, Simulation, Training data},
  pages     = {5849--5854},
  file      = {IEEE Xplore Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\PNFJF6C3\\Lermer and Reich - 2019 - Creation of Digital Twins by Combining Fuzzy Rules.pdf:application/pdf}
}


@article{renganathanAerodynamicDataFusion2020,
  title    = {Aerodynamic {Data} {Fusion} {Toward} the {Digital} {Twin} {Paradigm}},
  volume   = {58},
  issn     = {0001-1452},
  url      = {https://doi.org/10.2514/1.J059203},
  doi      = {10.2514/1.J059203},
  abstract = {This paper considers the fusion of two aerodynamic data sets originating from differing types of physical or computer experiments. This paper specifically addresses the fusion of 1) noisy and in-complete fields from wind-tunnel measurements and 2) deterministic but biased fields from numerical simulations. These two data sources are fused in order to estimate the true field that best matches measured quantities that serve as the ground truth. For example, two sources of pressure fields about an aircraft are fused based on measured forces and moments from a wind-tunnel experiment. A fundamental challenge in this problem is that the true field is unknown and cannot be estimated with 100\% certainty. A Bayesian framework is employed to infer the true fields conditioned on measured quantities of interest; essentially a statistical correction to the data is performed. The fused data may then be used to construct more accurate surrogate models suitable for early stages of aerospace design. An extension of the proper orthogonal decomposition with constraints is also introduced to solve the same problem. Both methods are demonstrated on fusing the pressure distributions for flow past the RAE2822 airfoil and the Common Research Model wing at transonic conditions. Comparison of both methods reveals that the Bayesian method is more robust when data are scarce and capable of also accounting for uncertainties in the data. Furthermore, given adequate data, the proper-orthogonal-decomposition-based and Bayesian approaches lead to surprisingly similar results.},
  number   = {9},
  urldate  = {2024-04-23},
  journal  = {AIAA Journal},
  author   = {Renganathan, S. Ashwin and Harada, Kohei and Mavris, Dimitri N.},
  year     = {2020},
  note     = {Publisher: American Institute of Aeronautics and Astronautics
              \_eprint: https://doi.org/10.2514/1.J059203},
  pages    = {3902--3918}
}


@article{carvalhoDesignSustainableChemical2008,
  series     = {{ECCE}-6},
  title      = {Design of sustainable chemical processes: {Systematic} retrofit analysis generation and evaluation of alternatives},
  volume     = {86},
  issn       = {0957-5820},
  shorttitle = {Design of sustainable chemical processes},
  url        = {https://www.sciencedirect.com/science/article/pii/S0957582008000281},
  doi        = {10.1016/j.psep.2007.11.003},
  abstract   = {The objective of this paper is to present a generic and systematic methodology for identifying the feasible retrofit design alternatives of any chemical process. The methodology determines a set of mass and energy indicators from steady-state process data, establishes the operational and design targets, and through a sensitivity-based analysis, identifies the design alternatives that can match a set of design targets. The significance of this indicator-based method is that it is able to identify alternatives, where one or more performance criteria (factors) move in the same direction thereby eliminating the need to identify trade-off-based solutions. These indicators are also able to reduce (where feasible) a set of safety indicators. An indicator sensitivity analysis algorithm has been added to the methodology to define design targets and to generate sustainable process alternatives. A computer-aided tool has been developed to facilitate the calculations needed for the application of the methodology. The application of the indicator-based methodology and the developed software are highlighted through a process flowsheet for the production of vinyl chlorine monomer (VCM).},
  number     = {5},
  urldate    = {2024-04-23},
  journal    = {Process Safety and Environmental Protection},
  author     = {Carvalho, Ana and Gani, Rafiqul and Matos, Henrique},
  month      = sep,
  year       = {2008},
  keywords   = {Indicator sensitivity algorithm, Mass and energy indicators, Process design, Safety index, Sustainability metrics},
  pages      = {328--346},
  file       = {ScienceDirect Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\JQA5GBA9\\S0957582008000281.html:text/html;Submitted Version:C\:\\Users\\bd65\\Zotero\\storage\\DUHCRMMF\\Carvalho et al. - 2008 - Design of sustainable chemical processes Systemat.pdf:application/pdf}
}



@book{kempPinchAnalysisEnergy2020,
  title      = {Pinch {Analysis} for {Energy} and {Carbon} {Footprint} {Reduction}: {User} {Guide} to {Process} {Integration} for the {Efficient} {Use} of {Energy}},
  isbn       = {978-0-08-102537-6},
  shorttitle = {Pinch {Analysis} for {Energy} and {Carbon} {Footprint} {Reduction}},
  abstract   = {Pinch Analysis for Energy and Carbon Footprint Reduction is the only dedicated pinch analysis and process integration guide, covering a breadth of material from foundational knowledge to in-depth processes. Readers are introduced to the main concepts of pinch analysis, the calculation of energy targets for a given process, the pinch temperature, and the golden rules of pinch-based design to meet energy targets. More advanced topics include the extraction of stream data necessary for a pinch analysis, the design of heat exchanger networks, hot and cold utility systems, combined heat and power (CHP), refrigeration, batch- and time-dependent situations, and optimization of system operating conditions, including distillation, evaporation, and solids drying.  This new edition offers tips and techniques for practical applications, supported by several detailed case studies. Examples stem from a wide range of industries, including buildings and other non-process situations. This reference is a must-have guide for chemical process engineers, food and biochemical engineers, plant engineers, and professionals concerned with energy optimization, including building designers.  Covers practical analysis of both new and existing processes  Teaches readers to extract the stream data necessary for a pinch analysis and describes the targeting process in depth; includes a downloadable spreadsheet to calculate energy targets  Demonstrates how to achieve the targets by heat recovery, utility system design, and process change  Updated to include carbon footprint, water and hydrogen pinch, developments in industrial applications and software, site data reconciliation, additional case studies, and answers to selected exercises},
  language   = {en},
  publisher  = {Butterworth-Heinemann},
  author     = {Kemp, Ian C. and Lim, Jeng Shiun},
  month      = aug,
  year       = {2020},
  note       = {Google-Books-ID: yW\_KDwAAQBAJ},
  keywords   = {Technology \& Engineering / Chemical \& Biochemical}
}

@inproceedings{lugaresiREALTIMESIMULATIONMANUFACTURING2018,
  title      = {{REAL}-{TIME} {SIMULATION} {IN} {MANUFACTURING} {SYSTEMS}: {CHALLENGES} {AND} {RESEARCH} {DIRECTIONS}},
  shorttitle = {{REAL}-{TIME} {SIMULATION} {IN} {MANUFACTURING} {SYSTEMS}},
  url        = {https://ieeexplore.ieee.org/document/8632542},
  doi        = {10.1109/WSC.2018.8632542},
  abstract   = {In the last years, the increase of data availability together with enhanced resource flexibility shed light on the possibility to develop planning and control methods with real-time inputs. Literature is rich of approaches to simulate, to quickly evaluate system performances, and to take decisions based on optimization criteria. Further, simulation has been identified as one of the pillars for the Industry 4.0 revolution. However, the lack of a generally recognized approach and methodology to deal with real-time decision-making through simulation is evident. Simulation approaches can and should play a central role in industry for the years to come. This position paper analyses the current research context with a brief state of the art on existing approaches, includes considerations about the issues for implementing Real-Time Simulation (RTS) concepts and their current state of development. Finally, it outlines research directions for the simulation community.},
  urldate    = {2024-04-23},
  booktitle  = {2018 {Winter} {Simulation} {Conference} ({WSC})},
  author     = {Lugaresi, Giovanni and Matta, Andrea},
  month      = dec,
  year       = {2018},
  note       = {ISSN: 1558-4305},
  keywords   = {Analytical models, Data collection, Data models, Industries, Manufacturing, Real-time systems, Synchronization},
  pages      = {3319--3330},
  file       = {IEEE Xplore Abstract Record:C\:\\Users\\bd65\\Zotero\\storage\\MZUYRFU2\\8632542.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\7M734ES6\\Lugaresi and Matta - 2018 - REAL-TIME SIMULATION IN MANUFACTURING SYSTEMS CHA.pdf:application/pdf}
}

@article{pistikopoulosProcessSystemsEngineering2021,
  title      = {Process systems engineering – \textit{{The} generation next?}},
  volume     = {147},
  issn       = {0098-1354},
  shorttitle = {Process systems engineering – {\textless}i{\textgreater}{The} generation next?},
  url        = {https://www.sciencedirect.com/science/article/pii/S0098135421000302},
  doi        = {10.1016/j.compchemeng.2021.107252},
  abstract   = {Process Systems Engineering (PSE) is the scientific discipline of integrating scales and components describing the behavior of a physicochemical system, via mathematical modelling, data analytics, design, optimization and control. PSE provides the ‘glue’ within scientific chemical engineering, and offers a scientific basis and computational tools towards addressing contemporary and future challenges such as in energy, environment, the ‘industry of tomorrow’ and sustainability. This perspective article offers a guide towards the next generation of PSE developments by looking at its history, core competencies, current status and ongoing trends.},
  urldate    = {2024-04-23},
  journal    = {Computers \& Chemical Engineering},
  author     = {Pistikopoulos, E N and Barbosa-Povoa, Ana and Lee, Jay H and Misener, Ruth and Mitsos, Alexander and Reklaitis, G V and Venkatasubramanian, V and You, Fengqi and Gani, Rafiqul},
  month      = apr,
  year       = {2021},
  keywords   = {Control, Modelling, Optimization, Process systems engineering, Supply chain, Synthesis-design},
  pages      = {107252}
}


@article{keshariDiscreteEventSimulation2018,
  series   = {6th {CIRP} {Global} {Web} {Conference} – {Envisaging} the future manufacturing, design, technologies and systems in innovation era ({CIRPe} 2018)},
  title    = {Discrete {Event} {Simulation} {Approach} for {Energy} {Efficient} {Resource} {Management} in {Paper} \& {Pulp} {Industry}},
  volume   = {78},
  issn     = {2212-8271},
  url      = {https://www.sciencedirect.com/science/article/pii/S2212827118312526},
  doi      = {10.1016/j.procir.2018.08.324},
  abstract = {To improve energy efficiency of a Paper \& Pulp industry, full capacity utilization of the processing equipment and diverse energy source utilities are promoted, while same time processing of diverse raw materials, meeting of strict processing time requirements, seamless material flow and obtaining required product quality are important concerns to be fulfilled. This is similar to a resources management problem while energy is considered as one of the measurable resources, wherein estimation of energy consumptions is crucial/complicated aspect. Discretized form of energy consumption (approximate energy consumed by unit amount of intermediate products at different intermediate processing stages) concept is utilized here with discrete event simulation for consumed energy estimation/analysis. The research deals with a real time working Paper \& Pulp industry resource management problem, wherein overall energy consumption rate and production rate were analyzed for some commonly occurring situation of efficiency reduction of the processing station(s). Energy efficient solutions are evaluated with varying production flow (varying the percentage of raw material) and resources management options (turning on/off paper machines). Energy efficient solution which ensures minimum loss in production rate and minimizes disturbances in material flow is suggested for implementation.},
  urldate  = {2024-04-23},
  journal  = {Procedia CIRP},
  author   = {Keshari, Anupam and Sonsale, Anand N. and Sharma, Brij K. and Pohekar, Sanjay D.},
  month    = jan,
  year     = {2018},
  keywords = {Discrete Event Simulation, Energy Efficiency, Paper \& Pulp Industry, Resource Management},
  pages    = {2--7},
  file     = {ScienceDirect Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\ZWZ8BH9E\\S2212827118312526.html:text/html}
}


@article{trigueirodesousajuniorDiscreteSimulationbasedOptimization2019,
  title      = {Discrete simulation-based optimization methods for industrial engineering problems: {A} systematic literature review},
  volume     = {128},
  issn       = {0360-8352},
  shorttitle = {Discrete simulation-based optimization methods for industrial engineering problems},
  url        = {https://www.sciencedirect.com/science/article/pii/S036083521830682X},
  doi        = {10.1016/j.cie.2018.12.073},
  abstract   = {In recent years, some attention has been driven to modeling, simulation, and optimization techniques capable of representing and improving discrete event systems. These techniques can support decision making helping to determine the best scenario on a combinatorial search space with stochastic variables. This paper presents findings from a systematic literature review of discrete simulation-based optimization applied to industrial engineering problems. It indicates the most frequent contexts, problems, methods, tools, and intended results of discrete-simulation based studies published in the last 25 years (1991–2016) in scientific journals and conference proceedings. The four research questions presented a scenario to help practitioners and researchers to develop simulation optimization projects for industrial engineering problems. A conclusion presented the gap and prospects found during the writing of the research.},
  urldate    = {2024-04-23},
  journal    = {Computers \& Industrial Engineering},
  author     = {Trigueiro de Sousa Junior, Wilson and Barra Montevechi, José Arnaldo and de Carvalho Miranda, Rafael and Teberga Campos, Afonso},
  month      = feb,
  year       = {2019},
  keywords   = {Discrete event simulation, Industrial engineering, Optimization},
  pages      = {526--540},
  file       = {ScienceDirect Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\SRSP3DTS\\S036083521830682X.html:text/html}
}



@article{abarAgentBasedModelling2017,
  title      = {Agent {Based} {Modelling} and {Simulation} tools: {A} review of the state-of-art software},
  volume     = {24},
  issn       = {1574-0137},
  shorttitle = {Agent {Based} {Modelling} and {Simulation} tools},
  url        = {https://www.sciencedirect.com/science/article/pii/S1574013716301198},
  doi        = {10.1016/j.cosrev.2017.03.001},
  abstract   = {The key intent of this work is to present a comprehensive comparative literature survey of the state-of-art in software agent-based computing technology and its incorporation within the modelling and simulation domain. The original contribution of this survey is two-fold: (1) Present a concise characterization of almost the entire spectrum of agent-based modelling and simulation tools, thereby highlighting the salient features, merits, and shortcomings of such multi-faceted application software; this article covers eighty five agent-based toolkits that may assist the system designers and developers with common tasks, such as constructing agent-based models and portraying the real-time simulation outputs in tabular/graphical formats and visual recordings. (2) Provide a usable reference that aids engineers, researchers, learners and academicians in readily selecting an appropriate agent-based modelling and simulation toolkit for designing and developing their system models and prototypes, cognizant of both their expertise and those requirements of their application domain. In a nutshell, a significant synthesis of Agent Based Modelling and Simulation (ABMS) resources has been performed in this review that stimulates further investigation into this topic.},
  urldate    = {2024-04-23},
  journal    = {Computer Science Review},
  author     = {Abar, Sameera and Theodoropoulos, Georgios K. and Lemarinier, Pierre and O’Hare, Gregory M. P.},
  month      = may,
  year       = {2017},
  keywords   = {Agent Based Modelling and Simulation (ABMS) tools, Artificial life / social science simulations, Modelling complex systems, Multi-agent computing, Software agent, Swarm intelligence},
  pages      = {13--33},
  file       = {ScienceDirect Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\N8CE6G57\\S1574013716301198.html:text/html}
}


@article{parkerModelPredictiveControl2023,
  title    = {Model predictive control simulations with block-hierarchical differential–algebraic process models},
  volume   = {132},
  issn     = {0959-1524},
  url      = {https://www.sciencedirect.com/science/article/pii/S0959152423002007},
  doi      = {10.1016/j.jprocont.2023.103113},
  abstract = {Hierarchical optimization modeling in an algebraic modeling environment facilitates construction of large models with many interchangeable sub-models. However, for dynamic simulation and optimization applications, a flattened structure that preserves time indexing is preferred. To convert from a structure that facilitates model construction to a structure that facilitates dynamic optimization, the concept of reshaping an optimization model is introduced along with the recently developed utilities in the Pyomo algebraic modeling environment that make this possible. The application of these utilities to model predictive control simulations and partial differential equation (PDE) discretization stability analysis is discussed, and two challenging nonlinear model predictive control case studies are presented to demonstrate the advantages of this approach.},
  urldate  = {2024-04-23},
  journal  = {Journal of Process Control},
  author   = {Parker, Robert B. and Nicholson, Bethany L. and Siirola, John D. and Biegler, Lorenz T.},
  month    = dec,
  year     = {2023},
  keywords = {Dynamic optimization, Model predictive control, Modeling, Simulation, Software},
  pages    = {103113},
  file     = {ScienceDirect Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\23E9QVPQ\\S0959152423002007.html:text/html}
}


@book{modelicaspec,
  title       = {Modelica ® -a {unified} {object}-{oriented} {language} for {systems} {modeling}: {language} {specification}},
  url         = {https://specification.modelica.org/maint/3.6/MLS.pdf},
  institution = {Modelica Association},
  year        = {2023},
  pages       = {5}
}

@article{anylogic,
  author  = {The {Anylogic} {Company}},
  title   = {Multimethod {Simulation} {Modelling} for {Business} {Applications}: Overview with guided model building example},
  journal = {Anylogic.com},
  url     = {https://www.anylogic.com/use-of-simulation/},
  year    = 2019
}
%https://www.anylogic.com/resources/white-papers/an-introduction-to-digital-twin-development/
@article{anylogictwin,
  author  = {The {Anylogic} {Company}},
  title   = {An {Introduction} to {Digital} {Twin} {Development}},
  journal = {Anylogic.com},
  url     = {https://www.anylogic.com/resources/white-papers/an-introduction-to-digital-twin-development/},
  year    = 2019
}
@article{rodicIndustryNewSimulation2017,
  title     = {Industry 4.0 and the {New} {Simulation} {Modelling} {Paradigm}},
  volume    = {50},
  copyright = {http://creativecommons.org/licenses/by-nc-nd/3.0},
  issn      = {1581-1832},
  url       = {https://www.sciendo.com/article/10.1515/orga-2017-0017},
  doi       = {10.1515/orga-2017-0017},
  abstract  = {Abstract
               
               Background and Purpose
               : The aim of this paper is to present the influence of Industry 4.0 on the development of the new simulation modelling paradigm, embodied by the Digital Twin concept, and examine the adoption of the new paradigm via a multiple case study involving real-life R\&D cases involving academia and industry.
               
               
               Design
               : We introduce the Industry 4.0 paradigm, presents its background, current state of development and its influence on the development of the simulation modelling paradigm. Further, we present the multiple case study methodology and examine several research and development projects involving automated industrial process modelling, presented in recent scientific publications and conclude with lessons learned.
               
               
               Results:
               We present the research problems and main results from five individual cases of adoption of the new simulation modelling paradigm. Main lesson learned is that while the new simulation modelling paradigm is being adopted by big companies and SMEs, there are significant differences depending on company size in problems that they face, and the methodologies and technologies they use to overcome the issues.
               
               
               Conclusion:
               While the examined cases indicate the acceptance of the new simulation modelling paradigm in the industrial and scientific communities, its adoption in academic environment requires close cooperation with industry partners and diversification of knowledge of researchers in order to build integrated, multi-level models of cyber-physical systems. As shown by the presented cases, lack of tools is not a problem, as the current generation of general purpose simulation modelling tools offers adequate integration options.},
  language  = {en},
  number    = {3},
  urldate   = {2024-04-21},
  journal   = {Organizacija},
  author    = {Rodič, Blaž},
  month     = aug,
  year      = {2017},
  pages     = {193--207},
  file      = {Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\6BYG2LBY\\Rodič - 2017 - Industry 4.0 and the New Simulation Modelling Para.pdf:application/pdf}
}


@article{depaulaferreiraSimulationIndustryStateoftheart2020,
  title      = {Simulation in industry 4.0: {A} state-of-the-art review},
  volume     = {149},
  issn       = {0360-8352},
  shorttitle = {Simulation in industry 4.0},
  url        = {https://www.sciencedirect.com/science/article/pii/S0360835220305635},
  doi        = {10.1016/j.cie.2020.106868},
  abstract   = {Simulation is a key technology for developing planning and exploratory models to optimize decision making as well as the design and operations of complex and smart production systems. It could also aid companies to evaluate the risks, costs, implementation barriers, impact on operational performance, and roadmap toward Industry 4.0. Although several advances have been made in this domain, studies that systematically characterize and analyze the development of simulation-based research in Industry 4.0 are scarce. Therefore, this study aims to investigate the state-of-the-art research performed on the intersecting area of simulation and the field of Industry 4.0. Initially, a conceptual framework describing Industry 4.0 in terms of enabling technologies and design principles for modeling and simulation of Industry 4.0 scenarios is proposed. Thereafter, literature on simulation technologies and Industry 4.0 design principles is systematically reviewed using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. This study reveals an increasing trend in the number of publications on simulation in Industry 4.0 within the last four years. In total, 10 simulation-based approaches and 17 Industry 4.0 design principles were identified. A cross-analysis of concepts and evaluation of models’ development suggest that simulation can capture the design principles of Industry 4.0 and support the investigation of the Industry 4.0 phenomenon from different perspectives. Finally, the results of this study indicate hybrid simulation and digital twin as the primary simulation-based approaches in the context of Industry 4.0.},
  urldate    = {2024-04-20},
  journal    = {Computers \& Industrial Engineering},
  author     = {de Paula Ferreira, William and Armellini, Fabiano and De Santa-Eulalia, Luis Antonio},
  month      = nov,
  year       = {2020},
  keywords   = {Artificial Intelligence, Agent-Based Simulation, Augmented Reality, Discrete-Event Simulation, System Dynamics, Virtual Reality},
  pages      = {106868},
  file       = {Accepted Version:C\:\\Users\\bd65\\Zotero\\storage\\AVGBU88N\\de Paula Ferreira et al. - 2020 - Simulation in industry 4.0 A state-of-the-art rev.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\4JWQ49BM\\S0360835220305635.html:text/html}
}


@article{maxim_5ws_2019,
  title      = {The {5W}’s for {Control} as {Part} of {Industry} 4.0: {Why}, {What}, {Where}, {Who}, and {When}—{A} {PID} and {MPC} {Control} {Perspective}},
  volume     = {4},
  copyright  = {http://creativecommons.org/licenses/by/3.0/},
  issn       = {2411-5134},
  shorttitle = {The {5W}’s for {Control} as {Part} of {Industry} 4.0},
  url        = {https://www.mdpi.com/2411-5134/4/1/10},
  doi        = {10.3390/inventions4010010},
  abstract   = {The advent of Industry 4.0 (I4.0) has pushed technology beyond its physical limits, making the process prone to errors and poorer performance. Whether it is about smart manufacturing where mass customization is envisaged, or collaborative human–robot engineering systems, the pyramid of process operation has changed to a matrix form and control is the backbone of all process elements. The paper gives a concise guideline as to how, when, where, and what to apply when it comes to choosing the most suitable control strategy as a function of multi-parameter objective optimization. Both proportional-integral-derivative (PID) and model predictive control (MPC) control are addressed in this context.},
  language   = {en},
  number     = {1},
  urldate    = {2024-04-20},
  journal    = {Inventions},
  author     = {Maxim, Anca and Copot, Dana and Copot, Cosmin and Ionescu, Clara M.},
  month      = mar,
  year       = {2019},
  note       = {Number: 1
                Publisher: Multidisciplinary Digital Publishing Institute},
  keywords   = {control design, digital control, Industry 4.0, loop interaction, monitoring, MPC control, networked control, performance, PID control, process automation, process control, robustness, scalability, tuning, wireless sensors},
  pages      = {10},
  file       = {Full Text PDF:/home/bert/Zotero/storage/66SCNIXU/Maxim et al. - 2019 - The 5W’s for Control as Part of Industry 4.0 Why,.pdf:application/pdf}
}


@article{qiao_industrial_2021,
  title    = {Industrial big-data-driven and {CPS}-based adaptive production scheduling for smart manufacturing},
  volume   = {59},
  issn     = {0020-7543},
  url      = {https://doi.org/10.1080/00207543.2020.1836417},
  doi      = {10.1080/00207543.2020.1836417},
  abstract = {Smart manufacturing that involves tight integration of the physical system and cyber system is a hot topic in both industry and academia in the era of the Internet and big data. However, the dynamic and uncertain manufacturing environment introduces a significant adaptive issue of production scheduling, which is one of the pivotal tasks for smart manufacturing. This paper focuses on this problem and proposes a closed-loop adaptive scheduling solution based on the Cyber-Physical Production System (CPPS) with four phases: production data acquisition (PDA), dynamic disturbance identification (DDI), scheduling strategy adjustment (SSA), and schedule scheme generation (SSG). In the DDI phase, in view of the disturbance classification, a disturbance identification procedure based on CPPS monitoring is studied to ensure real-time response. In the SSA phase, an industrial big-data-driven scheduling strategy adjustment method is proposed, which consists of GA-based offline knowledge learning and KNN-based online adjustment, to enhance the system adaptability. We apply and verify the proposed adaptive scheduling solution on an experimental semiconductor manufacturing system, and the results demonstrate that the proposed method outperforms the dynamic scheduling method in terms of multiple objectives under different disturbance levels.},
  number   = {23},
  urldate  = {2024-04-20},
  journal  = {International Journal of Production Research},
  author   = {Qiao, Fei and Liu, Juan and Ma, Yumin},
  month    = dec,
  year     = {2021},
  note     = {Publisher: Taylor \& Francis
              \_eprint: https://doi.org/10.1080/00207543.2020.1836417},
  keywords = {adaptive scheduling, cyber physical production system, industrial big data, Smart manufacturing, uncertainty},
  pages    = {7139--7159},
  file     = {Full Text PDF:/home/bert/Zotero/storage/RS5KGIX8/Qiao et al. - 2021 - Industrial big-data-driven and CPS-based adaptive .pdf:application/pdf}
}



@article{georgiadis_optimization-based_2019,
  title      = {Optimization-{Based} {Scheduling} for the {Process} {Industries}: {From} {Theory} to {Real}-{Life} {Industrial} {Applications}},
  volume     = {7},
  copyright  = {http://creativecommons.org/licenses/by/3.0/},
  issn       = {2227-9717},
  shorttitle = {Optimization-{Based} {Scheduling} for the {Process} {Industries}},
  url        = {https://www.mdpi.com/2227-9717/7/7/438},
  doi        = {10.3390/pr7070438},
  abstract   = {Scheduling is a major component for the efficient operation of the process industries. Especially in the current competitive globalized market, scheduling is of vital importance to most industries, since profit margins are miniscule. Prof. Sargent was one of the first to acknowledge this. His breakthrough contributions paved the way to other researchers to develop optimization-based methods that can address a plethora of process scheduling problems. Despite the plethora of works published by the scientific community, the practical implementation of optimization-based scheduling in industrial real-life applications is limited. In most industries, the optimization of production scheduling is seen as an extremely complex task and most schedulers prefer the use of a simulation-based software or manual decision, which result to suboptimal solutions. This work presents a comprehensive review of the theoretical concepts that emerged in the last 30 years. Moreover, an overview of the contributions that address real-life industrial case studies of process scheduling is illustrated. Finally, the major reasons that impede the application of optimization-based scheduling are critically analyzed and possible remedies are discussed.},
  language   = {en},
  number     = {7},
  urldate    = {2024-04-20},
  journal    = {Processes},
  author     = {Georgiadis, Georgios P. and Elekidis, Apostolos P. and Georgiadis, Michael C.},
  month      = jul,
  year       = {2019},
  note       = {Number: 7
                Publisher: Multidisciplinary Digital Publishing Institute},
  keywords   = {mixed-integer programming, optimization, process scheduling, process system engineering},
  pages      = {438},
  file       = {Full Text PDF:/home/bert/Zotero/storage/9PBKAWNR/Georgiadis et al. - 2019 - Optimization-Based Scheduling for the Process Indu.pdf:application/pdf}
}


@article{schluse_experimentable_2018,
  title     = {Experimentable {Digital} {Twins}—{Streamlining} {Simulation}-{Based} {Systems} {Engineering} for {Industry} 4.0},
  volume    = {14},
  copyright = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  issn      = {1551-3203, 1941-0050},
  url       = {http://ieeexplore.ieee.org/document/8289327/},
  doi       = {10.1109/TII.2018.2804917},
  abstract  = {Digital twins represent real objects or subjects with their data, functions, and communication capabilities in the digital world. As nodes within the internet of things, they enable networking and thus the automation of complex value-added chains. The application of simulation techniques brings digital twins to life and makes them experimentable; digital twins become experimentable digital twins (EDTs). Initially, these EDTs communicate with each other purely in the virtual world. The resulting networks of interacting EDTs model different application scenarios and are simulated in virtual testbeds, providing new foundations for comprehensive simulation-based systems engineering. Its focus is on EDTs, which become more detailed with every single application. Thus, complete digital representations of the respective real assets and their behaviors are created successively. The networking of EDTs with real assets leads to hybrid application scenarios in which EDTs are used in combination with real hardware, thus realizing complex control algorithms, innovative user interfaces, or mental models for intelligent systems.},
  language  = {en},
  number    = {4},
  urldate   = {2024-04-13},
  journal   = {IEEE Transactions on Industrial Informatics},
  author    = {Schluse, Michael and Priggemeyer, Marc and Atorf, Linus and Rossmann, Juergen},
  month     = apr,
  year      = {2018},
  pages     = {1722--1731},
  file      = {Schluse et al. - 2018 - Experimentable Digital Twins—Streamlining Simulati.pdf:/home/bert/Zotero/storage/AP6ELBSP/Schluse et al. - 2018 - Experimentable Digital Twins—Streamlining Simulati.pdf:application/pdf}
}


@article{bigdataknowlegepyramid,
  author     = {Jennex, Murray E.},
  title      = {Big Data, the Internet of Things, and the Revised Knowledge Pyramid},
  year       = {2017},
  issue_date = {November 2017},
  publisher  = {Association for Computing Machinery},
  address    = {New York, NY, USA},
  volume     = {48},
  number     = {4},
  issn       = {0095-0033},
  url        = {https://doi.org/10.1145/3158421.3158427},
  doi        = {10.1145/3158421.3158427},
  abstract   = {The knowledge pyramid has been used for several years to illustrate the hierarchical relationships between data, information, knowledge, and wisdom. An earlier version of this paper presented a revised knowledge-KM pyramid that included processes such as filtering and sense making, reversed the pyramid by positing there was more knowledge than data, and showed knowledge management as an extraction of the pyramid. This paper expands the revised knowledge pyramid to include the Internet of Things and Big Data. The result is a revision of the data aspect of the knowledge pyramid. Previous thought was of data as reflections of reality as recorded by sensors. Big Data and the Internet of Things expand sensors and readings to create two layers of data. The top layer of data is the traditional transaction / operational data and the bottom layer of data is an expanded set of data reflecting massive data sets and sensors that are near mirrors of reality. The result is a knowledge pyramid that appears as an hourglass.},
  journal    = {SIGMIS Database},
  month      = {nov},
  pages      = {69–79},
  numpages   = {11},
  keywords   = {analytics, big data, internet of things, knowledge management, knowledge pyramid}
}

  

@book{birta_modelling_2019,
  address    = {Cham},
  series     = {Simulation {Foundations}, {Methods} and {Applications}},
  title      = {Modelling and {Simulation}: {Exploring} {Dynamic} {System} {Behaviour}},
  copyright  = {http://www.springer.com/tdm},
  isbn       = {978-3-030-18868-9 978-3-030-18869-6},
  shorttitle = {Modelling and {Simulation}},
  url        = {http://link.springer.com/10.1007/978-3-030-18869-6},
  language   = {en},
  urldate    = {2024-04-13},
  publisher  = {Springer International Publishing},
  author     = {Birta, Louis G. and Arbez, Gilbert},
  year       = {2019},
  doi        = {10.1007/978-3-030-18869-6},
  file       = {Birta and Arbez - 2019 - Modelling and Simulation Exploring Dynamic System.pdf:/home/bert/Zotero/storage/A7WEH6I7/Birta and Arbez - 2019 - Modelling and Simulation Exploring Dynamic System.pdf:application/pdf}
}


@article{wisdompyramid,
  abstract = { This paper revisits the data-information-knowledge-wisdom (DIKW) hierarchy by examining the articulation of the hierarchy in a number of widely read textbooks, and analysing their statements about the nature of data, information, knowledge, and wisdom. The hierarchy referred to variously as the ‘Knowledge Hierarchy’, the ‘Information Hierarchy’ and the ‘Knowledge Pyramid’ is one of the fundamental, widely recognized and ‘taken-for-granted’ models in the information and knowledge literatures. It is often quoted, or used implicitly, in definitions of data, information and knowledge in the information management, information systems and knowledge management literatures, but there has been limited direct discussion of the hierarchy. After revisiting Ackoff’s original articulation of the hierarchy, definitions of data, information, knowledge and wisdom as articulated in recent textbooks in information systems and knowledge management are reviewed and assessed, in pursuit of a consensus on definitions and transformation processes. This process brings to the surface the extent of agreement and dissent in relation to these definitions, and provides a basis for a discussion as to whether these articulations present an adequate distinction between data, information, and knowledge. Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge, but there is less consensus in the description of the processes that transform elements lower in the hierarchy into those above them, leading to a lack of definitional clarity. In addition, there is limited reference to wisdom in these texts. },
  author   = {Jennifer Rowley},
  doi      = {10.1177/0165551506070706},
  eprint   = { https://doi.org/10.1177/0165551506070706 },
  journal  = {Journal of Information Science},
  number   = {2},
  pages    = {163–180},
  title    = {The wisdom hierarchy: representations of the DIKW hierarchy},
  url      = { 
              
              https://doi.org/10.1177/0165551506070706
              
              
              
              },
  volume   = {33},
  year     = {2007}
}


@article{9987476,
  author   = {Eneyew, Dagimawi D. and Capretz, Miriam A. M. and Bitsuamlak, Girma T.},
  journal  = {IEEE Access},
  title    = {Toward Smart-Building Digital Twins: BIM and IoT Data Integration},
  year     = {2022},
  volume   = {10},
  number   = {},
  pages    = {130487-130506},
  keywords = {Digital twins;Smart buildings;Semantics;Architecture;Interoperability;Resource description framework;Data integration;BIM;data integration;data interoperability;digital twin;IoT;ontology;semantic interoperability;smart building},
  doi      = {10.1109/ACCESS.2022.3229370}
}

@misc{ahuora,
  key     = {Ahuora - Center for Smart Energy Systems, University of Waikato},
  note    = {Ahuora - Center for Smart Energy Systems, University of Waikato. https://ahuora.waikato.ac.nz/},
  journal = {ahuora.waikato.ac.nz},
  url     = {https://ahuora.waikato.ac.nz/},
  year    = {2024}
}



@misc{mbie,
  title   = {Energy overview {\textbar} {Ministry} of {Business}, {Innovation} \& {Employment} https://www.mbie.govt.nz},
  url     = {https://www.mbie.govt.nz/building-and-energy/energy-and-natural-resources/energy-statistics-and-modelling/energy-publications-and-technical-papers/energy-in-new-zealand/energy-in-new-zealand-2023/energy-overview/},
  urldate = {2024-03-19},
  file    = {Energy overview | Ministry of Business, Innovation & Employment:C\:\\Users\\bd65\\Zotero\\storage\\RQYVZ6AB\\energy-overview.html:text/html}
}



@misc{mbievm,
  key  = {Ahuora - Center for Smart Energy Systems, University of Waikato},
  note = { Vision Matauranga: Unlocking the Innovation Potential of Māori Knowledge, Resources and People, Ministry for Business, Innovation, and Employment. https://www.mbie.govt.nz/assets/vision-matauranga-booklet.pdf},
  url  = {https://www.mbie.govt.nz/assets/vision-matauranga-booklet.pdf},
  year = {2024}
}


"@misc{physicsunitop,
  author       = { Haochen Li, David Spelman, and John Sansalone},
  title        = {{U}nit {O}peration and {P}rocess {M}odeling with {P}hysics-{I}nformed {M}achine {L}earning},
  howpublished = {\url{https://ascelibrary.org/doi/full/10.1061/JOEEDU.EEENG-7467}},
  year         = {},
  note         = {[Accessed 05-03-2024]}
}"

"@article{ZOBEIRY2021104232,
  title    = {A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications},
  journal  = {Engineering Applications of Artificial Intelligence},
  volume   = {101},
  pages    = {104232},
  year     = {2021},
  issn     = {0952-1976},
  doi      = {https://doi.org/10.1016/j.engappai.2021.104232},
  url      = {https://www.sciencedirect.com/science/article/pii/S0952197621000798},
  author   = {Navid Zobeiry and Keith D. Humfeld},
  keywords = {Physics-informed machine learning, Theory-guided feature engineering, Convective heat transfer, Advanced manufacturing, Industry 4.0},
  abstract = {A physics-informed neural network is developed to solve conductive heat transfer partial differential equation (PDE), along with convective heat transfer PDEs as boundary conditions (BCs), in manufacturing and engineering applications where parts are heated in ovens. Since convective coefficients are typically unknown, current analysis approaches based on trial-and-error finite element (FE) simulations are slow. The loss function is defined based on errors to satisfy PDE, BCs and initial condition. An adaptive normalizing scheme is developed to reduce loss terms simultaneously. In addition, theory of heat transfer is used for feature engineering. The predictions for 1D and 2D cases are validated by comparing with FE results. While comparing with theory-agnostic ML methods, it is shown that only by using physics-informed activation functions, the heat transfer beyond the training zone can be accurately predicted. Trained models were successfully used for real-time evaluation of thermal responses of parts subjected to a wide range of convective BCs.}
}"
"@incollection{CRISTIU20201141,
  title     = {Augmenting Heat Balance of the Wastewater Treatment Plant Model and Improving Plant Control by Counteracting Temperature Disturbances},
  editor    = {Sauro Pierucci and Flavio Manenti and Giulia Luisa Bozzano and Davide Manca},
  series    = {Computer Aided Chemical Engineering},
  publisher = {Elsevier},
  volume    = {48},
  pages     = {1141-1146},
  year      = {2020},
  booktitle = {30th European Symposium on Computer Aided Process Engineering},
  issn      = {1570-7946},
  doi       = {https://doi.org/10.1016/B978-0-12-823377-1.50191-9},
  url       = {https://www.sciencedirect.com/science/article/pii/B9780128233771501919},
  author    = {Daniel Crîstiu and Melinda Simon-Várhelyi and Alexandra Veronica Luca and Marius Adrian Brehar and Vasile Mircea Cristea},
  keywords  = {wastewater treatment, heat balance, temperature disturbances, control system design},
  abstract  = {Municipal wastewater treatment has become one of the most challenging environmental problems due to the volume increase of urban wastewaters and stricter regulations imposed to effluent pollutants concentration. Besides the tough pollutants concentration and flow rate influent disturbances, the influent temperature changes also affect the complex biochemical processes taking place in WWTP bioreactors. As a result, the WWTP dynamic behavior description relying on the mass balance needs to be completed either with temperature correction factors or by comprehensive heat balance equations. The latter approach was considered in the present research, addressing a municipal WWTP using the activated sludge technology in the Anaerobic-Anoxic-Oxic (A2O) configuration. The heat balance enhanced WWTP model was developed on the basis of the Activated Sludge Model No. 1 (ASM1), coupled with a modified version of the Benchmark Simulation Model no. 1 (BSM1). The wastewater temperature changes were evaluated and a control system structure was proposed in order to counteract the negative effects of the influent temperature associated to other typical influent disturbances. Simulation results performed with the calibrated municipal WWTP model and the proposed control system demonstrate twofold benefits. They may reduce the aeration and pumping energy costs by 12 % and improve the effluent quality by 5 %.}
}"
"@article{9359733,
  author   = {Rathore, M. Mazhar and Shah, Syed Attique and Shukla, Dhirendra and Bentafat, Elmahdi and Bakiras, Spiridon},
  journal  = {IEEE Access},
  title    = {The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities},
  year     = {2021},
  volume   = {9},
  number   = {},
  pages    = {32030-32052},
  keywords = {Big Data;Digital twin;Patents;Industries;Systematics;Tools;Libraries;Digital twin;artificial intelligence;machine learning;big data;industry 40},
  doi      = {10.1109/ACCESS.2021.3060863}
}
"



"
@patent{sachsInferringDigitalTwins2023,
  title       = {Inferring digital twins from captured data},
  url         = {https://patents.google.com/patent/US11694094B2/en?q=(Digital+Twin+Process+Engineering)&oq=Digital+Twin+Process+Engineering},
  nationality = {US},
  language    = {en},
  assignee    = {SwimIt Inc},
  number      = {US11694094B2},
  urldate     = {2024-03-06},
  author      = {Sachs, Christopher David},
  month       = jul,
  year        = {2023},
  keywords    = {computing, digital twin, event data, schema, stream},
  file        = {Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\P4KR3ENV\\Sachs - 2023 - Inferring digital twins from captured data.pdf:application/pdf}
}
"
"
@article{kahveci_end--end_2022,
  title      = {An end-to-end big data analytics platform for {IoT}-enabled smart factories: {A} case study of battery module assembly system for electric vehicles},
  volume     = {63},
  issn       = {0278-6125},
  shorttitle = {An end-to-end big data analytics platform for {IoT}-enabled smart factories},
  url        = {https://www.sciencedirect.com/science/article/pii/S0278612522000450},
  doi        = {10.1016/j.jmsy.2022.03.010},
  abstract   = {Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and cost efficient big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an electric vehicle battery module assembly automation system designed by the Automation Systems Group at the University of Warwick, the UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments.},
  urldate    = {2024-03-08},
  journal    = {Journal of Manufacturing Systems},
  author     = {Kahveci, Sinan and Alkan, Bugra and Ahmad, Mus’ab H. and Ahmad, Bilal and Harrison, Robert},
  month      = apr,
  year       = {2022},
  keywords   = {Big data analytics, Data visualisation, Industry 4.0, IoT, Smart manufacturing},
  pages      = {214--223},
  file       = {Accepted Version:/home/bd65/Zotero/storage/HQWBQJ6S/Kahveci et al. - 2022 - An end-to-end big data analytics platform for IoT-.pdf:application/pdf;ScienceDirect Snapshot:/home/bd65/Zotero/storage/2VZDSYCI/S0278612522000450.html:text/html}
}
"
"
@book{elerEvaluatingDifferentNeural2022,
  title    = {Evaluating {Different} {Neural} {Networks} {Architectures} for the {Solution} of {Heat} {Conduction} {Problems} in {NVIDIA} {Modulus}},
  abstract = {The use of neural networks to address engineering problems is increasing considerably. A limitation of using neural networks is the need for large amounts of data to fit nonlinear problems with acceptable accuracy. An alternative to the purely data-driven approach is the physics-informed neural networks, which add physical constraints that significantly reduce the amount of data needed to achieve acceptable accuracy. In this work, we solve two simple heat conduction problems using PINNs, evaluating the complexity of different neural network architectures. A direct comparison to the analytical solution proved the PINNs to be good solvers for the evaluated partial differential equations. A fully connected neural network (FCN) handles the problem well for the steady-state case. However, a gated recursive unit (GRU) architecture is needed to solve a transient problem. For both problems, an architecture of 6 layers with 64 units each is sufficient to achieve good results.},
  author   = {Eler, Felipe and Souza, Anaximandro and Couto, Paulo and Coutinho, Alvaro},
  month    = nov,
  year     = {2022},
  file     = {Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\968YBEC5\\Eler et al. - 2022 - Evaluating Different Neural Networks Architectures.pdf:application/pdf}
}
"
"
@misc{openusdUniversalSceneDescription,
  title   = {Universal {Scene} {Description}: {Universal} {Scene} {Description} ({USD})},
  url     = {https://openusd.org/release/api/index.html},
  urldate = {2024-03-08},
  author  = {OpenUSD},
  file    = {Universal Scene Description\: Universal Scene Description (USD):C\:\\Users\\bd65\\Zotero\\storage\\MWXM9VWC\\index.html:text/html}
}
"

"
@article{app10134503,
  author         = {Grznár, Patrik and Gregor, Milan and Krajčovič, Martin and Mozol, Štefan and Schickerle, Marek and Vavrík, Vladimír and Ďurica, Lukáš and Marschall, Martin and Bielik, Tomáš},
  title          = {Modeling and Simulation of Processes in a Factory of the Future},
  journal        = {Applied Sciences},
  volume         = {10},
  year           = {2020},
  number         = {13},
  article-number = {4503},
  url            = {https://www.mdpi.com/2076-3417/10/13/4503},
  issn           = {2076-3417},
  abstract       = {Current trends in manufacturing, which are based on customisation and gradually customised production, are becoming the main initiator for the development of new manufacturing approaches. New manufacturing approaches are counted as the application of new behavioural management patterns that calculate the retained competencies of decision-making by the individual members of the system agent; the production becomes decentralised. The interaction of the members of such a system creates emergent behaviour, where the result cannot be accurately determined by ordinary methods and simulation must be applied. Modelling and simulation will, therefore, be an integral part of the planning and control of the processes of factories of the future. The purpose of the article is to describe the use of modelling and simulation processes in factories of the future. The first part of the article describes new manufacturing concepts that will be used in factories of the future, with a description of modelling and simulation routing in the frame of Industry 4.0. The next section describes how simulation is used for the control of manufacturing processes in factories of the future. The included subsection describes the implementation of this suggested pattern in the laboratory of ZIMS (Zilina Intelligent Manufacturing System), with an example of a metamodeling application and the results obtained.},
  doi            = {10.3390/app10134503}
}


"


"@misc{anih2020detection,
  title         = {Detection of Anomalies in a Time Series Data using InfluxDB and Python},
  author        = {Tochukwu John Anih and Chika Amadi Bede and Chima Festus Umeokpala},
  year          = {2020},
  eprint        = {2012.08439},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG}
}"



@article{taoDigitalTwinDriven2018a,
  title    = {Digital twin driven prognostics and health management for complex equipment},
  volume   = {67},
  issn     = {0007-8506},
  url      = {https://www.sciencedirect.com/science/article/pii/S0007850618300799},
  doi      = {10.1016/j.cirp.2018.04.055},
  abstract = {Prognostics and health management (PHM) is crucial in the lifecycle monitoring of a product, especially for complex equipment working in a harsh environment. In order to improve the accuracy and efficiency of PHM, digital twin (DT), an emerging technology to achieve physical–virtual convergence, is proposed for complex equipment. A general DT for complex equipment is first constructed, then a new method using DT driven PHM is proposed, making effective use of the interaction mechanism and fused data of DT. A case study of a wind turbine is used to illustrate the effectiveness of the proposed method.},
  number   = {1},
  urldate  = {2024-03-11},
  journal  = {CIRP Annals},
  author   = {Tao, Fei and Zhang, Meng and Liu, Yushan and Nee, A. Y. C.},
  month    = jan,
  year     = {2018},
  keywords = {Condition monitoring, Digital twin, Maintenance},
  pages    = {169--172},
  file     = {ScienceDirect Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\R2AMYG8R\\S0007850618300799.html:text/html;Tao et al. - 2018 - Digital twin driven prognostics and health managem.pdf:C\:\\Users\\bd65\\Zotero\\storage\\DWTG3RGB\\Tao et al. - 2018 - Digital twin driven prognostics and health managem.pdf:application/pdf}
}



@article{systems7010007,
  author         = {Madni, Azad M. and Madni, Carla C. and Lucero, Scott D.},
  title          = {Leveraging Digital Twin Technology in Model-Based Systems Engineering},
  journal        = {Systems},
  volume         = {7},
  year           = {2019},
  number         = {1},
  article-number = {7},
  url            = {https://www.mdpi.com/2079-8954/7/1/7},
  issn           = {2079-8954},
  abstract       = {Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle. This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE. The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin technology in different industries. It concludes with a recommendation to make digital twin technology an integral part of MBSE methodology and experimentation testbeds.},
  doi            = {10.3390/systems7010007}
}





@article{walmsley_adaptive_2024,
  title    = {Adaptive digital twins for energy-intensive industries and their local communities},
  volume   = {10},
  issn     = {2772-5081},
  url      = {https://www.sciencedirect.com/science/article/pii/S2772508124000012},
  doi      = {10.1016/j.dche.2024.100139},
  abstract = {Digital Twins (DTs) are high-fidelity virtual models that behave-like, look-like and connect-to a physical system. In this work, the physical systems are operations and processes from energy-intensive industrial plants and their local communities. The creation of DTs demands expertise not just in engineering, but also in computer science, data science, and artificial intelligence. Here, we introduce the Adaptive Digital Twins (ADT) concept, anchored in five attributes inspired by the self-adaptive systems field from software engineering. These attributes are self-learning, self-optimizing, self-evolving, self-monitoring, and self-protection. This new approach merges cutting-edge computing with pragmatic engineering needs. ADTs can enhance decision-making in both the design phase and real-time operation of industrial facilities and allow for versatile 'what-if' scenario simulations. Seven applications within the energy-intensive industries are described where ADTs could be transformative.},
  urldate  = {2024-03-14},
  journal  = {Digital Chemical Engineering},
  author   = {Walmsley, Timothy Gordon and Patros, Panos and Yu, Wei and Young, Brent R. and Burroughs, Stephen and Apperley, Mark and Carson, James K. and Udugama, Isuru A. and Aeowjaroenlap, Hattachai and Atkins, Martin J. and Walmsley, Michael R. W.},
  month    = mar,
  year     = {2024},
  keywords = {Digital twin, Machine learning, Process control, Process integration, Process simulation, Self-adative systems},
  pages    = {100139},
  file     = {ScienceDirect Snapshot:/home/bert/Zotero/storage/V2GCBR2Q/S2772508124000012.html:text/html}
}



@article{yu_energy_2022,
  title      = {Energy digital twin technology for industrial energy management: {Classification}, challenges and future},
  volume     = {161},
  issn       = {1364-0321},
  shorttitle = {Energy digital twin technology for industrial energy management},
  url        = {https://www.sciencedirect.com/science/article/pii/S136403212200315X},
  doi        = {10.1016/j.rser.2022.112407},
  abstract   = {Digitalisation of the process and energy industries through energy digital twin technology promises step-improvements in energy management and optimisation, better servicing and maintenance, energy-efficient design and evolution of existing sites, and integration with locally and regionally generated renewable energy. This systematic and critical review aims to accelerate the understanding, classification, and application of energy digital twin technology. It adds to the literature by developing an original multi-dimensional digital twin classification framework, summarising the applications of energy digital twins throughout a site's lifecycle, and constructing a proposal of how to apply the technology to industrial sites and local areas to enable a reduction in carbon and other environmental footprints. The review concludes by identifying key challenges that face uptake of energy digital twins and a framework to apply the energy digital twins.},
  urldate    = {2024-03-14},
  journal    = {Renewable and Sustainable Energy Reviews},
  author     = {Yu, Wei and Patros, Panos and Young, Brent and Klinac, Elsa and Walmsley, Timothy Gordon},
  month      = jun,
  year       = {2022},
  keywords   = {Digital twin, Energy engineering, Industry 4.0, Process systems engineering, Renewable energy, Sustainable energy},
  pages      = {112407},
  file       = {Full Text:/home/bert/Zotero/storage/TR9QF69N/Yu et al. - 2022 - Energy digital twin technology for industrial ener.pdf:application/pdf;ScienceDirect Snapshot:/home/bert/Zotero/storage/65HNWT99/S136403212200315X.html:text/html}
}



@article{GlobalRisksReport,
  title    = {{Global} {Risks} {Report}},
  url      = {https://www.weforum.org/publications/global-risks-report-2023/},
  abstract = {The World Economic Forum's Global Risks Report 2023 explores some of the most severe risks we may face over the next decade that include energy supply and food crisis, rising inflation, cyberattacks, failure to meet net-zero targets, weaponization of economic policy, weakening of human rights.},
  language = {en},
  author   = {World Economic Forum},
  urldate  = {2024-03-14},
  month    = jan,
  year     = 2023,
  note     = {Accessible at \url{https://www.weforum.org/publications/global-risks-report-2023}},
  file     = {Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\4CF35U5W\\global-risks-report-2023.html:text/html}
}



@article{DecarbonisingProcessHeat,
  author  = {{{Ministry} of {Business}, {Innovation} \& {Employment}}},
  title   = {Low {Emissions} {Economy}: {Decarbonising} {Process} {Heat} },
  month   = Aug,
  year    = 2022,
  note    = {Accessible at \url{https://www.mbie.govt.nz/building-and-energy/energy-and-natural-resources/low-emissions-economy/decarbonising-process-heat/}},
  url     = {https://www.mbie.govt.nz/building-and-energy/energy-and-natural-resources/low-emissions-economy/decarbonising-process-heat/},
  urldate = {2024-03-14},
  file    = {Decarbonising process heat | Ministry of Business, Innovation & Employment:C\:\\Users\\bd65\\Zotero\\storage\\UX6H25DS\\decarbonising-process-heat.html:text/html}
}



@article{alam_data_2017,
  title      = {Data {Fusion} and {IoT} for {Smart} {Ubiquitous} {Environments}: {A} {Survey}},
  volume     = {5},
  issn       = {2169-3536},
  shorttitle = {Data {Fusion} and {IoT} for {Smart} {Ubiquitous} {Environments}},
  url        = {https://ieeexplore.ieee.org/document/7911293},
  doi        = {10.1109/ACCESS.2017.2697839},
  abstract   = {The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects connected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progressively. IoT, hence, is expected to be a major producer of big data. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments, such as smart cities and societies. A timely fusion and analysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable, and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge. A number of surveys exist on data fusion. However, these are mainly focused on specific application areas or classifications. The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments). The opportunities and challenges for each of the mathematical methods and environments are given. Future developments, including emerging areas that would intrinsically benefit from data fusion and IoT, autonomous vehicles, deep learning for data fusion, and smart cities, are discussed.},
  urldate    = {2024-03-15},
  journal    = {IEEE Access},
  author     = {Alam, Furqan and Mehmood, Rashid and Katib, Iyad and Albogami, Nasser N. and Albeshri, Aiiad},
  year       = {2017},
  note       = {Conference Name: IEEE Access},
  keywords   = {big data, Big Data, computational and artificial intelligence, Computer science, data fusion, Data integration, Decision making, high performance computing, Information technology, Internet of Things, smart cities, Smart cities, smart societies, ubiquitous environments},
  pages      = {9533--9554},
  file       = {IEEE Xplore Abstract Record:/home/bert/Zotero/storage/QGZDVHF9/7911293.html:text/html;IEEE Xplore Full Text PDF:/home/bert/Zotero/storage/K8P9I89Z/Alam et al. - 2017 - Data Fusion and IoT for Smart Ubiquitous Environme.pdf:application/pdf}
}



@article{pang_spatio-temporal_2020,
  title    = {Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on {SCADA} data},
  volume   = {161},
  issn     = {0960-1481},
  url      = {https://www.sciencedirect.com/science/article/pii/S0960148120310764},
  doi      = {10.1016/j.renene.2020.06.154},
  abstract = {Numerous sensors have been deployed in different locations in components of wind turbines to continuously monitor the health status of the turbine system and accordingly, generate a large volume of operation data by the supervisory control and data acquisition (SCADA) system. Naturally, these sensory data are multivariate time series with high spatio-temporal correlations. It is still challenging to effectively model such correlations and then enable an accurate fault diagnosis. To this end, we proposed a new spatio-temporal fusion neural network (STFNN) for wind turbine fault diagnosis. Specifically, a multi-kernel fusion convolution neural network (MKFCNN) with multiple convolution kernels of different sizes is first designed to extract multi-scale spatial correlations among different variables. Then, we adopt the long short-term memory (LSTM) to further learn the temporal dependence of the learned spatial features. The proposed STFNN model provides an end-to-end fault diagnosis way, which can directly learn spatio-temporal dependency from the raw SCADA data and give the fault diagnosis result. The effectiveness and superiority of the proposed method are evaluated on a generic wind turbine benchmark simulation dataset and a SCADA dataset from a real wind farm. Both experimental results have indicated that the proposed method outperformed several compared methods.},
  urldate  = {2024-03-18},
  journal  = {Renewable Energy},
  author   = {Pang, Yanhua and He, Qun and Jiang, Guoqian and Xie, Ping},
  month    = dec,
  year     = {2020},
  keywords = {Convolutional neural networks (CNN), Fault diagnosis, Long and short-term memory (LSTM), Spatio-temporal fusion, Wind turbine (WT)},
  pages    = {510--524},
  file     = {Pang et al. - 2020 - Spatio-temporal fusion neural network for multi-cl.pdf:/home/bd65/Zotero/storage/59LZ683S/Pang et al. - 2020 - Spatio-temporal fusion neural network for multi-cl.pdf:application/pdf;ScienceDirect Snapshot:/home/bd65/Zotero/storage/LNVCBGFY/S0960148120310764.html:text/html}
}



@article{deDevelopmentArtificialNeural2007,
  title    = {Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power ({CHP}) plant in {Sweden}},
  volume   = {32},
  issn     = {0360-5442},
  url      = {https://www.sciencedirect.com/science/article/pii/S0360544207000722},
  doi      = {10.1016/j.energy.2007.04.008},
  abstract = {The development of a model for any energy system is required for proper design, operation or its monitoring. Models based on accurate mathematical expressions for physical processes are mostly useful to understand the actual operation of the plant. However, for large systems like combined heat and power (CHP) plants, such models are usually complex in nature. The estimation of output parameters using these physical models is generally time consuming, as these involve many iterative solutions. Moreover, the complete physical model for new equipment may not be available. However, artificial neural network (ANN) models, developed by training the network with data from an existing plant, may be very useful especially for systems for which the full physical model is yet to be developed. Also, such trained ANN models have a fast response with respect to corresponding physical models and are useful for real-time monitoring of the plant. In this paper, the development of an ANN model for the biomass and coal cofired CHP plant of Västhamnsverket at Helsingborg, Sweden has been reported. The feed forward with back propagation ANN model was trained with data from this plant. The developed model is found to quickly predict the performance of the plant with good accuracy.},
  number   = {11},
  urldate  = {2024-03-17},
  journal  = {Energy},
  author   = {De, S. and Kaiadi, M. and Fast, M. and Assadi, M.},
  month    = nov,
  year     = {2007},
  keywords = {ANN modeling, Coal biomass cofired CHP plant, Steam processes},
  pages    = {2099--2109},
  file     = {ScienceDirect Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\ETR7ZEQE\\S0360544207000722.html:text/html}
}



@article{cecconOMLTOptimizationMachine2022,
  title      = {{OMLT}: {Optimization} \& {Machine} {Learning} {Toolkit}},
  volume     = {23},
  issn       = {1533-7928},
  shorttitle = {{OMLT}},
  url        = {http://jmlr.org/papers/v23/22-0277.html},
  abstract   = {The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger optimization problems. We discuss the advances in optimization technology that made OMLT possible and show how OMLT seamlessly integrates with the algebraic modeling language Pyomo. We demonstrate how to use OMLT for solving decision-making problems in both computer science and engineering.},
  number     = {349},
  urldate    = {2024-03-19},
  journal    = {Journal of Machine Learning Research},
  author     = {Ceccon, Francesco and Jalving, Jordan and Haddad, Joshua and Thebelt, Alexander and Tsay, Calvin and Laird, Carl D. and Misener, Ruth},
  year       = {2022},
  pages      = {1--8},
  file       = {Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\4FHY8U5R\\Ceccon et al. - 2022 - OMLT Optimization & Machine Learning Toolkit.pdf:application/pdf;Source Code:C\:\\Users\\bd65\\Zotero\\storage\\B5PAVY3J\\OMLT.html:text/html}
}


@phdthesis{alkatheriDatadrivenOptimizationApplications2021,
  type       = {Doctoral {Thesis}},
  title      = {Data-driven {Optimization}: {Applications} to {Energy} {Infrastructure} and {Process} {Industry}},
  shorttitle = {Data-driven {Optimization}},
  url        = {https://uwspace.uwaterloo.ca/handle/10012/17782},
  abstract   = {Nowadays, the existence and ease of access to massive amounts of data encourage proposing data-driven solutions. As optimization has always been based on the interchange between models and data, high-level optimization tasks such as planning and scheduling will extremely benefit from information mined from massive data sets. The development of big data tools (i.e., machine learning) has proven superiority over traditional data tools in dealing with vast amounts of data, data with undefined structure and capturing important information from data in a very efficient and computationally tractable manner. Therefore, in this work, big data tools are implemented to address the challenges associated with planning models of energy infrastructure that incorporate renewable resources and chemical engineering processes, namely, uncertainty handling, multiscale modelling, and unit process equation complexity. 
                A Data-driven stochastic optimization framework that leverages big data in design and operation of power generation planning is proposed. A k-means clustering algorithm is adopted to generate uncertainty scenarios for the stochastic optimization framework. These scenarios are used as inputs to the stochastic model where the proposed model is formulated as a mixed integer linear program (MILP) and solved using GAMS. The proposed approach is applied to different power planning models that include unit commitment (UC) characteristics where the size of uncertainty scenarios is reduced. Results show that the proposed approach is an effective tool to generate reduced size stochastic scenarios. 
                The design and operation of energy hub problem involves the integration of decision levels with different time scales that usually lead to multiscale models which are computationally expensive. The multiscale (i.e., planning and scheduling) energy hub systems that incorporate renewable energy resources become more challenging to model due to a high level of intermittency associated with renewable energy. A mathematical programming-based general clustering approach is applied to reduce the size of multiple attributes demand data and tackle the computational complexity of multiscale energy hub problems. Multiscale with multiple attributes energy hub incorporating hydrogen storage is modelled as a MILP stochastic optimization problem under wind uncertainty. Different case studies are generated under different environmental consideration to assess the efficiency of the clustering approach and stochastic formulation. Assessments conclude that the clustering approach is an effective tool to reduce the size of the original model while maintaining good results. 
                Recent advancements in supervised machine learning tools have demonstrated their ability to achieve accurate and efficient prediction results. Therefore, in this study, these tools are employed as alternative approaches to model a specific application in the gas industry. The chosen application is a natural gas condensate stabilization process based on operating data. Natural gas condensate treatment involves condensate stabilization process in which light end components are removed and thus condensate vapour pressure is reduced to meet storage and transportation specification. Different supervised machine learning models are developed to predict the performance of two industrial condensate stabilizer units. Large datasets of the two different industrial condensate stabilizers, including operating data of input-output variables, are utilized to develop and evaluate these models. The main purpose of developing these machine learning models is to predict the important parameters of the final stabilized liquid. Results attained from this study showcase the capability of the developed models to offer reliable and accurate predictions. A data-driven surrogate-based optimization framework is developed, where the generated machine learning models can serve as a convenient replacement for detailed first principle models, to find the optimal values of the variables corresponding to the minimal operational energy consumption. The proposed framework can benefit the gas industry to simultaneously achieve process efficiency, profitability, and safety.},
  language   = {en},
  urldate    = {2024-03-28},
  school     = {University of Waterloo},
  author     = {Alkatheri, Mohammed},
  month      = dec,
  year       = {2021},
  note       = {Accepted: 2021-12-20T15:59:47Z},
  file       = {Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\QN44Z758\\Alkatheri - 2021 - Data-driven Optimization Applications to Energy I.pdf:application/pdf}
}



@article{zhangDatadrivenStrategyIndustrial2023,
  title    = {A data-driven strategy for industrial cracking furnace system scheduling under uncertainty},
  volume   = {277},
  issn     = {0009-2509},
  url      = {https://www.sciencedirect.com/science/article/pii/S0009250923004219},
  doi      = {10.1016/j.ces.2023.118865},
  abstract = {Cyclic scheduling of ethylene cracking furnace system (CSECFS) has a significant impact on raising economic performance of ethylene plants. However, in actual plants, optimal results of deterministic models may be suboptimal or ineffective because of various uncertainties. This paper proposes a novel data-driven adaptive robust optimization (DDARO) strategy that effectively bridges robust optimization and machine learning methods. A mixed-integer nonlinear programming (MINLP) model for CSECFS is developed initially. Second, data-driven uncertainty sets are generated using the historical data of processing flow rates: maximally correlated principal component analysis (MCPCA) is employed to partition the data into two subspaces, which are then delineated by support vector clustering (SVC) and generalized norm approaches, respectively. Third, a multi-stage DDARO model is established using the derived uncertainty sets and afterward transformed as a tractable single-stage model. Finally, a real-world case is performed to exemplify the effectiveness of the proposed framework.},
  urldate  = {2024-03-28},
  journal  = {Chemical Engineering Science},
  author   = {Zhang, Chenhan and Wang, Zhenlei},
  month    = aug,
  year     = {2023},
  keywords = {Adaptive robust optimization, Cracking furnace system, Cycle scheduling, Machine learning, Uncertainty},
  pages    = {118865},
  file     = {ScienceDirect Snapshot:C\:\\Users\\bd65\\Zotero\\storage\\CPCXQR2R\\S0009250923004219.html:text/html;Zhang and Wang - 2023 - A data-driven strategy for industrial cracking fur.pdf:C\:\\Users\\bd65\\Zotero\\storage\\Y43DG7F2\\Zhang and Wang - 2023 - A data-driven strategy for industrial cracking fur.pdf:application/pdf}
}



@article{zhangDataDrivenDesignOptimization2014,
  title    = {Data-{Driven} {Design} and {Optimization} of {Feedback} {Control} {Systems} for {Industrial} {Applications}},
  volume   = {61},
  issn     = {1557-9948},
  url      = {https://ieeexplore.ieee.org/abstract/document/6718131?casa_token=4a5WbYqqyhkAAAAA:Z0Xnk2GUo55aPCW64p1TMkiAV9b82okWi7-Z0zfRLxDRfuGTfBpUTOL-gVQHuLkOtk6mFThtx3FS},
  doi      = {10.1109/TIE.2014.2301757},
  abstract = {In this paper, regarding the observer form of the well-known Youla parameterization, the controller design and optimization are exhibited with an integrated residual access. To better reveal this philosophy, the feedback control loop is interpreted on the basis of the observer-based residual generator. The next main attention is drawn to the generation of residuals, the design of a deadbeat controller for system stabilization both in the data-driven environment, and later the optimal adaptive realization of a dynamic system that translates residuals into compensatory control inputs to meet certain performance specifications. Towards these goals, numerical algorithms are summarized, and for the issues of controller optimization, the reinforcement learning algorithm is introduced using only measured input-output and residual signals. In addition, the effectiveness of developed schemes for industrial applications is also illustrated by experimental studies on a laboratory continuous stirred tank heater (CSTH) process.},
  number   = {11},
  urldate  = {2024-03-28},
  journal  = {IEEE Transactions on Industrial Electronics},
  author   = {Zhang, Yong and Yang, Ying and Ding, Steven X. and Li, Linlin},
  month    = nov,
  year     = {2014},
  note     = {Conference Name: IEEE Transactions on Industrial Electronics},
  keywords = {Algorithm design and analysis, Control system analysis, data-driven, data-driven, feedback systems, Feedback control, feedback systems, Generators, Observers, optimization, Optimization, Process control, residual generation, Vectors},
  pages    = {6409--6417},
  file     = {IEEE Xplore Abstract Record:C\:\\Users\\bd65\\Zotero\\storage\\DMSLKYW8\\6718131.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\GDSUP82C\\Zhang et al. - 2014 - Data-Driven Design and Optimization of Feedback Co.pdf:application/pdf}
}



@article{pragDataDrivenOptimalControl2022,
  title      = {Toward {Data}-{Driven} {Optimal} {Control}: {A} {Systematic} {Review} of the {Landscape}},
  volume     = {10},
  issn       = {2169-3536},
  shorttitle = {Toward {Data}-{Driven} {Optimal} {Control}},
  url        = {https://ieeexplore.ieee.org/abstract/document/9738649},
  doi        = {10.1109/ACCESS.2022.3160709},
  abstract   = {This literature review extends and contributes to research on the development of data-driven optimal control. Previous reviews have documented the development of model-based and data-driven control in isolation and have not critically reviewed reinforcement learning approaches for adaptive data-driven optimal control frameworks. The presented review discusses the development of model-based to model-free adaptive controllers, highlighting the use of data in control frameworks. In data-driven control frameworks, reinforcement learning methods may be used to derive the optimal policy for dynamical systems. Attractive characteristics of these methods include not requiring a mathematical model of complex systems, their inherent adaptive control capabilities, being an unsupervised learning technique and their decision-making abilities, which are both an advantage and motivation behind this approach. This review considers previous reviews on these topics, including recent work on data-driven control methods. In addition, this review shows the use of data to derive system dynamics, determine the control policy using feedback information, and tune fixed controllers. Furthermore, the review summarises various data-driven methods and their corresponding characteristics. Finally, the review provides a taxonomy, a timeline and a concise narrative of the development of model-based to model-free data-driven adaptive control and underlines the limitations of these techniques due to the lack of theoretical analysis. Areas of further work include theoretical analysis on stability and robustness for data-driven control systems, explainability of black-box policy learning techniques and an evaluation of the impact of the extension of system simulators to include digital twins.},
  urldate    = {2024-03-28},
  journal    = {IEEE Access},
  author     = {Prag, Krupa and Woolway, Matthew and Celik, Turgay},
  year       = {2022},
  note       = {Conference Name: IEEE Access},
  keywords   = {Adaptation models, adaptive control, Adaptive control, Control systems, Data models, Data-driven control, learning-based control, Mathematical models, model predictive control, model-based, model-free, optimal control, Optimal control, systematic review, Tuning},
  pages      = {32190--32212}
}



@article{gonzalezReshapingSmartCities2024,
  title     = {Reshaping {Smart} {Cities} through {NGSI}-{LD} {Enrichment}},
  volume    = {24},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  issn      = {1424-8220},
  url       = {https://www.mdpi.com/1424-8220/24/6/1858},
  doi       = {10.3390/s24061858},
  abstract  = {The vast amount of information stemming from the deployment of the Internet of Things and open data portals is poised to provide significant benefits for both the private and public sectors, such as the development of value-added services or an increase in the efficiency of public services. This is further enhanced due to the potential of semantic information models such as NGSI-LD, which enable the enrichment and linkage of semantic data, strengthened by the contextual information present by definition. In this scenario, advanced data processing techniques need to be defined and developed for the processing of harmonised datasets and data streams. Our work is based on a structured approach that leverages the principles of linked-data modelling and semantics, as well as a data enrichment toolchain framework developed around NGSI-LD. Within this framework, we reveal the potential for enrichment and linkage techniques to reshape how data are exploited in smart cities, with a particular focus on citizen-centred initiatives. Moreover, we showcase the effectiveness of these data processing techniques through specific examples of entity transformations. The findings, which focus on improving data comprehension and bolstering smart city advancements, set the stage for the future exploration and refinement of the symbiosis between semantic data and smart city ecosystems.},
  language  = {en},
  number    = {6},
  urldate   = {2024-04-08},
  journal   = {Sensors},
  author    = {González, Víctor and Martín, Laura and Santana, Juan Ramón and Sotres, Pablo and Lanza, Jorge and Sánchez, Luis},
  month     = jan,
  year      = {2024},
  note      = {Number: 6
               Publisher: Multidisciplinary Digital Publishing Institute},
  keywords  = {data enrichment, data processing, data understandability, linked data, semantic annotation, smart cities},
  pages     = {1858},
  file      = {Full Text PDF:C\:\\Users\\bd65\\Zotero\\storage\\6D3NP7V6\\González et al. - 2024 - Reshaping Smart Cities through NGSI-LD Enrichment.pdf:application/pdf}
}


@article{privat2021guidelines,
  title   = {Guidelines for Modelling with NGSI-LD},
  author  = {Privat, Gilles and Medvedev, Alexey},
  journal = {ETSI White Paper},
  volume  = {42},
  year    = {2021}
}




@article{chamorro-atalaya_scada_2021,
  title    = {{SCADA} and {Distributed} {Control} {System} of a {Chemical} {Products} {Dispatch} {Process}},
  volume   = {12},
  issn     = {21565570, 2158107X},
  url      = {http://thesai.org/Publications/ViewPaper?Volume=12&Issue=12&Code=IJACSA&SerialNo=88},
  doi      = {10.14569/IJACSA.2021.0121288},
  abstract = {The objective of this article is to show the application of a supervision, control and acquisition system of an industrial network of chemical products, for which the design of the control logic and the architecture of the industrial network on Profibus-DP protocols is described and Ethernet, with a peripheral terminal station ET-200, through the Siemens CP433 programmable logic controller and level sensor sensors, coupled to radar-type transmitters with an accuracy of ± 0.5 mm. As findings of the implementation of the control system, it was possible to demonstrate the optimal regulation of the filling system of the 3-compartment trucks with a capacity of 300 Kilos each, generating the elimination of spills of the chemical product, as well as the reduction of polluting particles in the work environment. Finally, as a direct consequence, the productivity of the company was improved, which is a relevant aspect at the level of planning, management and direction.},
  language = {en},
  number   = {12},
  urldate  = {2024-04-09},
  journal  = {International Journal of Advanced Computer Science and Applications},
  author   = {Chamorro-Atalaya, Omar and Arce-Santillan, Dora and Morales-Romero, Guillermo and Trinidad-Loli, Nicéforo and Quispe-Andía, Adrián and León-Velarde, César},
  year     = {2021},
  file     = {Chamorro-Atalaya et al. - 2021 - SCADA and Distributed Control System of a Chemical.pdf:/home/bd65/Zotero/storage/8U8J6HY7/Chamorro-Atalaya et al. - 2021 - SCADA and Distributed Control System of a Chemical.pdf:application/pdf}
}


@article{yu2022energy,
  title={Energy digital twin technology for industrial energy management: Classification, challenges and future},
  author={Yu, Wei and Patros, Panos and Young, Brent and Klinac, Elsa and Walmsley, Timothy Gordon},
  journal={Renewable and Sustainable Energy Reviews},
  volume={161},
  pages={112407},
  year={2022},
  publisher={Elsevier}
}

@article{connolly2023software,
  title={Software Engineering Practices in Academia: Promoting the 3Rs Readability, Resilience, and Reuse},
  author={Connolly, Andrew and Hellerstein, Joseph and Alterman, Naomi and Beck, David and Fatland, Rob and Lazowska, Ed and Mandava, Vani and Stone, Sarah},
  journal={Harvard data science review},
  volume={5},
  number={2},
  year={2023},
  publisher={Harvard Data Science Review}
}


@article{wohlin2021guiding,
  title={Guiding the selection of research methodology in industry--academia collaboration in software engineering},
  author={Wohlin, Claes and Runeson, Per},
  journal={Information and software technology},
  volume={140},
  pages={106678},
  year={2021},
  publisher={Elsevier}
}