1
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Gangwar S, Fernández D, Pozo C, Folgado R, Jiménez L, Boer D. Scheduling optimization and risk analysis for energy-intensive industries under uncertain electricity market to facilitate financial planning. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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2
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Elmisaoui S, Benjelloun S, Chkifa A, Latifi AM. Surrogate model based on hierarchical sparse polynomial interpolation for the phosphate ore dissolution. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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3
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Tang X, Baldea M. A grid view on the dynamics of processes participating in demand response programs. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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4
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Energy-aware enterprise-wide optimization and clean energy in the industrial gas industry. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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5
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On the acceleration of global optimization algorithms by coupling cutting plane decomposition algorithms with machine learning and advanced data analytics. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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6
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Distributed fairness-guided optimization for coordinated demand response in multi-stakeholder process networks. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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7
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A Review of EV Battery Utilization in Demand Response Considering Battery Degradation in Non-Residential Vehicle-to-Grid Scenarios. ENERGIES 2022. [DOI: 10.3390/en15093227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Integrating fleets of electric vehicles (EVs) into industrial applications with smart grids is an emerging field of important research. It is necessary to get a comprehensive overview of current approaches and proposed solutions regarding EVs with vehicle-to-grid and smart charging. In this paper, various approaches to battery modeling and demand response (DR) of EV charging in different decentralized optimization scenarios are reviewed. Modeling parameters of EVs and battery degradation models are summarized and discussed. Finally, optimization approaches to simulate and optimize demand response, taking into account battery degradation, are investigated to examine the feasibility of adapting the charging process, which may bring economic and environmental benefits and help to alleviate the increasing demand for flexibility. There is a lack of studies that comprehensively consider battery degradation for EV fleets in DR charging scenarios where corresponding financial compensation for the EV owners is considered. Therefore, models are required for estimating the level of battery degradation endured when EVs are utilized for DR. The level of degradation should be offset by providing the EV owner with subsidized or free electricity provided by the company which is partaking in the DR. This trade-off should be optimized in such a manner that the company makes cost savings while the EV owners are compensated to a level that is at least commensurate with the level of battery degradation. Additionally, there is a lack of studies that have examined DR in smart grids considering larger EV fleets and battery degradation in multi-criteria approaches to provide economic and environmental benefits.
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8
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Kelley MT, Tsay C, Cao Y, Wang Y, Flores-Cerrillo J, Baldea M. A data-driven linear formulation of the optimal demand response scheduling problem for an industrial air separation unit. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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9
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Kender R, Rößler F, Wunderlich B, Pottmann M, Thomas I, Ecker A, Rehfeldt S, Klein H. Improving the Load Flexibility of Industrial Air Separation Units Using a
Pressure‐Driven
Digital Twin. AIChE J 2022. [DOI: 10.1002/aic.17692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Robert Kender
- Department of Energy and Process Engineering, TUM School of Engineering and Design Institute of Plant and Process Technology, Technical University of Munich Garching Germany
| | - Felix Rößler
- Department of Energy and Process Engineering, TUM School of Engineering and Design Institute of Plant and Process Technology, Technical University of Munich Garching Germany
- Linde GmbH, Linde Engineering Pullach Germany
| | | | | | - Ingo Thomas
- Linde GmbH, Linde Engineering Pullach Germany
| | | | - Sebastian Rehfeldt
- Department of Energy and Process Engineering, TUM School of Engineering and Design Institute of Plant and Process Technology, Technical University of Munich Garching Germany
| | - Harald Klein
- Department of Energy and Process Engineering, TUM School of Engineering and Design Institute of Plant and Process Technology, Technical University of Munich Garching Germany
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10
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Thebelt A, Wiebe J, Kronqvist J, Tsay C, Misener R. Maximizing information from chemical engineering data sets: Applications to machine learning. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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11
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Di Pretoro A, Bruns B, Negny S, Grünewald M, Riese J. Demand Response Scheduling Using Derivative-Based Dynamic Surrogate Models. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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12
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13
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Advanced feasibility cuts in decoupled cooperative optimization of power flow. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Sadat Lavasani M, Raeisi Ardali N, Sotudeh-Gharebagh R, Zarghami R, Abonyi J, Mostoufi N. Big data analytics opportunities for applications in process engineering. REV CHEM ENG 2021. [DOI: 10.1515/revce-2020-0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Big data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. Initially, an overview of big data content, key characteristics, and related topics are presented. The paper also highlights a systematic review of available big data techniques and analytics. The available big data analytics tools and platforms are categorized. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data.
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Affiliation(s)
- Mitra Sadat Lavasani
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - Nahid Raeisi Ardali
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - Rahmat Sotudeh-Gharebagh
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - Reza Zarghami
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
| | - János Abonyi
- Department of Process Engineering , MTA – PE “Lendület” Complex Systems Monitoring Research Group, University of Pannonia , P.O. Box 158 , Veszprém , Hungary
| | - Navid Mostoufi
- Process Design and Simulation Research Center , School of Chemical Engineering, College of Engineering, University of Tehran , P.O. Box 11155-4563, Tehran , Iran
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15
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Bruns B, Di Pretoro A, Grünewald M, Riese J. Indirect Demand Response Potential of Large-Scale Chemical Processes. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c03925] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Bastian Bruns
- Ruhr University Bochum, Faculty of Mechanical Engineering, Laboratory of Fluid Separations, Universitätsstraße 150, 44801 Bochum, Germany
| | - Alessandro Di Pretoro
- Laboratoire de Génie Chimique, Université de Toulouse, CNRS/INP/UPS, Toulouse 31400, France
| | - Marcus Grünewald
- Ruhr University Bochum, Faculty of Mechanical Engineering, Laboratory of Fluid Separations, Universitätsstraße 150, 44801 Bochum, Germany
| | - Julia Riese
- Ruhr University Bochum, Faculty of Mechanical Engineering, Laboratory of Fluid Separations, Universitätsstraße 150, 44801 Bochum, Germany
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16
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Zhou Y, Gao K, Li D, Xu Z, Gao F. Data-Efficient Constrained Learning for Optimal Tracking of Batch Processes. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c02706] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Yuanqiang Zhou
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong999077, China
| | - Kaihua Gao
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong999077, China
| | - Dewei Li
- Department of Automation, Shanghai Jiao Tong University, Shanghai200240, China
| | - Zuhua Xu
- National Center for International Research on Quality-Targeted Process Optimization and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou310027, China
| | - Furong Gao
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong999077, China
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou511458, China
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17
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Kender R, Kaufmann F, Rößler F, Wunderlich B, Golubev D, Thomas I, Ecker AM, Rehfeldt S, Klein H. Development of a digital twin for a flexible air separation unit using a pressure-driven simulation approach. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107349] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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18
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Sansana J, Joswiak MN, Castillo I, Wang Z, Rendall R, Chiang LH, Reis MS. Recent trends on hybrid modeling for Industry 4.0. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107365] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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19
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Yan L, Deneke TL, Heljanko K, Harjunkoski I, Edgar TF, Baldea M. Dynamic Process Intensification via Data-Driven Dynamic Optimization: Concept and Application to Ternary Distillation. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c01415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Lingqing Yan
- McKetta Dept. of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Tewodros L. Deneke
- Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland
| | - Keijo Heljanko
- Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland
| | - Iiro Harjunkoski
- Department of Chemical and Metallurgical Engineering, Aalto University, 00076 Aalto, Finland
| | - Thomas F. Edgar
- McKetta Dept. of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Michael Baldea
- McKetta Dept. of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
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20
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Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control. ENERGIES 2021. [DOI: 10.3390/en14133998] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results.
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21
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Towards demand-side management of the chlor-alkali electrolysis: Dynamic modeling and model validation. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107287] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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22
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Pablos C, Merino A, Acebes LF, Pitarch JL, Biegler LT. Dynamic optimization approach to coordinate industrial production and cogeneration operation under electricity price fluctuations. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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23
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Otashu JI, Seo K, Baldea M. Cooperative optimal power flow with flexible chemical process loads. AIChE J 2021. [DOI: 10.1002/aic.17159] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Joannah I. Otashu
- McKetta Department of Chemical Engineering The University of Texas at Austin Austin Texas USA
| | - Kyeongjun Seo
- McKetta Department of Chemical Engineering The University of Texas at Austin Austin Texas USA
| | - Michael Baldea
- McKetta Department of Chemical Engineering The University of Texas at Austin Austin Texas USA
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24
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A Supervisory Control Strategy for Improving Energy Efficiency of Artificial Lighting Systems in Greenhouses. ENERGIES 2021. [DOI: 10.3390/en14010202] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Artificial lighting systems are used in commercial greenhouses to ensure year-round yields. Current Light Emitting Diode (LED) technologies improved the system efficiency. Nevertheless, having artificial lighting systems extended for hectares with power densities over 50W/m2 causes energy and power demand of greenhouses to be really significant. The present paper introduces an innovative supervisory and predictive control strategy to optimize the energy performance of the artificial lights of greenhouses. The controller has been implemented in a multi-span plastic greenhouse located in North Italy. The proposed control strategy has been tested on a greenhouse of 1 hectare with a lighting system with a nominal power density of 50 Wm−2 requiring an overall power supply of 1 MW for a period of 80 days. The results have been compared with the data coming from another greenhouse of 1 hectare in the same conditions implementing a state-of-the-art strategy for artificial lighting control. Results outlines that potential 19.4% cost savings are achievable. Moreover, the algorithm can be used to transform the greenhouse in a viable source of energy flexibility for grid reliability.
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25
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Riese J, Grünewald M. Challenges and Opportunities to Enhance Flexibility in Design and Operation of Chemical Processes. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Julia Riese
- Ruhr-University Bochum Faculty for Mechanical Engineering Laboratory of Fluid Separations Universitätsstraße 150 44801 Bochum Germany
| | - Marcus Grünewald
- Ruhr-University Bochum Faculty for Mechanical Engineering Laboratory of Fluid Separations Universitätsstraße 150 44801 Bochum Germany
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26
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Basán NP, Cóccola ME, Dondo RG, Guarnaschelli A, Schweickardt GA, Méndez CA. A reactive-iterative optimization algorithm for scheduling of air separation units under uncertainty in electricity prices. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107050] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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27
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Schäfer P, Caspari A, Schweidtmann AM, Vaupel Y, Mhamdi A, Mitsos A. The Potential of Hybrid Mechanistic/Data‐Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000048] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Pascal Schäfer
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Adrian Caspari
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Artur M. Schweidtmann
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Yannic Vaupel
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Adel Mhamdi
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Alexander Mitsos
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
- JARA-Energy Templergraben 55 52056 Aachen Germany
- Forschungszentrum Jülich GmbH Institute of Energy and Climate Research: Energy Systems Engineering (IEK-10) Wilhelm-Johnen-Straße 52425 Jülich Germany
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28
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Schäfer P, Daun TM, Mitsos A. Do investments in flexibility enhance sustainability? A simulative study considering the German electricity sector. AIChE J 2020. [DOI: 10.1002/aic.17010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Pascal Schäfer
- RWTH Aachen University, AVT ‐ Aachener Verfahrenstechnik, Process Systems Engineering Aachen Germany
| | - Torben M. Daun
- RWTH Aachen University, AVT ‐ Aachener Verfahrenstechnik, Process Systems Engineering Aachen Germany
| | - Alexander Mitsos
- RWTH Aachen University, AVT ‐ Aachener Verfahrenstechnik, Process Systems Engineering Aachen Germany
- JARA‐ENERGY Aachen Germany
- Forschungszentrum Jülich, Energy Systems Engineering (IEK‐10) Jülich Germany
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29
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Khan TA, Ullah K, Hafeez G, Khan I, Khalid A, Shafiq Z, Usman M, Qazi AB. Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller. SENSORS 2020; 20:s20164376. [PMID: 32764405 PMCID: PMC7472082 DOI: 10.3390/s20164376] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/30/2020] [Accepted: 07/31/2020] [Indexed: 11/16/2022]
Abstract
Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.
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Affiliation(s)
- Taimoor Ahmad Khan
- Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan; (T.A.K.); (K.U.); (G.H.); (I.K.); (Z.S.)
| | - Kalim Ullah
- Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan; (T.A.K.); (K.U.); (G.H.); (I.K.); (Z.S.)
| | - Ghulam Hafeez
- Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan; (T.A.K.); (K.U.); (G.H.); (I.K.); (Z.S.)
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 44000, Pakistan
| | - Imran Khan
- Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan; (T.A.K.); (K.U.); (G.H.); (I.K.); (Z.S.)
| | - Azfar Khalid
- Department of Engineering, School of Science & Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
- Correspondence:
| | - Zeeshan Shafiq
- Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan; (T.A.K.); (K.U.); (G.H.); (I.K.); (Z.S.)
| | - Muhammad Usman
- Department of Computer Software Engineering, University of Engineering and Technology, Mardan 23200, Pakistan;
| | - Abdul Baseer Qazi
- Department of Software Engineering, Bahria University, Islamabad 44000, Pakistan;
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30
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Schäfer P, Schweidtmann AM, Mitsos A. Nonlinear scheduling with time‐variable electricity prices using sensitivity‐based truncations of wavelet transforms. AIChE J 2020. [DOI: 10.1002/aic.16986] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Pascal Schäfer
- RWTH Aachen University AVT—Aachener Verfahrenstechnik, Process Systems Engineering Aachen Germany
| | - Artur M. Schweidtmann
- RWTH Aachen University AVT—Aachener Verfahrenstechnik, Process Systems Engineering Aachen Germany
| | - Alexander Mitsos
- RWTH Aachen University AVT—Aachener Verfahrenstechnik, Process Systems Engineering Aachen Germany
- JARA‐ENERGY Aachen Germany
- Forschungszentrum Jülich, Energy Systems Engineering (IEK‐10) Jülich Germany
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31
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Abstract
We review the impact of control systems and strategies on the energy efficiency of chemical processes. We show that, in many ways, good control performance is a necessary but not sufficient condition for energy efficiency. The direct effect of process control on energy efficiency is manyfold: Reducing output variability allows for operating chemical plants closer to their limits, where the energy/economic optima typically lie. Further, good control enables novel, transient operating strategies, such as conversion smoothing and demand response. Indirectly, control systems are key to the implementation and operation of more energy-efficient plant designs, as dictated by the process integration and intensification paradigms. These conclusions are supported with references to numerous examples from the literature.
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Affiliation(s)
- Jodie M Simkoff
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
| | - Fernando Lejarza
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
| | - Morgan T Kelley
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
| | - Calvin Tsay
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
| | - Michael Baldea
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
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32
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Li H, Bai J, Cui X, Li Y, Sun S. A new secondary decomposition-ensemble approach with cuckoo search optimization for air cargo forecasting. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106161] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Tsimopoulos EG, Georgiadis MC. Withholding strategies for a conventional and wind generation portfolio in a joint energy and reserve pool market: A gaming-based approach. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106692] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106598] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Letsios D, Baltean-Lugojan R, Ceccon F, Mistry M, Wiebe J, Misener R. Approximation algorithms for process systems engineering. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106599] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Tsay C, Baldea M. 110th Anniversary: Using Data to Bridge the Time and Length Scales of Process Systems. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b02282] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Calvin Tsay
- McKetta Department of Chemical Engineering The University of Texas at Austin, Austin, Texas 78712, United States
| | - Michael Baldea
- McKetta Department of Chemical Engineering The University of Texas at Austin, Austin, Texas 78712, United States
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