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Rodríguez Vera HU, Ricardez-Sandoval LA. Integration of Scheduling and Control for Chemical Batch Plants under Stochastic Uncertainty: A Back-Off Approach. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Mowbray M, Petsagkourakis P, del Rio-Chanona E, Zhang D. Safe chance constrained reinforcement learning for batch process control. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107630] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Advancements in Optimization and Control Techniques for Intensifying Processes. Processes (Basel) 2021. [DOI: 10.3390/pr9122150] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Process Intensification (PI) is a vast and growing area in Chemical Engineering, which deals with the enhancement of current technology to enable improved efficiency; energy, cost, and environmental impact reduction; small size; and better integration with the other equipment. Since process intensification results in novel, but complex, systems, it is necessary to rely on optimization and control techniques that can cope with such new processes. Therefore, this review presents some advancements in the field of process intensification that are worthy of exploring in detail in the coming years. At the end, several important open questions that can be taken into consideration in the coming years are listed.
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Sachio S, Mowbray M, Papathanasiou M, del Rio-Chanona EA, Petsagkourakis P. Integrating process design and control using reinforcement learning. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.10.032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Tiscar J, Llorens D, Mallol G, Boix J, Pérez J, Gilabert F. DEM-based modelling framework for spray-dried powders in ceramic tiles industry. Part III: Validation procedure. POWDER TECHNOL 2021. [DOI: 10.1016/j.powtec.2021.05.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Miao A, Cheng Z, Li P, Cui H, Liu S, Wu H. Locality‐preserving data modelling and its application in fault classification. CAN J CHEM ENG 2021. [DOI: 10.1002/cjce.24149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Aimin Miao
- College of Automation Zhongkai University of Agriculture and Engineering Guangzhou China
| | - Zhishang Cheng
- College of Automation Zhongkai University of Agriculture and Engineering Guangzhou China
| | - Peng Li
- Department of Electronic Engineering, School of Information Yunnan University Kunming China
| | - Huawei Cui
- College of Agriculture and Biology Zhongkai University of Agriculture and Engineering Guangzhou China
| | - Sitong Liu
- Department of Mathematical and Computational Sciences University of Toronto Toronto Ontario Canada
| | - Hong Wu
- School of Information Engineering Qujing Normal University Qujing China
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Abstract
The application of white box models in digital twins is often hindered by missing knowledge, uncertain information and computational difficulties. Our aim was to overview the difficulties and challenges regarding the modelling aspects of digital twin applications and to explore the fields where surrogate models can be utilised advantageously. In this sense, the paper discusses what types of surrogate models are suitable for different practical problems as well as introduces the appropriate techniques for building and using these models. A number of examples of digital twin applications from both continuous processes and discrete manufacturing are presented to underline the potentials of utilising surrogate models. The surrogate models and model-building methods are categorised according to the area of applications. The importance of keeping these models up to date through their whole model life cycle is also highlighted. An industrial case study is also presented to demonstrate the applicability of the concept.
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Cao J, He Y, Zhu Q. Solutions selection based on the
P
‐graph integrated data envelopment analysis for material scheduling in the ethylene production. CAN J CHEM ENG 2021. [DOI: 10.1002/cjce.23955] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Jian Cao
- College of Information Science & Technology Beijing University of Chemical Technology Beijing China
- Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China
| | - Yanlin He
- College of Information Science & Technology Beijing University of Chemical Technology Beijing China
- Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China
| | - Qunxiong Zhu
- College of Information Science & Technology Beijing University of Chemical Technology Beijing China
- Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China
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