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Balaji B, Shahab MA, Srinivasan B, Srinivasan R. ACT-R based human digital twin to enhance operators' performance in process industries. Front Hum Neurosci 2023; 17:1038060. [PMID: 36845875 PMCID: PMC9945966 DOI: 10.3389/fnhum.2023.1038060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 01/12/2023] [Indexed: 02/10/2023] Open
Abstract
To ensure safe and efficient operation, operators in process industries have to make timely decisions based on time-varying information. A holistic assessment of operators' performance is, therefore, challenging. Current approaches to operator performance assessment are subjective and ignore operators' cognitive behavior. In addition, these cannot be used to predict operators' expected responses during novel situations that may arise during plant operations. The present study seeks to develop a human digital twin (HDT) that can simulate a control room operator's behavior, even during various abnormal situations. The HDT has been developed using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. It mimics a human operator as they monitor the process and intervene during abnormal situations. We conducted 426 trials to test the HDT's ability to handle disturbance rejection tasks. In these simulations, we varied the reward and penalty parameters to provide feedback to the HDT. We validated the HDT using the eye gaze behavior of 10 human subjects who completed 110 similar disturbance rejection tasks as that of the HDT. The results indicate that the HDT exhibits similar gaze behaviors as the human subjects, even when dealing with abnormal situations. These indicate that the HDT's cognitive capabilities are comparable to those of human operators. As possible applications, the proposed HDT can be used to generate a large database of human behavior during abnormalities which can then be used to spot and rectify flaws in novice operator's mental models. Additionally, the HDT can also enhance operators' decision-making during real-time operation.
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Affiliation(s)
- Bharatwaajan Balaji
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Mohammed Aatif Shahab
- Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
| | - Babji Srinivasan
- Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
- American Express Lab for Data Analytics, Risk and Technology, Indian Institute of Technology Madras, Chennai, India
| | - Rajagopalan Srinivasan
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India
- American Express Lab for Data Analytics, Risk and Technology, Indian Institute of Technology Madras, Chennai, India
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2
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Chen H, Cen J, Yang Z, Si W, Cheng H. Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network. ACS OMEGA 2022; 7:34389-34400. [PMID: 36188261 PMCID: PMC9521029 DOI: 10.1021/acsomega.2c04017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Deep learning provides new ideas for chemical process fault diagnosis, reducing potential risks and ensuring safe process operation in recent years. To address the problem that existing methods have difficulty extracting the dynamic fault features of a chemical process, a fusion model (CS-IMLSTM) based on a convolutional neural network (CNN), squeeze-and-excitation (SE) attention mechanism, and improved long short-term memory network (IMLSTM) is developed for chemical process fault diagnosis in this paper. First, an extended sliding window is utilized to transform data into augmented dynamic data to enhance the dynamic features. Second, the SE is utilized to optimize the key fault features of augmented dynamic data extracted by CNN. Then, IMLSTM is used to balance fault information and further mine the dynamic features of time series data. Finally, the feasibility of the proposed method is verified in the Tennessee-Eastman process (TEP). The average accuracies of this method in two subdata sets of TEP are 98.29% and 97.74%, respectively. Compared with the traditional CNN-LSTM model, the proposed method improves the average accuracies by 5.18% and 2.10%, respectively. Experimental results confirm that the method developed in this paper is suitable for chemical process fault diagnosis.
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Affiliation(s)
- Honghua Chen
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
| | - Jian Cen
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
| | - Zhuohong Yang
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
| | - Weiwei Si
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
| | - Hongchao Cheng
- School
of Automation, Guangdong Polytechnic Normal
University, Guangzhou 510665, China
- Guangzhou
Intelligent Building Equipment Information Integration and Control
Key Laboratory, Guangzhou 510665, China
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Semi-supervised LSTM ladder autoencoder for chemical process fault diagnosis and localization. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117467] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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4
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Impact of Weak Signals on the Digitalization of Risk Analysis in Process Safety Operational Environments. Processes (Basel) 2022. [DOI: 10.3390/pr10040631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/10/2022] Open
Abstract
Weak signals in risk analysis digitalization are of great importance for preventing major accidents in risk analysis in the process industry, especially for process operations and production. However, some of the negative impacts are incorrect operational risk identification, significant inventory carrying costs, disruption of risk frequency, and risk consequence analysis, all of which will signal inaccurate information about unforeseen and current dangers in process facilities and operational environments. While the positive impacts are viewed as an early warning system that provides information on operational risk system status, the identification of potential risk weaknesses in process facilities, indicators of a transition or an emerging problem that may become significant in the future, highlighting future assumptions, challenge our views of the future and expand the selection of a processing facility. Lastly, weak signal identification in the digitalization of risk analysis can provide relevant information in supporting, assessing and analyzing the risks associated with the operation, in order to design a technical system and estimate the industry’s level of accident risk, as well as the possible control of a system. The present research will provide valuable information to the process industry on how to protect their operational facilities and increase process safety by providing information on weak safety risk monitoring systems in operations, strengthening the processes of the operational area.
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Naef M, Chadha K, Lefsrud L. Decision support for process operators: Task loading in the days of big data. J Loss Prev Process Ind 2022. [DOI: 10.1016/j.jlp.2021.104713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Yang P, Huang X, Peng L, Zheng Z, Wu X, Xing C. Safety evaluation of major hazard installations based on regional disaster system theory. J Loss Prev Process Ind 2021. [DOI: 10.1016/j.jlp.2020.104346] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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7
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Arunthavanathan R, Khan F, Ahmed S, Imtiaz S. An analysis of process fault diagnosis methods from safety perspectives. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2020.107197] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Iqbal MU, Srinivasan B, Srinivasan R. Dynamic assessment of control room operator's cognitive workload using Electroencephalography (EEG). Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106726] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Song G, Salvo Rossi P, Khan F, Paltrinieri N, BahooToroody A. Model-based information fusion investigation on fault isolation of subsea systems considering the interaction among subsystems and sensors. J Loss Prev Process Ind 2020. [DOI: 10.1016/j.jlp.2020.104267] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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10
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Yang S, Feng X, Liu L, Zhang Z, Deng C, Du J, Zhao J, Qian Y. Research advances on process systems integration and process safety in China. REV CHEM ENG 2019. [DOI: 10.1515/revce-2017-0046] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Process systems engineering research focuses on the planning, design, operation, and safety of process systems rather than unit operations. In response to the rapid growth of the chemical process industry in the last 20 years in China, advanced system integration and process safety technologies are investigated and applied for better resource utilization, less environmental impact, and safer working places. In this regard, the review in this article consists of four main achievements: (1) process synthesis, (2) energy system integration, (3) water system integration, and (4) process safety management. The purpose of process synthesis and integration is to improve resource and energy utilization, at the same time lowering by-products and emissions. Optimization is conducted on process structure and operation, following the principles of resource coupling and energy cascade utilization. Typical examples are coupling of coal and hydrogen-rich resources and integration of coal-based polygeneration process of chemicals, electricity, and heat. Energy integration implements the coordinated optimization of total site energy systems. Reviews are made on specific methodologies based on the thermodynamics and applications of design and retrofit in ethylene, oil refining, and synthetic ammonia industries. There are energy savings by 10%–20% and yields increasing by 20%–30%. In addition, waste heat recovery and cold energy utilization are also important research areas. Reviews on the progress of water system integration and its industrial applications are also conducted. It includes the direct reuse, regeneration, and reuse/recycle in water systems and systems with internal water mains. Finally, safety management and technologies are also indispensable technological advancements of the process. The legislation system and the work safety-related standard system have been gradually established and enforced. Process safety research progress is reviewed, and questions are proposed for improving the accident prevention and safety management agenda.
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Zhang S, Bi K, Qiu T. Bidirectional Recurrent Neural Network-Based Chemical Process Fault Diagnosis. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b05885] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Shuyuan Zhang
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing 100084, China
| | - Kexin Bi
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing 100084, China
| | - Tong Qiu
- Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing 100084, China
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He R, Chen G, Dong C, Sun S, Shen X. Data-driven digital twin technology for optimized control in process systems. ISA TRANSACTIONS 2019; 95:221-234. [PMID: 31109723 DOI: 10.1016/j.isatra.2019.05.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/22/2019] [Accepted: 05/11/2019] [Indexed: 06/09/2023]
Abstract
Due to the installation of various apparatus in process industries, both factors of complex structures and severe operating conditions could result in higher accident frequencies and maintenance challenges. Given the importance of security in process systems, this paper presents a data-driven digital twin system for automatic process applications by integrating virtual modeling, process monitoring, diagnosis, and optimized control into a cooperative architecture. For unknown model parameters, the adaptive system identification is proposed to model closed-loop virtual systems and residual signals with fault-free case data. Performance indices are improved to make the design of robust monitoring and diagnosis system to identify the apparatus status. Soft-sensor, parameterization control, and model-matching reconfiguration are ameliorated and incorporated into the optimized control configuration to guarantee stable and safe control performance under apparatus faults. The effectiveness and performance of the proposed digital twin system are evaluated by using different simulations on the Tennessee Eastman benchmark process in the presence of realistic fault scenarios.
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Affiliation(s)
- Rui He
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
| | - Guoming Chen
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
| | - Che Dong
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
| | - Shufeng Sun
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
| | - Xiaoyu Shen
- Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), No.66, Changjiang West Road, Qingdao, China.
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13
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Ming L, Zhao J. Feature selection for chemical process fault diagnosis by artificial immune systems. Chin J Chem Eng 2018. [DOI: 10.1016/j.cjche.2017.09.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Flame Failures and Recovery in Industrial Furnaces: A Neural Network Steady-State Model for the Firing Rate Setpoint Rearrangement. INTERNATIONAL JOURNAL OF CHEMICAL ENGINEERING 2018. [DOI: 10.1155/2018/3790849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Burner failures are common abnormal conditions associated with industrial fired heaters. Preventing from economic loss and major equipment damages can be attained by compensating the lost heat due to burners’ failures, which can be possible by defining appropriate setpoints to rearrange the firing rates for healthy burners. In this study, artificial neural network models were developed for estimating the appropriate setpoints for the combustion control system to recover an industrial fired-heater furnace from abnormal conditions. For this purpose, based on an accurate high-order mathematical model, constrained nonlinear optimization problems were solved using the genetic algorithm. For different failure scenarios, the best possible excess firing rates for healthy burners to recover the furnace from abnormal conditions were obtained and data were recorded for training and testing stages. The performances of the developed neural steady-state models were evaluated through simulation experiments. The obtained results indicated the feasibility of the proposed technique to deal with the failures in the combustion system.
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15
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El-Halwagi M, Ng KM. Editorial overview: Process systems engineering. Curr Opin Chem Eng 2016. [DOI: 10.1016/j.coche.2016.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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