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Sangiorgi L, Sberveglieri V, Carnevale C, De Nardi S, Nunez-Carmona E, Raccagni S. Data-Driven Virtual Sensing for Electrochemical Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:1396. [PMID: 38474932 DOI: 10.3390/s24051396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024]
Abstract
In recent years, the application of machine learning for virtual sensing has revolutionized the monitoring and management of information. In particular, electrochemical sensors generate large amounts of data, allowing the application of complex machine learning/AI models able to (1) reproduce the measured data and (2) predict and manage faults in the measuring sensor. In this work, data-driven models based on an autoregressive model and an artificial neural network have been identified and used to (i) evaluate sensor redundancy and (ii) predict and manage faults in the context of electrochemical sensors for the measurement of ethanol. The approach shows encouraging results in terms of both performance and sensitivity analyses, allowing for the reconstruction of the values measured by two sensors in a series of six sensors with different dopant levels and to reproduce their values after a fault.
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Affiliation(s)
- Lucia Sangiorgi
- Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy
| | - Veronica Sberveglieri
- National Research Council, Institute of Bioscience and Bioresources (CNR-IBBR), Via J.F. Kennedy, 42124 Reggio Emilia, Italy
- Nano Sensor System srl (NASYS), Via Alfonso Catalani, 42124 Reggio Emilia, Italy
| | - Claudio Carnevale
- Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy
| | - Sabrina De Nardi
- Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy
| | - Estefanía Nunez-Carmona
- National Research Council, Institute of Bioscience and Bioresources (CNR-IBBR), Via J.F. Kennedy, 42124 Reggio Emilia, Italy
| | - Sara Raccagni
- Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy
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2
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Wei C, Wen C, He J, Song Z. Visual Process Monitoring by Data-Dependent Kernel Discriminant Analysis with t-Distributed Similarities. ACS OMEGA 2023; 8:38013-38024. [PMID: 37867721 PMCID: PMC10586300 DOI: 10.1021/acsomega.3c03496] [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: 05/19/2023] [Accepted: 09/21/2023] [Indexed: 10/24/2023]
Abstract
Visual process monitoring would provide more directly appreciable and more easily comprehensible information about the process operating status as well as clear depictions of the occurrence path of faults; however, as a more challenging task, it has been sporadically discussed in the research literature on conventional process monitoring. In this paper, the Data-Dependent Kernel Discriminant Analysis (D2K-DA) model is proposed. A special data-dependent kernel function is constructed and learned from the measured data, so that the low-dimensional visualizations are guaranteed, combined with intraclass compactness, interclass separability, local geometry preservation, and global geometry preservation. The new optimization is innovatively designed by exploiting both discriminative information and t-distributed geometric similarities. On the construction of novel indexes for visualization, experiments of visual monitoring tasks on simulated and real-life industrial processes illustrate the merits of the proposed method.
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Affiliation(s)
- Chihang Wei
- Guangdong
Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China
- School
of Information Science and Technology, Hangzhou
Normal University, Hangzhou 311121, China
| | - Chenglin Wen
- Guangdong
Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Jieguang He
- Guangdong
Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Zhihuan Song
- State
Key Laboratory of Industrial Control Technology, College of Control
Science and Engineering, Zhejiang University, Hangzhou 310027, China
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3
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Wei C, Song Z. Real-Time Forecasting of Subsurface Inclusion Defects for Continuous Casting Slabs: A Data-Driven Comparative Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:5415. [PMID: 37420581 DOI: 10.3390/s23125415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 05/30/2023] [Accepted: 06/06/2023] [Indexed: 07/09/2023]
Abstract
Subsurface inclusions are one of the most common defects that affect the inner quality of continuous casting slabs. This increases the defects in the final products and increases the complexity of the hot charge rolling process and may even cause breakout accidents. The defects are, however, hard to detect online by traditional mechanism-model-based and physics-based methods. In the present paper, a comparative study is carried out based on data-driven methods, which are only sporadically discussed in the literature. As a further contribution, a scatter-regularized kernel discriminative least squares (SR-KDLS) model and a stacked defect-related autoencoder back propagation neural network (SDAE-BPNN) model are developed to improve the forecasting performance. The scatter-regularized kernel discriminative least squares is designed as a coherent framework to directly provide forecasting information instead of low-dimensional embeddings. The stacked defect-related autoencoder back propagation neural network extracts deep defect-related features layer by layer for a higher feasibility and accuracy. The feasibility and efficiency of the data-driven methods are demonstrated through case studies based on a real-life continuous casting process, where the imbalance degree drastically vary in different categories, showing that the defects are timely (within 0.01 ms) and accurately forecasted. Moreover, experiments illustrate the merits of the developed scatter-regularized kernel discriminative least squares and stacked defect-related autoencoder back propagation neural network methods regarding the computational burden; the F1 scores of the developed methods are clearly higher than common methods.
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Affiliation(s)
- Chihang Wei
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
| | - Zhihuan Song
- State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
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4
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Liu J, Tsai BY, Chen DS. Deep reinforcement learning based controller with dynamic feature extraction for an industrial claus process. J Taiwan Inst Chem Eng 2023. [DOI: 10.1016/j.jtice.2023.104779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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5
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Li Y, Han W, Shao W, Zhao D. Virtual sensing for dynamic industrial process based on localized linear dynamical system models with time-delay optimization. ISA TRANSACTIONS 2023; 133:505-517. [PMID: 35810027 DOI: 10.1016/j.isatra.2022.06.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 05/10/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Virtual sensors play an important role in real-time sensing of key quality-related variables in industrial processes. Linear dynamical system (LDS) paradigm has established itself as a powerful tool for developing dynamic virtual sensors. However, there are still some practically pivotal issues unresolved, such as how to improve the generalization reliability and accuracy when accounting for the time delays and how to broaden the application sphere by breaking their limitations to linear processes. Motivated by dealing with these challenging issues this paper proposes a virtual sensing framework called 'localized LDS (LoLDS)'. In the LoLDS framework, the process dynamics and nonlinearities are taken into consideration from different scales without increasing the model complexity, and the time delays are intelligently optimized which triggers the model inconsistency by a designed diversified localization scheme at the offline stage. Moreover, an adaptive online model switch scheme is developed to enable the real-timely best LDS models to be responsible to predict the quality variables. The offline and online operations together enable the LoLDS to improve the generalization performance of the dynamic virtual sensor. The LoLDS framework is highly automated, and its performance has been extensively evaluated by two real-life industrial processes, showing very promising application foregrounds.
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Affiliation(s)
- Yougao Li
- Department of Chemical Equipment and Control Engineering, College of New Energy, China University of Petroleum (East China), Qingdao 266580, China.
| | - Wenxue Han
- Department of Chemical Equipment and Control Engineering, College of New Energy, China University of Petroleum (East China), Qingdao 266580, China.
| | - Weiming Shao
- Department of Chemical Equipment and Control Engineering, College of New Energy, China University of Petroleum (East China), Qingdao 266580, China.
| | - Dongya Zhao
- Department of Chemical Equipment and Control Engineering, College of New Energy, China University of Petroleum (East China), Qingdao 266580, China.
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A survey on big data-enabled innovative online education systems during the COVID-19 pandemic. JOURNAL OF INNOVATION & KNOWLEDGE 2023; 8:100295. [PMCID: PMC9800816 DOI: 10.1016/j.jik.2022.100295] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 12/28/2022] [Indexed: 07/15/2023]
Abstract
With the spread of COVID-19 around the world, the education industry faces enormous challenges. Some colleges and universities have launched online teaching. Comprehensive online teaching and student health checkups help students complete the set teaching content and return to school as soon as possible. With the development of big data, combined with the epidemic risk we are facing, the rational use of big data and the internet for innovative online education has become a mainstream teaching method. Colleges and universities are not yet familiar with the development prospects and future of online education. Through the research of this paper, we can understand the combination of online education and the development of big data and promote its application in colleges and universities. Not only have innovative online education platforms such as MOOC and DingTalk been widely used, but innovative online education methods such as virtual classrooms also have been created. Based on the current epidemic background, this paper analyzes the development of online education, introduces the impact of the combination of online education and big data, and introduces innovative online education technologies and their effects. It helps online education under the influence of the new coronavirus epidemic, operating big data technology to analyze the current prospects and development of online education, showing the combination of big data technology and online education through the analysis of big data technology, and ending with more expectations on other aspects of the use of big data, which affects the online education industry as well as other industries. Finally, we summarize the combination of big data and innovative online education since the emergence of COVID-19 and introduce the concepts and methods of combining online education and big data technology in detail. The online education platform also makes a reasonable introduction. The thesis can be used to understand the problems and challenges faced by innovative online education in the context of the new coronavirus epidemic and look forward to the future on this basis.
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Tong Y, Shu M, Li M, Liu Y, Tao R, Zhou C, Zhao Y, Zhao G, Li Y, Dong Y, Zhang L, Liu L, Du J. A neural network-based production process modeling and variable importance analysis approach in corn to sugar factory. Front Chem Sci Eng 2022. [DOI: 10.1007/s11705-022-2190-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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8
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Reis MS, Strelet E, Sansana J, Quina MJ, Gando-Ferreira LM, Rato TJ. A Federated Classification Approach of Waste Lubricant Oils in Geographically Distributed Laboratories. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02293] [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]
Affiliation(s)
- Marco S. Reis
- Department of Chemical Engineering, Univ Coimbra, CIEPQPF, Rua Sílvio Lima, Pólo II─Pinhal de Marrocos, 3030-790Coimbra, Portugal
| | - Eugeniu Strelet
- Department of Chemical Engineering, Univ Coimbra, CIEPQPF, Rua Sílvio Lima, Pólo II─Pinhal de Marrocos, 3030-790Coimbra, Portugal
| | - Joel Sansana
- Department of Chemical Engineering, Univ Coimbra, CIEPQPF, Rua Sílvio Lima, Pólo II─Pinhal de Marrocos, 3030-790Coimbra, Portugal
| | - Margarida J. Quina
- Department of Chemical Engineering, Univ Coimbra, CIEPQPF, Rua Sílvio Lima, Pólo II─Pinhal de Marrocos, 3030-790Coimbra, Portugal
| | - Licínio M. Gando-Ferreira
- Department of Chemical Engineering, Univ Coimbra, CIEPQPF, Rua Sílvio Lima, Pólo II─Pinhal de Marrocos, 3030-790Coimbra, Portugal
| | - Tiago J. Rato
- Department of Chemical Engineering, Univ Coimbra, CIEPQPF, Rua Sílvio Lima, Pólo II─Pinhal de Marrocos, 3030-790Coimbra, Portugal
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10
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Ma K, Sahinidis NV, Bindlish R, Bury SJ, Haghpanah R, Rajagopalan S. Data-driven strategies for extractive distillation unit optimization. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107970] [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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11
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Wei C, Zuo L, Zhang X, Song Z. Hessian Semisupervised Scatter Regularized Classification Model With Geometric and Discriminative Information for Nonlinear Process. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:8862-8875. [PMID: 33729981 DOI: 10.1109/tcyb.2021.3062058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The integration of semisupervised modeling and discriminative information has been sporadically discussed in the research literature of traditional classification modeling, while the former one would make full use of the collected data and the latter one would further improve the classification performance. In this article, the Hessian semisupervised scatter regularized classification model is proposed as a coherent framework for the nonlinear process classification upon both labeled and unlabeled data. It is innovatively designed with a loss function to evaluate the classification accuracy and three regularization terms, respectively, corresponding to the geometry information, discriminative information, and model complexity. Both cases of the coherent framework, respectively, casted to the reproducing kernel Hilbert space and linear space, enjoy a theoretically guaranteed analytical solution. Experiments on process classification tasks on a benchmark dataset and a real industrial polyethylene process illustrate the merits of the proposed method in a sense that the class information of novel collected data is accurately predicted.
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12
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Data predictive control of nonlinear process feature dynamics through latent variable behaviours. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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13
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Decision fusion for reliable fault classification in energy-intensive process industries. COMPUT IND 2022. [DOI: 10.1016/j.compind.2022.103640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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14
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Dias T, Oliveira R, Saraiva PM, Reis MS. Linear and Non-Linear Soft Sensors for Predicting the Research Octane Number (RON) through Integrated Synchronization, Resolution Selection and Modelling. SENSORS 2022; 22:s22103734. [PMID: 35632144 PMCID: PMC9146269 DOI: 10.3390/s22103734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/05/2022] [Accepted: 05/09/2022] [Indexed: 11/29/2022]
Abstract
The Research Octane Number (RON) is a key quality parameter for gasoline, obtained offline through complex, time-consuming, and expensive standard methods. Measurements are usually only available a few times per week and after long delays, making process control very challenging. Therefore, alternative methods have been proposed to predict RON from readily available data. In this work, we report the development of inferential models for predicting RON from process data collected in a real catalytic reforming process. Data resolution and synchronization were explicitly considered during the modelling stage, where 20 predictive linear and non-linear machine learning models were assessed and compared using a robust Monte Carlo double cross-validation approach. The workflow also handles outliers, missing data, multirate and multiresolution observations, and processes dynamics, among other features. Low RMSE were obtained under testing conditions (close to 0.5), with the best methods belonging to the class of penalized regression methods and partial least squares. The developed models allow for improved management of the operational conditions necessary to achieve the target RON, including a more effective use of the heating utilities, which improves process efficiency while reducing costs and emissions.
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Affiliation(s)
- Tiago Dias
- Department of Chemical Engineering, University of Coimbra, CIEPQPF, Rua Sílvio Lima, Pólo II—Pinhal de Marrocos, 3030-790 Coimbra, Portugal; (T.D.); (P.M.S.)
- Petrogal, S.A., 4451-852 Leça da Palmeira, Portugal;
| | | | - Pedro M. Saraiva
- Department of Chemical Engineering, University of Coimbra, CIEPQPF, Rua Sílvio Lima, Pólo II—Pinhal de Marrocos, 3030-790 Coimbra, Portugal; (T.D.); (P.M.S.)
- Dean of NOVA IMS, Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal
| | - Marco S. Reis
- Department of Chemical Engineering, University of Coimbra, CIEPQPF, Rua Sílvio Lima, Pólo II—Pinhal de Marrocos, 3030-790 Coimbra, Portugal; (T.D.); (P.M.S.)
- Correspondence:
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15
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FISHER O, WATSON NJ, PORCU L, BACON D, RIGLEY M, GOMES RL. Data-driven modelling of bioprocesses: Data volume, variability, and visualisation for an industrial bioprocess. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108499] [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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16
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Zhao L, Yang J. Batch Process Monitoring Based on Quality-Related Time-Batch 2D Evolution Information. SENSORS (BASEL, SWITZERLAND) 2022; 22:2235. [PMID: 35336405 PMCID: PMC8954576 DOI: 10.3390/s22062235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
This paper proposed a quality-related online monitoring strategy based on time and batch two-dimensional evolution information for batch processes. In the direction of time, considering the difference between each phase and the steady part and the transition part in the same phase, the change trend of the regression coefficient of the PLS model is used to divide each batch into phases, and each phase into parts. The phases, the steady parts, and the transition parts are finally distinguished and dealt with separately in the subsequent modeling process. In the batch direction, considering the slow time-varying characteristics of batch evolution, sliding windows are used to perform mode division by analyzing the evolution trend of the score matrix T in the PLS model on the base of phase division and within-phase part division. Finally, an online monitoring model that comprehensively considers the evolution information of time and batch is obtained. In a typical batch operation process, injection molding is used as an example for experimental analysis. The results show that the proposed algorithm takes advantage of mixing the time-batch two-dimensional evolution information. Compared with the traditional methods, the proposed method can overcome the shortcomings caused by the single dimension analysis and has better monitoring results.
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17
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Bao Y, Wang B, Guo P, Wang J. Chemical process fault diagnosis based on a combined deep learning method. CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24153] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Yu Bao
- School of Chemical Engineering and Technology Tianjin University Tianjin People's Republic of China
| | - Bo Wang
- School of Chemical Engineering and Technology Tianjin University Tianjin People's Republic of China
| | - Pandeng Guo
- School of Chemical Engineering and Technology Tianjin University Tianjin People's Republic of China
| | - Jingtao Wang
- School of Chemical Engineering and Technology Tianjin University Tianjin People's Republic of China
- Tianjin Key Laboratory of Chemical Process Safety and Equipment Technology Tianjin People's Republic of China
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18
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Retrospective comparison of several typical linear dynamic latent variable models for industrial process monitoring. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107587] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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19
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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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20
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Ozbuyukkaya G, Parker RS, Veser G. Determining robust reaction kinetics from limited data. AIChE J 2021. [DOI: 10.1002/aic.17538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Gizem Ozbuyukkaya
- Department of Chemical Engineering, Swanson School of Engineering, and Center for Energy University of Pittsburgh Pittsburgh Pennsylvania USA
| | - Robert S. Parker
- Department of Chemical Engineering, Swanson School of Engineering, and Center for Energy University of Pittsburgh Pittsburgh Pennsylvania USA
| | - Goetz Veser
- Department of Chemical Engineering, Swanson School of Engineering, and Center for Energy University of Pittsburgh Pittsburgh Pennsylvania USA
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22
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Gärtler M, Khaydarov V, Klöpper B, Urbas L. The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100134] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Marco Gärtler
- ABB Corporate Research Center Wallstadter Straße 59 68526 Ladenburg Germany
| | - Valentin Khaydarov
- Technische Universität Dresden Professur für Prozessleittechnik 01062 Dresden Germany
| | - Benjamin Klöpper
- ABB Corporate Research Center Wallstadter Straße 59 68526 Ladenburg Germany
| | - Leon Urbas
- Technische Universität Dresden Professur für Prozessleittechnik 01062 Dresden Germany
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23
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Schweidtmann AM, Esche E, Fischer A, Kloft M, Repke J, Sager S, Mitsos A. Machine Learning in Chemical Engineering: A Perspective. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100083] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Artur M. Schweidtmann
- Delft University of Technology Department of Chemical Engineering Van der Maasweg 9 2629 HZ Delft The Netherlands
- RWTH Aachen University Aachener Verfahrenstechnik Forckenbeckstr. 51 52074 Aachen Germany
| | - Erik Esche
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
| | - Asja Fischer
- Ruhr-Universität Bochum Department of Mathematics Universitätsstraße 150 44801 Bochum Germany
| | - Marius Kloft
- Technische Universität Kaiserslautern Department of Computer Science Erwin-Schrödinger-Straße 52 67663 Kaiserslautern Germany
| | - Jens‐Uwe Repke
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
| | - Sebastian Sager
- Otto-von-Guericke-Universität Magdeburg Department of Mathematics Universitätsplatz 2 39106 Magdeburg Germany
| | - Alexander Mitsos
- RWTH Aachen University Aachener Verfahrenstechnik Forckenbeckstr. 51 52074 Aachen Germany
- JARA Center for Simulation and Data Science (CSD) Aachen Germany
- Forschungszentrum Jülich Institute for Energy and Climate Research IEK-10 Energy Systems Engineering Wilhelm-Johnen-Straße 52428 Jülich Germany
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25
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Yao L, Shao W, Ge Z. Hierarchical Quality Monitoring for Large-Scale Industrial Plants With Big Process Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:3330-3341. [PMID: 31902781 DOI: 10.1109/tnnls.2019.2958184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
For large-scale industrial plants, quality-related process monitoring is challenging because of the complex features of multiunit, multimode, high-dimension data. Hence, a hierarchical quality monitoring (HQM) algorithm based on the distributed parallel semisupervised Gaussian mixture model (dp-S2GMM) is proposed in this article. In HQM, a large-scale process is first decomposed into a group of unit blocks according to the process structure. Subsequently, in each block, a quality regression model with multimode big process data is built using the dp-S2GMM, which is derived from a scalable stochastic variational inference semisupervised GMM (SVI-S2GMM). With the regression model, a hierarchical fault detection and diagnosis scheme in both quality-related and quality-unrelated subspaces is proposed from the variable level, block level to plant-wide level. Finally, an industrial case study on the Tennessee Eastman process demonstrates the feasibility and effectiveness of the proposed HQM algorithm.
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Jiang Q, Yan S, Cheng H, Yan X. Local-Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:3355-3365. [PMID: 32324574 DOI: 10.1109/tnnls.2020.2985223] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Industrial big data and complex process nonlinearity have introduced new challenges in plant-wide process monitoring. This article proposes a local-global modeling and distributed computing framework to achieve efficient fault detection and isolation for nonlinear plant-wide processes. First, a stacked autoencoder is used to extract dominant representations of each local process unit and establish the local inner monitor. Second, mutual information (MI) is used to determine the neighborhood variables of a local unit. Afterward, a joint representation learning is then performed between the local unit and the neighborhood variables to extract the outer-related representations and establish the outer-related monitor for the local unit. Finally, the outer-related representations from all process units are used to establish global monitoring systems. Given that the modeling of each unit can be performed individually, the computation process can be efficiently completed with different CPUs. The proposed modeling and monitoring method is applied to the Tennessee Eastman (TE) and laboratory-scale glycerol distillation processes to demonstrate the feasibility of the method.
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Zhang MQ, Luo XL. Novel dynamic enhanced robust principal subspace discriminant analysis for high-dimensional process fault diagnosis with industrial applications. ISA TRANSACTIONS 2021; 114:1-14. [PMID: 33388145 DOI: 10.1016/j.isatra.2020.12.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 11/30/2020] [Accepted: 12/10/2020] [Indexed: 06/12/2023]
Abstract
Since the data are often polluted by numerous measured noise or outliers, traditional subspace discriminant analysis is difficult to extract optimal diagnostic information. To alleviate the impact of the problem, a robust principal subspace discriminant analysis algorithm for fault diagnosis is designed. On the premise of decreasing the impact of redundant information, the optimal latent features can be calculated. Specifically, in the algorithm, dual constraints of the weighted principal subspace center and l2,1-norm are introduced into the objective function to suppress outliers and noise. Besides, considering that the current changes of the data in a dynamic process rely on past observations, merely analyzing the current data may lead to an incorrect interpretation of the mechanism model, especially in the presence of similar variable data under the two different conditions. Therefore, based on the robust principal subspace discriminant analysis, we further develop its dynamic enhanced version. The dynamic enhanced method utilizes the dynamic augmented matrix to enhance the latent features of historical data into current shifted features, so as to enlarge the difference between similar modes. Finally, the experimental results arranged on the Tennessee Eastman process and a commercial multi-phase flow process demonstrate that the proposed method has advanced diagnostic performance and satisfactory convergence speed.
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Affiliation(s)
- Ming-Qing Zhang
- Department of Automation, China University of Petroleum, Beijing, 102249, China.
| | - Xiong-Lin Luo
- Department of Automation, China University of Petroleum, Beijing, 102249, China.
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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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29
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Quality-Analysis-Based Process Monitoring for Multi-Phase Multi-Mode Batch Processes. Processes (Basel) 2021. [DOI: 10.3390/pr9081321] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance between the historical modes and the new mode. Further, online quality prediction is achieved applying the selected model based on multi-mode quality analysis, and an according process-monitoring strategy is developed. The simulation results show the availability of this method for multi-phase multi-mode batch processes.
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Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11083535] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process.
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31
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Cao XY, Xu F, Luo XL. A novel strategy of continuous process transition and wide range throughput fluctuating ethylene column. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.03.052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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32
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Zhang MQ, Luo XL. Modified canonical variate analysis based on dynamic kernel decomposition for dynamic nonlinear process quality monitoring. ISA TRANSACTIONS 2021; 108:106-120. [PMID: 32854955 DOI: 10.1016/j.isatra.2020.08.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 08/11/2020] [Accepted: 08/11/2020] [Indexed: 06/11/2023]
Abstract
It is crucial to adopt an efficient process monitoring technique that ensures process operation safety and improves product quality. Toward this endeavor, a modified canonical variate analysis based on dynamic kernel decomposition (DKDCVA) approach is proposed for dynamic nonlinear process quality monitoring. Different from traditional canonical variate analysis and its expansive kernel methods, the chief intention of the our proposed method is to establish a partial-correlation nonlinear model between input dynamic kernel latent variables and output variables, and ensures the extracted feature information can be maximized. More specifically, the dynamic nonlinear model is orthogonally decomposed to obtain quality-related and independent subspace by singular value decomposition. From the perspective of quality monitoring, Hankel matrices of past and future vectors of quality-related subspace are derived in detail, and corresponding statistical metrics are constructed. Furthermore, given the existence of non-Gaussian process variables, kernel density estimation evaluates the upper control limit instead of traditional control limits. Finally, the experimental results conducted on a simple numerical example, the Tennessee Eastman process and the hot strip mill process indicate that the DKDCVA approach can be preferable to monitor abnormal operation for the dynamic nonlinear process.
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Affiliation(s)
- Ming-Qing Zhang
- Department of Automation, China University of Petroleum Beijing, 102249, China.
| | - Xiong-Lin Luo
- Department of Automation, China University of Petroleum Beijing, 102249, China.
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Li W, Chai Y, Khan F, Jan SRU, Verma S, Menon VG, Li X. A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System. MOBILE NETWORKS AND APPLICATIONS 2021; 26. [PMCID: PMC7786888 DOI: 10.1007/s11036-020-01700-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.
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Affiliation(s)
- Wei Li
- Faculty of Engineering, Huanghe Science and Technology College, Zhengzhou, China
| | - Yuanbo Chai
- Faculty of Engineering, Huanghe Science and Technology College, Zhengzhou, China
| | - Fazlullah Khan
- Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, 758307 Vietnam
- Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, 758307 Vietnam
| | - Syed Rooh Ullah Jan
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Sahil Verma
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab 140413 India
| | - Varun G. Menon
- Department of Computer Science and Engineering, SCMS School of Engineering and Technology, Ernakulam, 683576 India
| | - Xingwang Li
- School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, Henan Province China
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35
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Wang Y, Jiang Q. Recursive correlated representation learning for adaptive monitoring of slowly varying processes. ISA TRANSACTIONS 2020; 107:360-369. [PMID: 32768133 DOI: 10.1016/j.isatra.2020.07.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 05/27/2020] [Accepted: 07/28/2020] [Indexed: 06/11/2023]
Abstract
Correlated representation learning has found wide usage in process monitoring. However, slow and normal changes frequently occur in practical production processes, which may lead to model mismatch and degrade monitoring performance. Therefore, updating the monitoring model online and involving recently processed data information are important. This study proposes a recursive correlated representation learning (RCRL) incorporating an approach for online model updating for adaptive monitoring of slowly varying processes. First, an initial canonical correlation analysis-based monitoring model is established using historical process data. Second, an online model updating criterion is developed, and updating procedures are provided to reflect online data information and update monitoring model in a timely manner. Then, monitoring statistics are established and decision making logic is established to identify process status. The fitness of the monitoring scheme is increased because the online process information is considered to update the model. The proposed RCRL-based monitoring scheme is applied on a numerical example and a lab-scale distillation process. The effectiveness and superiority of the RCRL approach are verified.
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Affiliation(s)
- Yang Wang
- School of Electric Engineering, Shanghai Dianji University, Shanghai 200240, PR China; School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, PR China
| | - Qingchao Jiang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China.
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36
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37
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Wang Y, Jiang Q. Data‐driven
nonlinear chemical process fault diagnosis based on hierarchical representation learning. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Yang Wang
- Department of Control Science and Engineering Zhejiang University Hangzhou China
- School of Electric Engineering Shanghai Dianji University Shanghai P. R. China
| | - Qingchao Jiang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai P. R. China
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38
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He G, Dang Y, Zhou L, Dai Y, Que Y, Ji X. Architecture model proposal of innovative intelligent manufacturing in the chemical industry based on multi-scale integration and key technologies. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106967] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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39
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Fisher OJ, Watson NJ, Escrig JE, Witt R, Porcu L, Bacon D, Rigley M, Gomes RL. Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106881] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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40
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Song W, Cao W, Hu W, Wu M. Identification of multiple operating modes based on fused features for continuous annealing processes. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.04.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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41
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Harjunkoski I, Ikonen T, Mostafaei H, Deneke T, Heljanko K. Synergistic and Intelligent Process Optimization: First Results and Open Challenges. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c02032] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Iiro Harjunkoski
- Aalto University, Department of Chemical and Metallurgical Engineering, P.O. Box
16100, 00076 Aalto, Finland
- Hitachi ABB Power Grids Research, Kallstadter Straße 1, 68309 Mannheim, Germany
| | - Teemu Ikonen
- Aalto University, Department of Chemical and Metallurgical Engineering, P.O. Box
16100, 00076 Aalto, Finland
| | - Hossein Mostafaei
- Aalto University, Department of Chemical and Metallurgical Engineering, P.O. Box
16100, 00076 Aalto, Finland
| | - Tewodros Deneke
- University of Helsinki, Department of Computer Science, P.O. Box 68, 00014 Helsinki, Finland
| | - Keijo Heljanko
- University of Helsinki, Department of Computer Science, P.O. Box 68, 00014 Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), 00014 Helsinki, Finland
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42
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McBride K, Sanchez Medina EI, Sundmacher K. Hybrid Semi‐parametric Modeling in Separation Processes: A Review. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000025] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Kevin McBride
- Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstraße 1 39106 Magdeburg Germany
| | - Edgar Ivan Sanchez Medina
- Otto-von-Guericke University Magdeburg Chair for Process Systems Engineering Universitätsplatz 2 39106 Magdeburg Germany
| | - Kai Sundmacher
- Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstraße 1 39106 Magdeburg Germany
- Otto-von-Guericke University Magdeburg Chair for Process Systems Engineering Universitätsplatz 2 39106 Magdeburg Germany
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43
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Md Nor N, Che Hassan CR, Hussain MA. A review of data-driven fault detection and diagnosis methods: applications in chemical process systems. REV CHEM ENG 2020. [DOI: 10.1515/revce-2017-0069] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractFault detection and diagnosis (FDD) systems are developed to characterize normal variations and detect abnormal changes in a process plant. It is always important for early detection and diagnosis, especially in chemical process systems to prevent process disruptions, shutdowns, or even process failures. However, there have been only limited reviews of data-driven FDD methods published in the literature. Therefore, the aim of this review is to provide the state-of-the-art reference for chemical engineers and to promote the application of data-driven FDD methods in chemical process systems. In general, there are two different groups of data-driven FDD methods: the multivariate statistical analysis and the machine learning approaches, which are widely accepted and applied in various industrial processes, including chemicals, pharmaceuticals, and polymers. Many different multivariate statistical analysis methods have been proposed in the literature, such as principal component analysis, partial least squares, independent component analysis, and Fisher discriminant analysis, while the machine learning approaches include artificial neural networks, neuro-fuzzy methods, support vector machine, Gaussian mixture model, K-nearest neighbor, and Bayesian network. In the first part, this review intends to provide a comprehensive literature review on applications of data-driven methods in FDD systems for chemical process systems. In addition, the hybrid FDD frameworks have also been reviewed by discussing the distinct advantages and various constraints, with some applications as examples. However, the choice for the data-driven FDD methods is not a straightforward issue. Thus, in the second part, this paper provides a guideline for selecting the best possible data-driven method for FDD systems based on their faults. Finally, future directions of data-driven FDD methods are summarized with the intent to expand the use for the process monitoring community.
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Affiliation(s)
- Norazwan Md Nor
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
- School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia
| | - Che Rosmani Che Hassan
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Mohd Azlan Hussain
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Li Z, Hong X, Hao K, Chen L, Huang B. Gaussian process regression with heteroscedastic noises — A machine-learning predictive variance approach. Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.02.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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45
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Bos TS, Knol WC, Molenaar SR, Niezen LE, Schoenmakers PJ, Somsen GW, Pirok BW. Recent applications of chemometrics in one- and two-dimensional chromatography. J Sep Sci 2020; 43:1678-1727. [PMID: 32096604 PMCID: PMC7317490 DOI: 10.1002/jssc.202000011] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/20/2020] [Accepted: 02/21/2020] [Indexed: 12/28/2022]
Abstract
The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.
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Affiliation(s)
- Tijmen S. Bos
- Division of Bioanalytical ChemistryAmsterdam Institute for Molecules, Medicines and SystemsVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Wouter C. Knol
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Stef R.A. Molenaar
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Leon E. Niezen
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Peter J. Schoenmakers
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Govert W. Somsen
- Division of Bioanalytical ChemistryAmsterdam Institute for Molecules, Medicines and SystemsVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
| | - Bob W.J. Pirok
- Analytical Chemistry Groupvan ’t Hoff Institute for Molecular Sciences, Faculty of ScienceUniversity of AmsterdamAmsterdamThe Netherlands
- Centre for Analytical Sciences Amsterdam (CASA)AmsterdamThe Netherlands
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46
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Modern Soft-Sensing Modeling Methods for Fermentation Processes. SENSORS 2020; 20:s20061771. [PMID: 32210053 PMCID: PMC7146123 DOI: 10.3390/s20061771] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/15/2020] [Accepted: 03/20/2020] [Indexed: 11/29/2022]
Abstract
For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.
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47
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A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring. Processes (Basel) 2019. [DOI: 10.3390/pr8010024] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.
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Abstract
With the increasing availability of large amounts of data, methods that fall under the term data science are becoming important assets for chemical engineers to use. Methods, broadly speaking, are needed to carry out three tasks, namely data management, statistical and machine learning and data visualization. While claims have been made that data science is essentially statistics, consideration of the three tasks previously mentioned make it clear that it is really broader than just statistics alone and furthermore, statistical methods from a data-poor era are likely insufficient. While there have been many successful applications of data science methodologies, there are still many challenges that must be addressed. For example, just because a dataset is large, does not necessarily mean it is meaningful or information rich. From an organizational point of view, a lack of domain knowledge and a lack of a trained workforce among other issues are cited as barriers for the successful implementation of data science within an organization. Many of the methodologies employed in data science are familiar to chemical engineers; however, it is generally the case that not all the methods required to carry out data science projects are covered in an undergraduate chemical engineering program. One option to address this is to adjust the curriculum by modifying existing courses and introducing electives. Other examples include the introduction of a data science minor or a postgraduate certificate or a Master’s program in data science.
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49
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Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty. Processes (Basel) 2019. [DOI: 10.3390/pr7100744] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is a powerful tool to bring benefits and save energy for ethylene plants. However, the uncertainty of device efficiencies and the fluctuation of the process demands cause great difficulties to traditional mathematical programming methods, which could result in suboptimal or infeasible solution. The growing data-driven optimization approaches offer new techniques to eliminate uncertainty in the process system engineering community. A data-driven robust optimization (DDRO) methodology is proposed to deal with uncertainty in the optimization of steam system in an ethylene plant. A hybrid model of extraction–exhausting steam turbine is developed, and its coefficients are considered as uncertain parameters. A deterministic mixed integer linear programming model of the steam system is formulated based on the model of the components to minimize the operating cost of the ethylene plant. The uncertain parameter set of the proposed model is derived from the historical data, and the Dirichlet process mixture model is employed to capture the features for the construction of the uncertainty set. In combination with the derived uncertainty set, a data-driven conic quadratic mixed-integer programming model is reformulated for the optimization of the steam system under uncertainty. An actual case study is utilized to validate the performance of the proposed DDRO method.
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50
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Tian W, Ren Y, Dong Y, Wang S, Bu L. Fault monitoring based on mutual information feature engineering modeling in chemical process. Chin J Chem Eng 2019. [DOI: 10.1016/j.cjche.2018.11.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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