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Wang X, Mohsin A, Sun Y, Li C, Zhuang Y, Wang G. From Spatial-Temporal Multiscale Modeling to Application: Bridging the Valley of Death in Industrial Biotechnology. Bioengineering (Basel) 2023; 10:744. [PMID: 37370675 DOI: 10.3390/bioengineering10060744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.
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Affiliation(s)
- Xueting Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Ali Mohsin
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yifei Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Chao Li
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai 200237, China
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Yu J, Zhang Y. Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08017-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Hematillake D, Freethy D, McGivern J, McCready C, Agarwal P, Budman H. Design and Optimization of a Penicillin Fed-Batch Reactor Based on a Deep Learning Fault Detection and Diagnostic Model. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Dylan Hematillake
- Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L3G1, Canada
| | - Daniele Freethy
- Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L3G1, Canada
| | - Jacob McGivern
- Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L3G1, Canada
| | - Chris McCready
- Sartorius Stedim North America, Oakville, Ontario L6M 2V9, Canada
| | - Piyush Agarwal
- Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L3G1, Canada
| | - Hector Budman
- Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L3G1, Canada
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Sparse one-dimensional convolutional neural network-based feature learning for fault detection and diagnosis in multivariable manufacturing processes. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06575-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Chen S, Yu J, Wang S. One-dimensional convolutional neural network-based active feature extraction for fault detection and diagnosis of industrial processes and its understanding via visualization. ISA TRANSACTIONS 2022; 122:424-443. [PMID: 33985785 DOI: 10.1016/j.isatra.2021.04.042] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 04/25/2021] [Accepted: 04/28/2021] [Indexed: 06/12/2023]
Abstract
Feature extraction from process signals enables process monitoring models to be effective in industrial processes. Deep learning presents extensive possibilities for extracting abstract features from image and visual data. However, the main inputs of conventional deep neural networks are large images. To overcome this, a one-dimension convolution neural network-based model optimized by a reinforcement-learning-based neural architecture search, is proposed for multivariate processes control. The experimental results illustrate its predominance for detecting and recognizing process faults. Feature and network visualization are also implemented to explore the reasons for its outstanding performance. This research extends the applications of convolutional neural network based on one-dimension process signals in complex multivariate process control.
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Affiliation(s)
- Shumei Chen
- School of Mechanical Engineering, Tongji University, 4800 CaoAn Road, 201804 Shanghai, PR China
| | - Jianbo Yu
- School of Mechanical Engineering, Tongji University, 4800 CaoAn Road, 201804 Shanghai, PR China.
| | - Shijin Wang
- School of Economics and Management, Tongji University, 4800 CaoAn Road, 201804 Shanghai, PR China
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Sun Y, Ding S, Zhang Z, Zhang C. Hypergraph based semi-supervised support vector machine for binary and multi-category classifications. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01452-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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8
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Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05171-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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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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Implementing multivariate statistics-based process monitoring: A comparison of basic data modeling approaches. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.02.051] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Ren H, Chai Y, Qu J, Ye X, Tang Q. A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: A case study on cryogenic propellant loading system. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.10.063] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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13
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Event-triggered fault detection framework based on subspace identification method for the networked control systems. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.02.027] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Vibration-Based Signal Analysis for Shearer Cutting Status Recognition Based on Local Mean Decomposition and Fuzzy C-Means Clustering. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7020164] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Raftery JP, DeSessa MR, Karim MN. Economic improvement of continuous pharmaceutical production via the optimal control of a multifeed bioreactor. Biotechnol Prog 2017; 33:902-912. [PMID: 28054464 DOI: 10.1002/btpr.2433] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 12/14/2016] [Indexed: 11/09/2022]
Abstract
Projections on the profitability of the pharmaceutical industry predict a large amount of growth in the coming years. Stagnation over the last 20 years in product development has led to the search for new processing methods to improve profitability by reducing operating costs or improving process productivity. This work proposes a novel multifeed bioreactor system composed of independently controlled feeds for substrate(s) and media used that allows for the free manipulation of the bioreactor supply rate and substrate concentrations to maximize bioreactor productivity and substrate utilization while reducing operating costs. The optimal operation of the multiple feeds is determined a priori as the solution of a dynamic optimization problem using the kinetic models describing the time-variant bioreactor concentrations as constraints. This new bioreactor paradigm is exemplified through the intracellular production of beta-carotene using a three feed bioreactor consisting of separate glucose, ethanol and media feeds. The performance of a traditional bioreator with a single substrate feed is compared to that of a bioreactor with multiple feeds using glucose and/or ethanol as substrate options. Results show up to a 30% reduction in the productivity with the addition of multiple feeds, though all three systems show an improvement in productivity when compared to batch production. Additionally, the breakeven selling price of beta-carotene is shown to decrease by at least 30% for the multifeed bioreactor when compared to the single feed counterpart, demonstrating the ability of the multifeed reactor to reduce operating costs in bioreactor systems. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:902-912, 2017.
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Affiliation(s)
- Jonathan P Raftery
- Artie McFerrin Dept. of Chemical Engineering, Texas A&M University, College Station, TX, 77840
| | - Melanie R DeSessa
- Artie McFerrin Dept. of Chemical Engineering, Texas A&M University, College Station, TX, 77840
| | - M Nazmul Karim
- Artie McFerrin Dept. of Chemical Engineering, Texas A&M University, College Station, TX, 77840
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