1
|
Wang R, He Z, Chen H, Guo S, Zhang S, Wang K, Wang M, Ho SH. Enhancing biomass conversion to bioenergy with machine learning: Gains and problems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172310. [PMID: 38599406 DOI: 10.1016/j.scitotenv.2024.172310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/20/2024] [Accepted: 04/06/2024] [Indexed: 04/12/2024]
Abstract
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.
Collapse
Affiliation(s)
- Rupeng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Zixiang He
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Honglin Chen
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Silin Guo
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shiyu Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Ke Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Meng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shih-Hsin Ho
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
| |
Collapse
|
2
|
Mahanty B. Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges. Biotechnol Bioeng 2023; 120:2072-2091. [PMID: 37458311 DOI: 10.1002/bit.28503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/09/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023]
Abstract
Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.
Collapse
Affiliation(s)
- Biswanath Mahanty
- Department of Biotechnology, Krunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Yang CT, Kristiani E, Leong YK, Chang JS. Big data and machine learning driven bioprocessing - Recent trends and critical analysis. BIORESOURCE TECHNOLOGY 2023; 372:128625. [PMID: 36642201 DOI: 10.1016/j.biortech.2023.128625] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.
Collapse
Affiliation(s)
- Chao-Tung Yang
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan
| | - Endah Kristiani
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia
| | - Yoong Kit Leong
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan
| | - Jo-Shu Chang
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan 701, Taiwan.
| |
Collapse
|
5
|
Investigating ‘greyness’ of hybrid model for bioprocess predictive modelling. Biochem Eng J 2023. [DOI: 10.1016/j.bej.2022.108761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
6
|
Mowbray MR, Wu C, Rogers AW, Rio-Chanona EAD, Zhang D. A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification. Biotechnol Bioeng 2023; 120:154-168. [PMID: 36225098 PMCID: PMC10092184 DOI: 10.1002/bit.28262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 07/18/2022] [Accepted: 10/09/2022] [Indexed: 11/09/2022]
Abstract
Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e., parameters varying over time) and history-dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a longstanding research challenge. Current advances in hybrid modeling offer a solution to this by integrating kinetic models with data-driven techniques. This article proposes a novel two-step framework: first (i) speculate and combine several possible kinetic model structures sourced from process and phenomenological knowledge, then (ii) identify the most likely kinetic model structure and its parameter values using model-free Reinforcement Learning (RL). Specifically, Step 1 collates feasible history-dependent model structures, then Step 2 uses RL to simultaneously identify the correct model structure and the time-varying parameter trajectories. To demonstrate the performance of this framework, a range of in-silico case studies were carried out. The results show that the proposed framework can efficiently construct high-fidelity models to quantify both time-varying and history-dependent kinetic behaviors while minimizing the risks of over-parametrization and over-fitting. Finally, the primary advantages of the proposed framework and its limitation were thoroughly discussed in comparison to other existing hybrid modeling and model structure identification techniques, highlighting the potential of this framework for general bioprocess modeling.
Collapse
Affiliation(s)
- Max R Mowbray
- Department of Chemical Engineering, Centre for Process Integration, University of Manchester, Manchester, UK
| | - Chufan Wu
- Department of Chemical Engineering, Centre for Process Integration, University of Manchester, Manchester, UK
| | - Alexander W Rogers
- Department of Chemical Engineering, Centre for Process Integration, University of Manchester, Manchester, UK
| | | | - Dongda Zhang
- Department of Chemical Engineering, Centre for Process Integration, University of Manchester, Manchester, UK
| |
Collapse
|
7
|
Modeling of Bioprocesses via MINLP-based Symbolic Regression of S-system Formalisms. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108108] [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]
|
8
|
Dai Z, Zhang Z, Zhu L, Zhu Z, Jiang L. Complete Genome Sequencing Analysis of Deinococcus wulumuqiensis R12, an Extremely Radiation-Resistant Strain. Curr Microbiol 2022; 79:292. [PMID: 35972568 DOI: 10.1007/s00284-022-02984-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 07/20/2022] [Indexed: 11/03/2022]
Abstract
Genome sequencing was performed by the PacBio RS II platform and Illumina HiSeq 4000 platform to discover the metabolic profile of the Deinococcus wulumuqiensis R12, which was isolated from radiation-contaminated soils in Xinjiang Uygur Autonomous Region of northwest China. The genome of 3.5 Mbp comprises one circular chromosome and four circular plasmids with 3679 genes and a GC content of 66.97%. A total of 41 new transcriptional factors were identified using the DeepTFactor tool. Genomic analysis revealed the presence of genes for homologous recombination repair, which suggested high recombination efficiency in R12. Three Type I and one Type II RM systems, two CRISPR arrays, and one Cas-Type IC protein were found, allowing the development of endogenous CRISPR-Cas gene-editing tools. Additionally, we found that R12 has a broad spectrum of substrate utilization, which was validated by physiological experiments. Genes involved in the carotenoid biosynthesis pathway and the antioxidative system were also identified. Overall, the comprehensive description of the genome of R12 will facilitate the additional exploitation of this strain as a versatile cell factory for biotechnological applications.
Collapse
Affiliation(s)
- Zijie Dai
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Zhidong Zhang
- Xinjiang Key Laboratory of Special Environmental Microbiology, Institute of Applied Microbiology, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, Xinjiang, China
| | - Liying Zhu
- College of Chemical and Molecular Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Zhengming Zhu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, China. .,College of Food Science and Light Industry, Nanjing Tech University, Nanjing, 211816, China.
| | - Ling Jiang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, China. .,College of Food Science and Light Industry, Nanjing Tech University, Nanjing, 211816, China.
| |
Collapse
|
9
|
Wang Z, Peng X, Xia A, Shah AA, Huang Y, Zhu X, Zhu X, Liao Q. The role of machine learning to boost the bioenergy and biofuels conversion. BIORESOURCE TECHNOLOGY 2022; 343:126099. [PMID: 34626766 DOI: 10.1016/j.biortech.2021.126099] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 06/13/2023]
Abstract
The development and application of bioenergy and biofuels conversion technology can play a significant role for the production of renewable and sustainable energy sources in the future. However, the complexity of bioenergy systems and the limitations of human understanding make it difficult to build models based on experience or theory for accurate predictions. Recent developments in data science and machine learning (ML), can provide new opportunities. Accordingly, this critical review provides a deep insight into the application of ML in the bioenergy context. The latest advances in ML assisted bioenergy technology, including energy utilization of lignocellulosic biomass, microalgae cultivation, biofuels conversion and application, are reviewed in detail. The strengths and limitations of ML in bioenergy systems are comprehensively analysed. Moreover, we highlight the capabilities and potential of advanced ML methods when encountering multifarious tasks in the future prospects to advance a new generation of bioenergy and biofuels conversion technologies.
Collapse
Affiliation(s)
- Zhengxin Wang
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Xinggan Peng
- School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
| | - Ao Xia
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China.
| | - Akeel A Shah
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Yun Huang
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Xianqing Zhu
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Xun Zhu
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| | - Qiang Liao
- Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 400044, PR China; Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400044, PR China
| |
Collapse
|
10
|
Rogers AW, Vega-Ramon F, Yan J, Del Río-Chanona EA, Jing K, Zhang D. A transfer learning approach for predictive modeling of bioprocesses using small data. Biotechnol Bioeng 2021; 119:411-422. [PMID: 34716712 DOI: 10.1002/bit.27980] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 10/28/2021] [Indexed: 11/06/2022]
Abstract
Predictive modeling of new biochemical systems with small data is a great challenge. To fill this gap, transfer learning, a subdomain of machine learning that serves to transfer knowledge from a generalized model to a more domain-specific model, provides a promising solution. While transfer learning has been used in natural language processing, image analysis, and chemical engineering fault detection, its application within biochemical engineering has not been systematically explored. In this study, we demonstrated the benefits of transfer learning when applied to predict dynamic behaviors of new biochemical processes. Two different case studies were presented to investigate the accuracy, reliability, and advantage of this innovative modeling approach. We thoroughly discussed the different transfer learning strategies and the effects of topology on transfer learning, comparing the performance of the transfer learning models against benchmark kinetic and data-driven models. Furthermore, strong connections between the underlying process mechanism and the transfer learning model's optimal structure were highlighted, suggesting the interpretability of transfer learning to enable more accurate prediction than a naive data-driven modeling approach. Therefore, this study shows a novel approach to effectively combining data from different resources for bioprocess simulation.
Collapse
Affiliation(s)
- Alexander W Rogers
- Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
| | - Fernando Vega-Ramon
- Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
| | - Jiangtao Yan
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China
| | | | - Keju Jing
- Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
| | - Dongda Zhang
- Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
| |
Collapse
|
11
|
Vega-Ramon F, Zhu X, Savage TR, Petsagkourakis P, Jing K, Zhang D. Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty. Biotechnol Bioeng 2021; 118:4854-4866. [PMID: 34612511 DOI: 10.1002/bit.27950] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 09/29/2021] [Accepted: 09/30/2021] [Indexed: 11/11/2022]
Abstract
Astaxanthin is a high-value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. However, little effort has been made to investigate the effects of multiple sugars on X. dendrorhous biomass growth and astaxanthin production. Furthermore, the construction of a high-fidelity model is challenging due to the system's variability, also known as batch-to-batch variation. Two innovations are proposed in this study to address these challenges. First, a kinetic model was developed to compare process kinetics between the single sugar (glucose) based and the mixed sugar (glucose and sucrose) based fermentation methods. Then, the kinetic model parameters were modeled themselves as Gaussian processes, a probabilistic machine learning technique, to improve the accuracy and robustness of model predictions. We conclude that although the presence of sucrose does not affect the biomass growth kinetics, it introduces a competitive inhibitory mechanism that enhances astaxanthin accumulation by inducing adverse environmental conditions such as osmotic gradients. Moreover, the hybrid model was able to greatly reduce model simulation error and was particularly robust to uncertainty propagation. This study suggests the advantage of mixed sugar-based fermentation and provides a novel approach for bioprocess dynamic modeling.
Collapse
Affiliation(s)
- Fernando Vega-Ramon
- Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
| | - Xianfeng Zhu
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China
| | - Thomas R Savage
- Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
| | | | - Keju Jing
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China
| | - Dongda Zhang
- Department of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
| |
Collapse
|
12
|
Mowbray M, Savage T, Wu C, Song Z, Cho BA, Del Rio-Chanona EA, Zhang D. Machine learning for biochemical engineering: A review. Biochem Eng J 2021. [DOI: 10.1016/j.bej.2021.108054] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|