Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process
[Display omitted] •Machine learning models developed for H2 production by dark fermentation.•GBM, SVR, RF and AdaBoost methods were robust models for the process.•All four models demonstrated close agreement with results (R2 ≥ 90%).•PVI was able to assess the relative importance of the inputs.•Aceta...
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| Published in | Bioresource technology Vol. 343; p. 126111 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2022
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| Subjects | |
| Online Access | Get full text |
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| Abstract | [Display omitted]
•Machine learning models developed for H2 production by dark fermentation.•GBM, SVR, RF and AdaBoost methods were robust models for the process.•All four models demonstrated close agreement with results (R2 ≥ 90%).•PVI was able to assess the relative importance of the inputs.•Acetate, butyrate, ethanol, Fe and Ni showed high importance in decreasing order.
Dark fermentation process for simultaneous wastewater treatment and H2 production is gaining attention. This study aimed to use machine learning (ML) procedures to model and analyze H2 production from wastewater during dark fermentation. Different ML procedures were assessed based on the mean squared error (MSE) and determination coefficient (R2) to select the most robust models for modeling the process. The research showed that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF) and AdaBoost were the most appropriate models, which were optimized by grid search and deeply analyzed by permutation variable importance (PVI) to identify the relative importance of process variables. All four models demonstrated promising performances in predicting H2 production with high R2 values (0.893, 0.885, 0.902 and 0.889) and small MSE values (0.015, 0.015, 0.016 and 0.015). Moreover, RF-PVI demonstrated that acetate, butyrate, acetate/butyrate, ethanol, Fe and Ni were of high importance in decreasing order. |
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| AbstractList | Dark fermentation process for simultaneous wastewater treatment and H₂ production is gaining attention. This study aimed to use machine learning (ML) procedures to model and analyze H₂ production from wastewater during dark fermentation. Different ML procedures were assessed based on the mean squared error (MSE) and determination coefficient (R²) to select the most robust models for modeling the process. The research showed that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF) and AdaBoost were the most appropriate models, which were optimized by grid search and deeply analyzed by permutation variable importance (PVI) to identify the relative importance of process variables. All four models demonstrated promising performances in predicting H₂ production with high R² values (0.893, 0.885, 0.902 and 0.889) and small MSE values (0.015, 0.015, 0.016 and 0.015). Moreover, RF-PVI demonstrated that acetate, butyrate, acetate/butyrate, ethanol, Fe and Ni were of high importance in decreasing order. Dark fermentation process for simultaneous wastewater treatment and H2 production is gaining attention. This study aimed to use machine learning (ML) procedures to model and analyze H2 production from wastewater during dark fermentation. Different ML procedures were assessed based on the mean squared error (MSE) and determination coefficient (R2) to select the most robust models for modeling the process. The research showed that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF) and AdaBoost were the most appropriate models, which were optimized by grid search and deeply analyzed by permutation variable importance (PVI) to identify the relative importance of process variables. All four models demonstrated promising performances in predicting H2 production with high R2 values (0.893, 0.885, 0.902 and 0.889) and small MSE values (0.015, 0.015, 0.016 and 0.015). Moreover, RF-PVI demonstrated that acetate, butyrate, acetate/butyrate, ethanol, Fe and Ni were of high importance in decreasing order.Dark fermentation process for simultaneous wastewater treatment and H2 production is gaining attention. This study aimed to use machine learning (ML) procedures to model and analyze H2 production from wastewater during dark fermentation. Different ML procedures were assessed based on the mean squared error (MSE) and determination coefficient (R2) to select the most robust models for modeling the process. The research showed that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF) and AdaBoost were the most appropriate models, which were optimized by grid search and deeply analyzed by permutation variable importance (PVI) to identify the relative importance of process variables. All four models demonstrated promising performances in predicting H2 production with high R2 values (0.893, 0.885, 0.902 and 0.889) and small MSE values (0.015, 0.015, 0.016 and 0.015). Moreover, RF-PVI demonstrated that acetate, butyrate, acetate/butyrate, ethanol, Fe and Ni were of high importance in decreasing order. [Display omitted] •Machine learning models developed for H2 production by dark fermentation.•GBM, SVR, RF and AdaBoost methods were robust models for the process.•All four models demonstrated close agreement with results (R2 ≥ 90%).•PVI was able to assess the relative importance of the inputs.•Acetate, butyrate, ethanol, Fe and Ni showed high importance in decreasing order. Dark fermentation process for simultaneous wastewater treatment and H2 production is gaining attention. This study aimed to use machine learning (ML) procedures to model and analyze H2 production from wastewater during dark fermentation. Different ML procedures were assessed based on the mean squared error (MSE) and determination coefficient (R2) to select the most robust models for modeling the process. The research showed that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF) and AdaBoost were the most appropriate models, which were optimized by grid search and deeply analyzed by permutation variable importance (PVI) to identify the relative importance of process variables. All four models demonstrated promising performances in predicting H2 production with high R2 values (0.893, 0.885, 0.902 and 0.889) and small MSE values (0.015, 0.015, 0.016 and 0.015). Moreover, RF-PVI demonstrated that acetate, butyrate, acetate/butyrate, ethanol, Fe and Ni were of high importance in decreasing order. |
| ArticleNumber | 126111 |
| Author | Altaee, Ali Hosseinzadeh, Ahmad Li, Donghao Zhou, John L. |
| Author_xml | – sequence: 1 givenname: Ahmad surname: Hosseinzadeh fullname: Hosseinzadeh, Ahmad organization: Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia – sequence: 2 givenname: John L. surname: Zhou fullname: Zhou, John L. email: junliang.zhou@uts.edu.au organization: Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia – sequence: 3 givenname: Ali surname: Altaee fullname: Altaee, Ali organization: Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia – sequence: 4 givenname: Donghao surname: Li fullname: Li, Donghao organization: Department of Chemistry, Yanbian University, Park Road 977, Yanji 133002, Jilin Province, China |
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•Machine learning models developed for H2 production by dark fermentation.•GBM, SVR, RF and AdaBoost methods were robust models for the... Dark fermentation process for simultaneous wastewater treatment and H2 production is gaining attention. This study aimed to use machine learning (ML)... Dark fermentation process for simultaneous wastewater treatment and H₂ production is gaining attention. This study aimed to use machine learning (ML)... |
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| SubjectTerms | acetates Biohydrogen butyrates Dark fermentation ethanol fermentation hydrogen production Machine learning Process modelling support vector machines wastewater Wastewater treatment |
| Title | Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process |
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