Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process.

Publisher:
Elsevier BV
Publication Type:
Journal Article
Citation:
Bioresource technology, 2021, pp. 126111
Issue Date:
2021-10-11
Filename Description Size
Machine learning modelling and analysis of biohydrogen production.pdfAccepted Manuscript Version1.02 MB
Adobe PDF
Full metadata record
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.
Please use this identifier to cite or link to this item: