A review of advanced optimization strategies for fermentative biohydrogen production processes. (1st May 2022)

• Record Type: Journal Article
• Authors: Mohd Asrul, Mohamad Afiq; Atan, Mohd Farid; Abdul Halim Yun, Hafizah; Lai, Josephine Chang Hui
• Is Part Of: International journal of hydrogen energy. Volume 47:Number 38(2022)
• Page Start: 16785
• Page End: 16804
• Publication Date: 2022-05-01
• Subjects: Biohydrogen -- Fermentation -- Artificial intelligence -- Predictive model -- Optimization; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2022.03.197
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: The inability of statistical optimization to represent the dynamic interaction of the biohydrogen process, which is highly non-linear and complicated, has been identified. However, incorporating a data-driven black-box model could overcome the limitations of conventional methods to provide correct responses rapidly and cost-effective modeling. Despite significant reports on the optimization of hydrogen production from fermentation, fewer studies have been made for the case using artificial intelligence algorithms. As a result, critical and extensive analyses of previous works are conducted to develop a general methodological framework for advanced response optimization. Graphical abstract: Image 1 Highlights: The bioreactor analysis of hydrogen fermentative is a highly non-linear and complex. Artificial intelligence algorithm for predicting and optimizing. A methodological framework from the past studies reviewed in chronology. Proposed guideline for the advanced modeling and optimization implementation. The feasibility of the model strategy for the macroscopic operational data.