A Review Unveiling Various Machine Learning Algorithms Adopted for Biohydrogen Productions from Microalgae

Biohydrogen production from microalgae is a potential alternative energy source that now intensively being researched. The complex natures of the biological processes involved have afflicted accuracy traditional modelling and optimization, besides costly. Accordingly, machine learning algorithms been employed to overcome setbacks, as these approaches capability predict nonlinear interactions handle multivariate data microalgal biohydrogen studies. Thus, review focuses on revealing recent applications techniques in production. working principles random forests, artificial neural networks, support vector machines, regression are covered. analyzed compared for their effectiveness, advantages disadvantages relationship studies, classification results, prediction hydrogen These shown great performance despite limited sets nonlinear. However, current still susceptible overfitting, which could potentially reduce performance. be resolved or mitigated by comparing methods, should input limited.