Machine learning-enabled microalgal bioprocesses for sustainable biofuel production: a comprehensive review

The pursuit of sustainable alternatives to fossil fuels has significantly accelerated as a result of the global energy crisis and increasing environmental concerns. Owing to their high lipid content, rapid growth rates, and ability to make use of non-arable land and non-potable water, microalgae have emerged as one of the most promising feedstocks for next-generation biofuels. The conventional optimization methods have not been effective at managing the intricate framework encompassing the cultivation of microalgae and the extraction procedure of biofuels. This review evaluates the transforming role of machine learning (ML)-based technologies in optimizing the production of biofuel from microalgae by exploiting the available datasets. Of the various ML approaches, the most effective models are artificial neural networks, support vector machines, and ensemble methods, along with some specific algorithms displaying an evident upper hand on varied targeted applications and microalgal species. The application of ML approaches in this field is likely to strengthen the biofuel production while alleviating depletion of the natural resources and nurturing the renewable resources of energy.