Artificial Intelligence Application in Bioethanol Production

Abstract

Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a complex process of known challenges in the area of handling, optimizing, and future forecasting. The existence of modelling techniques like artificial intelligence (AI) is, therefore, necessary in the design, handling, and optimization of bioethanol production. The flexibility and high accuracy of artificial neural network (ANN), a machine learning technique, to solve intricate processes is beneficial in modelling pretreatment, fermentation, and conversion stages of a bioethanol production system. This paper reviews various AI techniques in bioethanol production giving emphasis on published articles in the past decade.

Comparative Analysis of Acidic and Alkaline Pretreatment Techniques for Bioethanol Production from Perennial Grasses

“…Blends currently incorporated include E5, E10, E15, and E85, denoting bioethanol and gasoline ratios of 5:95, 10:90, 15:85, and 85:15, respectively. The utilization of these blends does not necessitate any modifications to the engine; however, the E85 blend is exclusively compatible with flex-fuel engines [8][9][10][11]. Bioethanol is currently derived through the fermentation of first generation (1G) starch-based crops, including corn starch, sugarcane, and sugar beet, a practice that raises significant concerns about its impact on food security [12].…”