Optimization of hydrolysis of cassava starch for biofuel production using a Hybrid Metaheuristic Algorithm

Biofuels are a current alternative to promote cleaner energy, minimizing environmental impacts of transport worldwide. One of the sources of these biofuels is cassava starch, which can provide fuel ethanol in the same way as sugarcane. The present study aimed to compare the development of the SAGAC algorithm and other techniques to optimize the cassava starch hydrolysis process. Experimental attempts were made to improve the cassava starch hydrolysis method with the following algorithms: Tabu Search (TS), Simulated Annealing (SA), Genetic Algorithm (GA), and Hybrid Metaheuristic Algorithms (SAG and SAGAC). The algorithms were calibrated and parameterized to generate the values of the variables within their respective domains as follows concentration (6.590 to 23.375 g/L), temperature (28.18 to 61.82 °C), and time (9 to 111 min). Results presented the best conditions for starch hydrolysis at 23.375 g/L, 61.9 °C, and 111 min, where a yield of 84.04% was achieved. Results indicated that the Hybrid Metaheuristic optimization technique SAGAC is more efficient than other optimization techniques and could improve industrial ethanol production from cassava.