Optimizing Fermentation Parameters for Bioethanol Production from Areca Nut Leaves using Artificial Neural Networks and Response Surface Methodologies (Asian Journal of Pharmaceutics (AJP), 2024)
The research covered the entire process, from collecting the Areca nut leaves to purifying the produced bioethanol.Materials and Methods: The Areca nut leaves were pre-treated with sulfuric acid and sodium hydroxide, followedby enzymatic hydrolysis using cellulose enzymes. The hydrolyzed biomass was then fermented by Saccharomycescerevisiae for 12–72 h to produce bioethanol. The produced bioethanol was purified through distillation using a rotaryflask evaporator. To optimize the fermentation process and bioethanol production, the researchers employed twomodeling approaches: Artificial neural networks (ANN) and response surface methodology (RSM). Variables suchas pH, fermentation time, and disodium hydrogen phosphate (Na2HPO4) concentration, identified from the Plackett-Burman design, were optimized using the central composite design of RSM. Results and Discussion: The R² valuefor the RSM model was 91.72%, and the adjusted R² was 84.72%. In addition, an ANN algorithm model with 3input neurons, 10 hidden layer neurons, and 1 output neuron was developed to investigate the relationship betweenbioethanol production and fermentation parameters. The ANN model achieved an R² of 99.78%, indicating higheraccuracy and reliability compared to the RSM approach. The optimal conditions for bioethanol production wereidentified as pH 5.5, 60 h fermentation time, and 0.45 g of Na2HPO4. Under these conditions, the experimentalbioethanol concentration reached 36.54 g/L. Conclusion: This study demonstrates the effective utilization of Arecanut leaves, a readily available agricultural waste, to produce bioethanol. The combination of statistical and machinelearning techniques, such as ANN and RSM, allowed for the optimization of the fermentation process and theenhancement of bioethanol yield, showcasing the potential of this approach for sustainable biofuel production.