Optimizing Fermentation Parameters for Bioethanol Production from Areca Nut Leaves using Artificial Neural Networks and Response Surface Methodologies

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, followed by enzymatic hydrolysis using cellulose enzymes. The hydrolyzed biomass was then fermented by Saccharomyces cerevisiae for 12–72 h to produce bioethanol. The produced bioethanol was purified through distillation using a rotary flask evaporator. To optimize the fermentation process and bioethanol production, the researchers employed two modeling approaches: Artificial neural networks (ANN) and response surface methodology (RSM). Variables such as 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 value for the RSM model was 91.72%, and the adjusted R² was 84.72%. In addition, an ANN algorithm model with 3 input neurons, 10 hidden layer neurons, and 1 output neuron was developed to investigate the relationship between bioethanol production and fermentation parameters. The ANN model achieved an R² of 99.78%, indicating higher accuracy and reliability compared to the RSM approach. The optimal conditions for bioethanol production were identified as pH 5.5, 60 h fermentation time, and 0.45 g of Na2HPO4. Under these conditions, the experimental bioethanol concentration reached 36.54 g/L. Conclusion: This study demonstrates the effective utilization of Areca nut leaves, a readily available agricultural waste, to produce bioethanol. The combination of statistical and machine learning techniques, such as ANN and RSM, allowed for the optimization of the fermentation process and the enhancement of bioethanol yield, showcasing the potential of this approach for sustainable biofuel production.