Modeling and optimization of acid hydrolysis for spirulina-based ethanol production by response surface methodology and neural network techniques.

The main aim of this study was to evaluate the optimum conditions for extracting the total reducing sugar content for bioethanol production using spirulina algae. The spirulina algae was pretreated using microwave-assisted acid hydrolysis, and the parameters were optimized using response surface methodology (RSM). The selected independent parameters were microwave power (250-350 W), sulfuric acid concentration (1-7%), and time duration (1-5 min). The results showed that a maximum reducing sugar concentration of 3.8 mg/mL was produced at optimum conditions. ANOVA and R-squared (R2) value (99.87%) show the model was significant (p value is < 0.0001). Additionally, a study on optimization and modeling was conducted utilizing response surface methodology (RSM) as well as artificial neural networks (ANN) to evaluate the impact of temperature (30-40 °C), concentration of inoculum (1-5 g/L), and fermentation duration (12-45 h). This comparative assessment showed that the highest ethanol concentration of 1.824 g/L was achieved under optimal conditions of 30 °C, 5 g/L inoculum concentration, and 28.5 h duration, as determined by the high-performance liquid chromatography method. Finally, it is suggested that the RSM approach demonstrated superior performance with a higher R2 value (97.42%), p value is < 0.0001 (significant), and a lower mean square error (MSE) of 0.0065 compared to the ANN model.