Optimization of Enzymatic Hydrolysis of Soybean Dregs Based on Genetic Algorithm-Neural Network and Its Kinetics

Objective: The optimal process conditions for the solution of legumes by cellulase enzymes were obtained, and a model of the electrodynamics of bean slag enzymes was established. Methods: Based on the single-factor experiment, the effects of pH, cellulase addition, enzymatic temperature and enzyme solution time on reduced sugar production were studied by using the response surface method (RSM) and the genetic algorithm-neural network model (GA-ANN). Results: Genetic algorithm-neural network optimization of the bean slag enzyme solution process conditions were: pH5.2, cellulase addition 4.4%, enzymatic temperature 52 ℃, enzymatic time 3.2 h, under these conditions reduced sugar production was 2.65 g/kg, higher than the results of response surface optimization (reduced sugar production was 2.54 g/kg). The kinetics parameter k₀ was 0.537 and the scropular dimensionality h was 0.153, according to the experimental data and the dynamic model, the fit was 0.9827, and the fitting effect was good. Conclusion: In this paper, the soybean slag enzyme solution process was optimized by using the response surface method and the genetic algorithm-neural network model, and the soybean slag enzyme solution dynamics model was established, which could provide theoretical reference for the soybean slag enzyme solution process.