The present study demonstrates a comparative analysis between the artificial neural network (ANN) and response surface methodology (RSM) as optimization tools for pretreatment and enzymatic hydrolysis of lignocellulosic rice straw. The efficacy for both the processes, that is, pretreatment and enzymatic hydrolysis was evaluated using correlation coefficient (R²) & mean squared error (MSE). The values of R² obtained by ANN after training, validation, and testing were 1, 0.9005, and 0.997 for pretreatment and 0.962, 0.923, and 0.9941 for enzymatic saccharification, respectively. On the other hand, the R² values obtained with RSM were 0.9965 for cellulose recovery and 0.9994 for saccharification efficiency. Thus, ANN and RSM together successfully identify the substantial process conditions for rice straw pretreatment and enzymatic saccharification. The percentage of error for ANN and RSM were 0.009 and 0.01 for cellulose recovery and for 0.004 and 0.005 for saccharification efficiency, respectively, which showed the authority of ANN in exemplifying the non-linear behavior of the system.

本研究证明了人工神经网络（ANN）和响应面方法（RSM）作为木质纤维素纤维素稻草的预处理和酶水解的优化工具的对比分析。使用相关系数（R ²）和均方误差（MSE）评估了两种方法（即预处理和酶水解）的功效。经过ANN训练，验证和测试后得到的R ²值，预处理的分别为1,0.9005和0.997，酶促糖化的分别为0.962、0.923和0.9941。另一方面，R ²用RSM获得的纤维素回收率为0.9965，糖化效率为0.9994。因此，ANN和RSM一起成功地确定了稻草预处理和酶促糖化的主要工艺条件。纤维素回收率的ANN和RSM误差百分比分别为0.009和0.01，糖化效率的误差百分比分别为0.004和0.005，这表明ANN在例示系统的非线性行为方面具有权威性。