Abstract：
The wastewater treatment efficiency of Diplosphaera mucosa VSPA was enhanced by optimising five input parameters and increasing the biomass yield. pH, temperature, light intensity, wastewater percentage (pollutant concentration), and N/P ratio were optimised, and their effects were studied. Two competitive techniques, response surface methodology (RSM) and artificial neural network (ANN), were applied for constructing predictive models using experimental data generated according to central composite design. Both MATLAB and Python were used for constructing ANN models. ANN models predicted the experimental data with high accuracy and less error than RSM models. Generated models were hybridised with a genetic algorithm (GA) to determine the optimised values of input parameters leading to high biomass productivity. ANN-GA hybridisation approach performed in Python presented optimisation results with less error (0.45%), which were 7.8 pH, 28.8 °C temperature, 105.20 μmol m-2 s-1 light intensity, 93.10 wastewater % (COD) and 23.5 N/P ratio.
摘要：
通过优化五个输入参数并提高生物量产量，提高了二倍体粘膜VSPA的废水处理效率。pH值,温度,光强度,废水百分比（污染物浓度），和N/P比进行了优化，并对其效果进行了研究。两种竞争技术，响应面法(RSM)和人工神经网络(ANN)，应用于使用根据中央复合设计生成的实验数据构建预测模型。MATLAB和Python都用于构建神经网络模型。与RSM模型相比，ANN模型对实验数据的预测精度较高，误差较小。将生成的模型与遗传算法（GA）混合，以确定导致高生物量生产率的输入参数的优化值。在Python中执行的ANN-GA混合方法给出了误差较小(0.45%)的优化结果，pH值为7.8，温度28.8°C,105.20μmolm-2s-1光强度，93.10废水%(COD)和23.5N/P比。
