基于GA-BP神经网络的餐厨垃圾合成PHA工艺产量预测

为了预估混合底物碳源条件下活性污泥PHA合成产量预测的准确度,通过引入遗传算法对BP人工神经网络的权值和阈值进行优选,建立基于GA-BP神经网络的餐厨垃圾合成PHA工艺产量预测模型。以餐厨垃圾发酵液为底物碳源,利用活性污泥在ADD模式下进行PHA合成。以实验数据为基础训练神经网络模型,通过实测数据与模型预测数据之间的对比,验证了人工神经网络预测模型的精确度,并对长期PHA合成能力进行了预测。结论表明:基于遗传算法改进的GA-BP网络模型表现出比传统BP神经网络模型更佳的预测准确度,为评估混合菌群PHA最大合成产量的长期发展趋势,确定合理富集时长探索了可行方法。

PREDICTION OF POLYHYDROXYALKANOATE (PHA) PRODUCTION UTILIZING FOOD WASTE BASED ON GA-BP NEURAL NETWORK METHOD

To evaluate the activated sludge PHA synthesis yield prediction under the condition of mixed carbon sources, genetic algorithms were proposed to optimize the weights and thresholds of the BP artificial neural network, and the research established the prediction model based on the GA-BP network. Food waste fermentation liquid was applied as the substrate and activated sludge was used to synthesize PHA under the ADD mode. Based on experimental data, the comparison between the measured data and the model predictions was carried out, to verify the accuracy of the network prediction model and the prediction of the long-term PHA synthesis ability. Results showed that the GA-BP network model improved based on the genetic algorithm had high prediction accuracy than the traditional BP neural network model, and the model explored a feasible method to evaluate the long-term variation of the maximum PHA production yield in mixed microbial cultures and determined the reasonable enrichment time.