Study on quality prediction of anaerobic fermentation bacteria in bio-enhancement system based on Attention Seq2Seq neural network
Bio-enhanced anaerobic fermentation system can improve fermentation efficiency and product quality.However,in the process of bio-enhanced methane anaerobic fermentation,key biological parameters are difficult to be measured online in real time.To solve this problem,this study proposes a quality prediction method based on attention fusion into the Seq2Seq-LSTM model.The method inputs the time series data through an encoder and introduces an attention mechanism to enhance the focus on important information to obtain the updated intermediate vectors.The attention mechanism is also introduced in the decoder,and the LSTM neural network is utilized to synthesize the intermediate vectors and input information at the current moment.Meanwhile,in order to improve the stability of the model,Adamw gradient descent optimizer is used for training.Finally,the method is applied to methane fermentation bacterial mass prediction together with LSTM and AM-LSTM models.The experimental results show that the model fitting ability and prediction accuracy are superior to the other two models,and can be better applied to the online prediction of methane fermentation bacterial mass.
bio-enhancementanaerobic fermentationquality predictionLSTMattention mechanismSeq2Seq model
NETL
NSTL
万方数据
