The invention relates to the field of
sewage treatment agent feeding, and discloses a CNN-BiGRU-based
sewage treatment agent feeding concentration control method, which comprises the following steps: S10, collecting data of
sewage treatment agent feeding concentration; s20, carrying out preprocessing on the collected data; s30, performing
feature extraction on the data by using a
convolutional neural network; s40, taking the features extracted by the
convolutional neural network as an input sequence, and inputting the input sequence into a bidirectional gating circulation unit for
sequence modeling; s50, according to the trained model, performing
drug delivery
concentration prediction on new input data; and S60, in combination with the data monitored in real time, a
feedback control strategy is adopted to adjust the prediction result, and the control strategy is updated in real time.
Sequence modeling is carried out by using the
convolutional neural network and the bidirectional gating circulation unit, so that the characteristics in the
sewage treatment process can be extracted more accurately, and the concentration of the put medicament can be predicted. Therefore, the agent feeding strategy can be optimized, and the
sewage treatment efficiency and quality can be improved.