Sludge bulking prediction method for Sewage treatment plant based on GWO-CPSO algorithm

Various biological and abiotic factors influence the process of sludge bulking, showing a high degree of nonlinearity. Various inducing factors interact with each other and have strong coupling. In theory, the artificial neural network can approximate any nonlinear system with any precision, so it is determined to use a neural network algorithm for prediction. The Middle BP algorithm is the most common and effective method. This paper proposes a prediction method based on the BP neural network optimized by the grey wolf optimization algorithm and chaotic particle swarm optimization algorithm to predict the trend of sludge bulking, in which the grey wolf algorithm and messy particle swarm optimization method are used to optimize the neural network algorithm. This algorithm can solve the problems of the BP neural network in the early stage, such as convergence, easy falling into the local minimum value, too dependent on the early weight, and effectively improve the neural network's calculation speed and prediction accuracy.