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Soft-Sensing Method of Effluent Ammonia Nitrogen Based on Emotional Neural Network

In the process of wastewater treatment process (WWTP), the concentration of effluent ammonia nitrogen is one of the important indexes to test whether the water quality reaches the standard. Soft sensing technology is a method which can use some easily measurable auxiliary variables to predict key variables online. In this paper, we designed a soft sensing model of effluent NH4-N based on radial basis emotional neural network (RBENN), and then a feature selection (FS) method of sticky binary particle swarm optimization (SBPSO) algorithm based on mutual information (MI) and symmetric uncertainty (SU) is proposed to obtain the appropriate feature variables while balancing correlation and redundancy, and then the network parameters are optimized by comprehensive learning particle swarm optimization (CLPSO) algorithm. Simulation results show that the proposed method effectively improves the prediction performance of the model.