Studies On Hybrid Soft Sensor Modeling And Simulation For Wastewater Treatment Process

This paper presents the development of a simplified principle model (SPM) based on ASM1 and ASM1-CN. Compared with other models, this model developed consists of more simplified configuration and has few parameters with better applications. In addition, the air fan is the key factor to reduce the operation costs. The fan control process is introduced in this model. We can control the wind speed by SPM according to the concentration of oxygen in order to reduce the cost.This model is a result of amelioration based on complex model, so some parameters could be the same as former model, but the value is not exact enough, we need to optimize the parameters by using genetic arithmetic (GA).Wastewater treatment is a complex process, including lots of unknown factors, such as field condition, weather and inputs, and so on. The errors exist if we simulate the process using SPM. We can use neural network for error emendation because of its nonlinear simulation ability, building a hybrid soft sensor model.The following steps are for hybrid model:a. Simulate real process by using the principle model and get the error between the predicted and actual values.b. Choose a suitable neural network and determine the node number of every layer.c. Confirm the inputs of neural network, and the output is the error we got from the step a, then training the neural network, we can get the hybrid model consisting of both principle model and neural network.. d. For the new input data, we can get the predicted output value by the hybrid model.The simulation results show the proposed hybrid model based soft sensor may provide good results and applicability, and may reduce operation costs and increase throughput through precisely predicting the desired processing time.