Data Authorization and Forecasting by a Proactive Soft Sensing Tool-Anammox Based Process

Precise control of biological wastewater treatment for nitrogen removal is difficult because of the nonlinearity, time-varying, and time-consuming nature of the process. With due emphasis on addressing the challenges involved in its effective implementation, this study developed an artificial neural network (ANN) based soft sensor (SS) with a set of proposed thumb rules for online forecasting of the concentrations of hard-to-measure parameters (NH4+ and NO2-) from secondary easy-to-measure variables, (reactor volume, dissolved oxygen, suspended solids, pH, temperature, and ORP) in an Anammox based pilot plant. Four hybrid neural networks (PCA-Kalman NN, PCA NN, Kalman NN, and Non NN) were applied to identify net optimum input vectors for the SS, using an appropriate quantity of samples from the set of secondary variables. The proposed hybrid SS was tested on a sewage wastewater treatment plant operated using a Matlab R2018a framework and validated using operational plant data. The results showed that the PCA-Kalman neural network with R-2 values of 0.9985 and 0.9263 for NH4+ and NO2-, respectively, is potentially a valuable tool for plant operators in the selection of operational states to minimize cost and to efficiently predict important parameters that are prone to errors due to a failure in online sensors.