Predicting Phosphorus Concentrations in WWTPs Using Data-Driven Methods

Abstract

Modelling wastewater treatment processes is the key to improve and optimize treatment performance, and the task has been the topic of various research for decades. However, the problem remains a major challenge in both academia and industry as the wastewater processes are highly nonlinear, coupled and time-varying dynamic systems containing both physical and biochemical reactions and large time delay features. As a result, the use of data-driven system identification has increased, introducing the artificial neural networks as predictive models for the processes. This study proposes several data-driven identification methods to predict the phosphorus concentration at a case plant. The wastewater treatment plant (WWTP) of interest is located in Agtrup, Denmark, and the plant uses a combination of chemical precipitation and biological phosphorus removal. In this study, both linear and nonlinear data-driven methods are investigated to obtain the best model for phosphorus concentration in wastewater. Dynamic mode decomposition with control is applied to obtain a linear model, however, the model shows poor generalizability, and is assessed inadequate to predict the inherently nonlinear process. To accurately model the nonlinearities in the system, two neural network structures are proposed; a NARX neural network and a long short-term memory network. Bayesian optimization is applied to optimize the model structure, and results shows that a LSTM structure with Bayesian optimized hyperparameters has the best prediction performance. The obtained models are compared based on several statistical measures, including temporal evaluations, ensuring that the model dynamics reflects the dynamics of the actual system. The best model is concluded to the a LSTM with 25 inputs, 2 hidden LSTM layers with 93 units in each and a output layer with a single unit. When validated on new data, the best model shows strong performance estimating the phosphorus concentration with a low MSE of 0:0848 and R2 = 0:42.

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A master thesis from Aalborg University

Predicting Phosphorus Concentrations in WWTPs Using Data-Driven Methods