Prediction of Dissolved Oxygen and Nitrate Concentration in Activated Sludge Wastewater Treatment using Artificial Neural Network

Maimun, Binti Huja Husin and M. F., Bin Rahmat and N. A., Wahab (2016) Prediction of Dissolved Oxygen and Nitrate Concentration in Activated Sludge Wastewater Treatment using Artificial Neural Network. In: International UNIMAS STEM Engineering Conference 2018, 12 - 14 September 2018, Riverside Majestic, Kuching, Sarawak.

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Abstract

Plays a significant role in returning safe and clean water back to its source, wastewater treatment plant (WWTP) need to operate efficiently despite challenges in energy consumption and stringent effluent standards set by the authority. Modeling the activated sludge process (ASP) of WWTP is essential for better understanding of the system, safety, dynamic prediction, control, and optimization of the plant The mechanistic model is too complex causing it difficult to be applied directly to controller design making a data-driven model that is known for its simplicity, and high prediction accuracy is a desirable choice. The aim of this study is to determine a reliable data-driven model for a WWTP by identification of the relevant input variables for the prediction of dissolved oxygen concentration and nitrate concentration in the neural network model. This is essential because the ASP WWTP contains large variations of parameters and is highly nonlinear. In this study, the important parameters for both dissolved oxygen and nitrate have been successfully identified. The simulation results using the proposed input combinations show that the neural network model able to predict both controlled variables closely. Additionally, the selected combination is with the lowest mean-square error and highest regression percentage.

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