State estimation of wastewater treatment plants based on model approximation

Deep learning in wastewater treatment: a critical review

Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms, a high degree of disturbance variability and non-linear (generally stable) behavior with multiple internal recycle loops. Semi-mechanistic biochemical models currently dominate research and application, with data-driven deep learning models emerging as an alternative and supplementary approach. But these modeling approaches have grown in separate communities of research and practice, and so there is limited appreciation of the strengths, weaknesses, contrasts and similarities between the methods. This review addresses that gap by providing a detailed guide to deep learning methods and their application to wastewater process modeling. The review is aimed at wastewater modeling experts who are familiar with established mechanistic modeling approach, and are curious about the opportunities and challenges afforded by deep learning methods. We conclude with a discussion and needs analysis on the value of different ways of modeling wastewater processes and open research problems.

Plant-wide modeling of a metropolitan wastewater treatment plant to reduce energy consumption and carbon footprint

A real metropolitan wastewater treatment plant (RWWTP) serving a population equivalent of 1.55 million was modeled to reduce energy consumption and carbon footprint (CFP). An approach was proposed to handle the dilution factor and partial aeration due to discontinuous air diffuser locations in the Bardenpho-5 configuration. Various operational, structural, and configurational modifications were evaluated. Results indicated that management scenarios might provide conflicting outcomes for different targets. Reduced energy consumption may not result in lower CFP at the same time. Moreover, operational changes that would impact total nitrogen (TN) concentrations and N₂O release may significantly impact CFP. A policy of using a modified Bardenpho-5 process with reduced internal recycle (IR) ratio, waste activated sludge (WAS), and return activated sludge (RAS) flow rates provided the lowest CPF. Modified Bardenpho-5 process and replacing belt thickeners with gravity thickeners supplied the highest savings in energy consumption. Overall, up to 14% and 20% reductions were possible in the energy consumption and CFP of the plant, respectively. The RWWTP may save up to 10% in energy expenses annually by operational modifications.

Economic MPC of Wastewater Treatment Plants Based on Model Reduction

In this paper, we consider the problem of economic model predictive control of wastewater treatment plants based on model reduction. We apply two model approximation methods to a wastewater treatment plant (WWTP) described by a modified Benchmark Simulation Model No.1 to overcome the intensive computation associated with economic model predictive control (MPC). Two computationally efficient models are obtained based on trajectory piecewise linearization (TPWL) and reduced order TPWL. To obtain the reduced order TPWL model, a proper orthogonal decomposition (POD)-based method is utilized. Further, the reduced order model is linearized to obtain a TPWL-POD model. The objective is to design controllers which minimize the overall economic cost. Accordingly, we design economic MPC (EMPC) controllers based on each of the models. The economic control cost can be described as a weighted summation of effluent quality and overall operating cost. We compare the accuracy of the two proposed approximation models with different linearization point numbers. We evaluate the average