A Review of Computational Modeling in Wastewater Treatment Processes

Abstract

Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models

“…A recent review paper highlighted the explosive growth in the number of publications related to machine learning in environmental science and engineering, with around 50% being in the water sector [11]. Moreover, the number of research publications on AI application to wastewater treatment was 19 times greater in 2019 than in 1995, and papers had 36 more citations on average [12].…”

“…ANNs are one of the models most applied in the simulation and prediction of the performance of biological treatment in WWTPs [11]. The models are composed of several artificial neurons, connected by links of variable weight, to form black box representations of pseudo neurological systems.…”

“…The models are composed of several artificial neurons, connected by links of variable weight, to form black box representations of pseudo neurological systems. The most common machine learning models used in the simulation, prediction, evaluation, and diagnosis of wastewater treatment operations are ANNs, Fuzzy Logic (FL), genetic algorithms (GA), and neural-fuzzy (NF), as well as artificial neural networks-genetic algorithm (ANN-GA) as hybrid models [11]. Table 1 shows the application of the AI model for operation management in WWTPs [13][14][15][16][17][18].…”

Prediction of COD in Industrial Wastewater Treatment Plant using an Artificial Neural Network

“…ANNs have been increasingly applied in various environmental modeling studies 5,6 and investigations into water quality issues 7,8 . In the domain of wastewater treatment plant (WWTP) modeling, ANNs have been successfully employed for the prediction of WWTP parameters [9][10][11] , process control [12][13][14][15] , and the estimation of output parameters and characteristics 16,17 . However, many of these studies necessitate diverse input data, contributing to the costliness and time-consuming nature of the modeling process.…”

Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

“…The special issue includes several review articles encompassing a wide spectrum, ranging from a historical perspective of water data to computational modeling in wastewater treatment to ML modeling of environmental chemical reactions, environmental toxicology, heavy metal removal, and cyanobacterial harmful algal blooms (HABs) . One significant application of these innovative tools is ML-assisted environmental monitoring, which can address diverse problems, such as predicting effluent nutrients or influent flow rates and nutrient loads at wastewater treatment plants, , formation of disinfection byproducts, drivers of the accumulation of potentially toxic elements in sediments, greenhouse gas emissions, , occurrence of PFAS, water quality assessment, microplastics, microcystins, and differentiation of landfill leachate and domestic sludge .…”