A Review of Computational Modeling in Wastewater Treatment Processes

How small is big enough? Big data-driven machine learning predictions for a full-scale wastewater treatment plant

Wastewater treatment plants (WWTPs) generate vast amounts of water quality, operational, and biological data. The potential of these big data, particularly through machine learning (ML), to improve WWTP management is increasingly recognized. However, the costs associated with data collection and processing can rise sharply as datasets grow larger, and research on determining the optimal data volume for effective ML application remains limited. In this study, we comprehensively analyzed water quality, operational, and biological data collected from a full-scale WWTP over 970 days. Our results demonstrate that ML models can predict not only operational and water quality parameters (concentrations of dissolved oxygen and effluent chemical oxygen demand) but also the abundances of functional bacteria. Notably, we discovered that increasing data volume does not always improve model performance, and that data collection intervals do not need to be excessively small, as moderate intervals can still yield reliable predictions. These findings suggest that excessively large datasets may not be necessary for effective ML predictions in WWTPs. Overall, this study underscores the importance of optimizing dataset size to balance computation efficiency and prediction accuracy, providing valuable insights into data management strategies that can enhance the operational efficiency and sustainability of WWTPs.

Model-based scenario analysis to support the operation of solar photo-Fenton plants

Model-based tools applied to wastewater management have been identified as an emerging solution to address the associated challenges related to the optimization of the technologies, meeting more restricted water quality standards. Thus, for the first time, the demonstration of the solar photo-Fenton process for microcontaminant removal in the operating environment of a model-based tool is reported. This tool aids in determining the right cost-effective seasonal strategy for a 37-m2 demonstration-scale photoreactor operating in a rural wastewater treatment plant. It was developed using a model tuned adequately with experimental data obtained at lab scale and then validated in the solar photo-Fenton demonstration plant, proving its reliability, and enveloping a robust operation. Imidacloprid removal was the treatment target, and reagent concentrations were 0.1 mM for ferric nitrilotriacetate and 0.73 mM for hydrogen peroxide. According to the model-based tool, to attain the maximum treatment capacity, the best operating conditions were a liquid depth of 20-cm, and hydraulic residence time of 45 and 60-min in summer and winter, respectively, augmenting the treatment cost by 25% (0.49 €∙m-3 vs. 0.65 €∙m-3). This model-based tool allows the control and optimization of the technology to be improved, while promoting its attractiveness in the market.

Understanding machine learning predictions of wastewater treatment plant sludge with explainable artificial intelligence

This study investigates the use of machine learning (ML) models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence (XAI) techniques for understanding the impact of variables behind the prediction. Three ML models, random forest (RF), gradient boosting machine (GBM), and gradient boosting tree (GBT), were evaluated for their performance using statistical indicators. Input variable combinations were selected through different feature selection (FS) methods. XAI techniques were employed to enhance the interpretability and transparency of ML models. The results suggest that prediction accuracy depends on the choice of model and the number of variables. XAI techniques were found to be effective in interpreting the decisions made by each ML model. This study provides an example of using ML models in sludge production prediction and interpreting models applying XAI to understand the factors influencing it. Understandable interpretation of ML model prediction can facilitate targeted interventions for process optimization and improve the efficiency and sustainability of wastewater treatment processes. PRACTITIONER POINTS: Explainable artificial intelligence can play a crucial role in promoting trust between machine learning models and their real-world applications. Widely practiced machine learning models were used to predict sludge production of a United States wastewater treatment plant. Feature selection methods can reduce the required number of input variables without compromising model accuracy. Explainable artificial intelligence techniques can explain driving variables behind machine learning prediction.

Mechanistic and data-driven modeling of carbon respiration with bio-electrochemical sensors

Bioelectrochemical sensor (BES) technologies have been developed to measure soluble carbon concentrations in wastewater. However, architectures and analytical methods developed in controlled laboratory environments fail to predict BES behavior during field deployments at water resource recovery facilities (WRRFs). Here, we examine the possibilities and obstacles associated with integrating BESs into environmental sensing networks and machine learning algorithms to monitor the biodegradable carbon dynamics and microbial metabolism at WRRFs. This approach highlights the potential of BESs to provide real-time insights into full-scale biodegradable carbon consumption across WRRFs.

Prediction of COD in industrial wastewater treatment plant using an artificial neural network

In this investigation, the modeling of the Aksaray industrial wastewater treatment plant was performed using artificial neural networks with various architectures in the MATLAB software. The dataset utilized in this study was collected from the Aksaray wastewater treatment plant over a 9-month period through daily records. The treatment efficiency of the plants was assessed based on the output values of chemical oxygen demand (COD) output. Principal component analysis (PCA) was applied to furnish input for the Feedforward Backpropagation Artificial Neural Networks (FFBANN). The model's performance was evaluated using the Mean Squared Error (MSE), the Mean Absolute Error (MAE) and correlation coefficient (R2) parameters. The optimal architecture for the neural network model was determined through several trial and error iterations. According to the modeling results, the ANN exhibited a high predictive capability for plant performance, with an R2 reaching up to 0.9997 when comparing the observed and predicted output variables.

A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future

Given the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.