Prediction of Oxidant Exposures and Micropollutant Abatement during Ozonation Using a Machine Learning Method

Application of machine learning algorithms for the prediction of metformin removal with hydroxyl radical-based photochemical oxidation and optimization of process parameters

This study investigated the effectiveness of hydroxyl radical-based photochemical oxidation processes on metformin (METF) removal, and the experimental data were modeled by machine learning (ML) algorithms. Hydrogen peroxide (HP), sodium percarbonate (PC), and peracetic acid (PAA) were used as hydroxyl radicals sources. Modeling was conducted using ML algorithms with the integration of additional experiments. Under optimum conditions (UV/PC: pH 5, PC 6 mM, UV/HP: pH 3, HP 6 mM, UV/PAA: pH 9, PAA 6 mM), the METF removal efficiency was 74.1 %, 40.7 %, and 47.9 % with UV/PC, UV/HP, and UV/PAA, respectively. The scavenging experiments revealed that hydroxyl and singlet oxygen radicals were dominant in UV/PC and hydroxyl radicals were predominant in UV/HP and UV/PAA. Nitrate negatively affected UV/HP, UV/PC, and UV/PAA, whereas chlorine had a positive impact. The EE/O were 0.682, 1.75, and 1.41 kWh/L for UV/PC, UV/HP, and UV/PAA, respectively. The experimental results were successfully modeled by ML models with high R2 values and low MAE and RMSE values. XGBoost models effectively represent data with generalization by avoiding overfitting. Using ML algorithms to model hydroxyl radical-based photochemical oxidation processes is considered an effective and practical method for future research.

Emerging applications of fluorescence excitation-emission matrix with machine learning for water quality monitoring: A systematic review

Fluorescence excitation-emission matrix (FEEM) spectroscopy is increasingly utilized in water quality monitoring due to its rapid, sensitive, and non-destructive measurement capabilities. The integration of machine learning (ML) techniques with FEEM offers a powerful approach to enhance data interpretation and improve monitoring efficiency. This review systematically examines the application of ML-FEEM in urban water systems across three primary tasks of ML: classification, regression, and pattern recognition. Contributed by the effectiveness of ML in nonlinear and high dimensional data analysis, ML-FEEM achieved superior accuracy and efficiency in pollutant qualification and quantification. The fluorescence features extracted through ML are more representative and hold potential for generating new FEEM samples. Additionally, the rich visualization capabilities of ML-FEEM facilitate the exploration of the migration and transformation of dissolved organic matter in water. This review underscores the importance of leveraging the latest ML advancements to uncover hidden information within FEEM data, and advocates for the use of pattern recognition methods, represented by self-organizing map, to further elucidate the behavior of pollutants in aquatic environments. Despite notable advancements, several issues require careful consideration, including the portable or online setups for FEEM collection, the standardized pretreatment processes for FEEM analysis, and the smart feedback of long-term FEEM governance.

Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process

Heterogeneous activation of peracetic acid (PAA) process is a promising method for removing organic pollutants from water. Nevertheless, this process is constrained by several complex factors, such as the selection of catalysts, optimization of reaction conditions, and identification of mechanism. In this study, a task decomposition strategy was adopted by combining a catalyst and reaction condition optimization machine learning (CRCO-ML) model and a mechanism identification machine learning (MI-ML) model to address these issues. The Categorical Boosting (CatBoost) model was identified as the best-performing model for the dataset (1024 sets and 7122 data points) in this study, achieving an R2 of 0.92 and an RMSE of 1.28. Catalyst composition, PAA dosage, and catalyst dosage were identified as the three most important features through SHAP analysis in the CRCO-ML model. The HCO3- is considered the most influential water matrix affecting the k value. The errors between all reverse experiment results and the predictions of the CRCO-ML and MI-ML models were <10 % and 15 %, respectively. This interdisciplinary work provides novel insights into the design and application of the heterogeneous activation of PAA process, significantly contributing to the rapid development of this technology.

Predicting removal of arsenic from groundwater by iron based filters using deep neural network models

Arsenic (As) contamination in drinking water has been highlighted for its environmental significance and potential health implications. Iron-based filters are cost-effective and sustainable solutions for As removal from contaminated water. Applying Machine Learning (ML) models to investigate and optimize As removal using iron-based filters is limited. The present study developed Deep Learning Neural Network (DLNN) models for predicting the removal of As and other contaminants by iron-based filters from groundwater. A small Original Dataset (ODS) consisting of 20 data points and 13 groundwater parameters was obtained from the field performances of 20 individual iron-amended ceramic filters. Cubic-spline interpolation (CSI) expanded the ODS, generating 1600 interpolated data points (IDPs) without duplication. The Bayesian optimization algorithm tuned the model hyper-parameters and IDPs in a Stratified fivefold Cross-Validation (CV) setup trained all the models. The models demonstrated reliable performances with the coefficient of determination (R2) 0.990-0.999 for As, 0.774-0.976 for Iron (Fe), 0.934-0.954 for Phosphorus (P), and 0.878-0.998 for predicting manganese (Mn) in the effluent. Sobol sensitivity analysis revealed that As (total order index (ST) = 0.563), P (ST = 0.441), Eh (ST = 0.712), and Temp (ST = 0.371) are the most sensitive parameters for the removal of As, Fe, P, and Mn. The comprehensive approach, from data expansion through DLNN model development, provides a valuable tool for estimating optimal As removal conditions from groundwater.

Prediction of hydroxyl radical exposure during ozonation using different machine learning methods with ozone decay kinetic parameters

The abatement of micropollutants by ozonation can be accurately calculated by measuring the exposures of molecular ozone (O3) and hydroxyl radical (•OH) (i.e., ∫[O3]dt and ∫[•OH]dt). In the actual ozonation process, ∫[O3]dt values can be calculated by monitoring the O3 decay during the process. However, calculating ∫[•OH]dt is challenging in the field, which necessitates developing models to predict ∫[•OH]dt from measurable parameters. This study demonstrates the development of machine learning models to predict ∫[•OH]dt (the output variable) from five basic input variables (pH, dissolved organic carbon concentration, alkalinity, temperature, and O3 dose) and two optional ones (∫[O3]dt and instantaneous ozone demand, IOD). To develop the models, four different machine learning methods (random forest, support vector regression, artificial neural network, and Gaussian process regression) were employed using the input and output variables measured (or determined) in 130 different natural water samples. The results indicated that incorporating ∫[O3]dt as an input variable significantly improved the accuracy of prediction models, increasing overall R2 by 0.01-0.09, depending on the machine learning method. This suggests that ∫[O3]dt plays a crucial role as a key variable reflecting the •OH-yielding characteristics of dissolved organic matter. Conversely, IOD had a minimal impact on the accuracy of the prediction models. Generally, machine-learning-based prediction models outperformed those based on the response surface methodology developed as a control. Notably, models utilizing the Gaussian process regression algorithm demonstrated the highest coefficients of determination (overall R2 = 0.91-0.95) among the prediction models.

Application of tryptophan-like fluorescence index to quantify the trace organic compounds removal in wastewater ozonation

The effectiveness of ozonation, one of the techniques known for destroying organic contaminants from wastewater, depends on the composition of the wastewater matrix. The required ozone (O3) dose is determined based on the target compounds during ozonation. Hydroxyl radicals are quantified using a probe compound. The para-chlorobenzoic acid (pCBA) is typically used as a probe compound to measure hydroxyl radicals. However, real-time measurement is impossible, as the analysis process consumes time and resources. This study aimed to evaluate the spectroscopic characteristics of various organic substances in wastewater ozonation through fluorescence excitation-emission matrix and parallel factor analysis. The study also demonstrated that real-time analyzable tryptophan-like fluorescence (TLF) can be used as a hydroxyl radical index. Importantly, the correlation between para-chlorobenzoic acid and TLF was derived, and the results showed a high correlation (R2 = 0.91), confirming the reliability of our findings. Seven trace organic compounds, classified based on their reactivity with O3 and hydroxyl radicals, were selected as target compounds and treated with O3. The TLF index was used as a model factor for the removal rate of the target compounds. The experimental and model values matched when the O3 dose was below 1.0 g O3/g DOC (RMSE: 0.0445-0.0895).

Machine learning based prediction and experimental validation of arsenite and arsenate sorption on biochars

Arsenic (As) contamination in water is a significant environmental concern with profound implications for human health. Accurate prediction of the adsorption capacity of arsenite [As(III)] and arsenate [As(V)] on biochar is vital for the reclamation and recycling of polluted water resources. However, comprehending the intricate mechanisms that govern arsenic accumulation on biochar remains a formidable challenge. Data from the literature on As adsorption to biochar was compiled and fed into machine learning (ML) based modelling algorithms, including AdaBoost, LGBoost, and XGBoost, in order to build models to predict the adsorption efficiency of As(III) and As(V) to biochar, based on the compositional and structural properties. The XGBoost model showed superior accuracy and performance for prediction of As adsorption efficiency (for As(III): coefficient of determination (R2) = 0.93 and root mean square error (RMSE) = 1.29; for As(V), R2 = 0.99, RMSE = 0.62). The initial concentrations of As(III) and As(V) as well as the dosage of the adsorbent were the most significant factors influencing adsorption, explaining 48 % and 66 % of the variability for As(III) and As(V), respectively. The structural properties and composition of the biochar explained 12 % and 40 %, respectively, of the variability of As(III) adsorption, and 13 % and 21 % of that of As(V). The XGBoost models were validated using experimental data. R2 values were 0.9 and 0.84, and RMSE values 6.5 and 8.90 for As(III) and As(V), respectively. The ML approach can be a valuable tool for improving the treatment of inorganic As in aqueous environments as it can help estimate the optimal adsorption conditions of As in biochar-amended water, and serve as an early warning for As-contaminated water.

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models

Analyzing chemicals and their effects on the environment from a life cycle viewpoint can produce a thorough analysis that takes end-of-life (EoL) activities into account. Chemical risk assessment, predicting environmental discharges, and finding EoL paths and exposure scenarios all depend on chemical flow data availability. However, it is challenging to gain access to such data and systematically determine EoL activities and potential chemical exposure scenarios. As a result, this work creates quantitative structure-transfer relationship (QSTR) models for aiding environmental managment decision-making based on chemical structure-based machine learning (ML) models to predict potential industrial EoL activities, chemical flow allocation, environmental releases, and exposure routes. Further multi-label classification methods may improve the predictability of QSTR models according to the ML experiment tracking. The developed QSTR models will assist stakeholders in predicting and comprehending potential EoL management activities and recycling loops, enabling environmental decision-making and EoL exposure assessment for new or existing chemicals in the global marketplace.

Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditions

Enhancing engineering efficiency and reducing operating costs are permanent subjects that face all engineers over the world. To effectively improve the performance of filtration systems, it is necessary to determine an optimal operating condition beyond conventional methods of periodic and empirical operation. Herein, this paper proposes an effective approach to finding an optimal operating strategy using deep reinforcement learning (DRL), particularly for an ultrafiltration (UF) system. Deep learning was developed to represent the UF system utilizing a long-short term memory and provided an environment for DRL. DRL was designed to control three actions; operating pressure, cleaning time, and cleaning concentration. Ultimately, DRL proposed the UF system to actively change the operating pressure and cleaning conditions over time toward better water productivity and operating efficiency. DRL denoted ∼20.9% of specific energy consumption can be reduced by increasing average water flux (39.5-43.7 L m^-2 h^-1) and reducing operating pressure (0.617-0.540 bar). Moreover, the optimal action of DRL was reasonable to achieve better performance beyond the conventional operation. Crucially, this study demonstrated that due to the nature of DRL, the approach is tractable for engineering systems that have structurally complex relationships among operating conditions and resultants.

Multiple Roles of Dissolved Organic Matter in Advanced Oxidation Processes

Advanced oxidation processes (AOPs) can degrade a wide range of trace organic contaminants (TrOCs) to improve the quality of potable water or discharged wastewater effluents. Their effectiveness is impacted, however, by the dissolved organic matter (DOM) that is ubiquitous in all water sources. During the application of an AOP, DOM can scavenge radicals and/or block light penetration, therefore impacting their effectiveness toward contaminant transformation. The multiple ways in which different types or sources of DOM can impact oxidative water purification processes are critically reviewed. DOM can inhibit the degradation of TrOCs, but it can also enhance the formation and reactivity of useful radicals for contaminants elimination and alter the transformation pathways of contaminants. An in-depth analysis highlights the inhibitory effect of DOM on the degradation efficiency of TrOCs based on DOM's structure and optical properties and its reactivity toward oxidants as well as the synergistic contribution of DOM to the transformation of TrOCs from the analysis of DOM's redox properties and DOM's transient intermediates. AOPs can alter DOM structure properties as well as and influence types, mechanisms, and extent of oxidation byproducts formation. Research needs are proposed to advance practical understanding of how DOM can be exploited to improve oxidative water purification.

Insights into the Mechanism of Ozone Activation and Singlet Oxygen Generation on N-Doped Defective Nanocarbons: A DFT and Machine Learning Study

N-doped defective nanocarbon (N-DNC) catalysts have been widely studied due to their exceptional catalytic activity in many applications, but the O₃ activation mechanism in catalytic ozonation of N-DNCs has yet to be established. In this study, we systematically mapped out the detailed reaction pathways of O₃ activation on 10 potential active sites of 8 representative configurations of N-DNCs, including the pyridinic N, pyrrolic N, N on edge, and porphyrinic N, based on the results of density functional theory (DFT) calculations. The DFT results indicate that O₃ decomposes into an adsorbed atomic oxygen species (O_ads) and an ³O₂ on the active sites. The atomic charge and spin population on the O_ads species indicate that it may not only act as an initiator for generating reactive oxygen species (ROS) but also directly attack the organics on the pyrrolic N. On the N site and C site of the N₄V₂ system (quadri-pyridinic N with two vacancies) and the pyridinic N site at edge, O₃ could be activated into ¹O₂ in addition to ³O₂. The N₄V₂ system was predicted to have the best activity among the N-DNCs studied. Based on the DFT results, machine learning models were utilized to correlate the O₃ activation activity with the local and global properties of the catalyst surfaces. Among the models, XGBoost performed the best, with the condensed dual descriptor being the most important feature.

A review of the application of machine learning in water quality evaluation

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.

An Intelligent Deep Learning Model for CO 2 Adsorption Prediction

In this paper, we propose a supervised deep learning neural network (D-CNN) approach to predict CO2 adsorption form the textural and compositional features of biomass porous carbon waste and adsorption features. Both the textural and compositional features of biomass porous carbon waste are utilized as inputs for the D-CNN architecture. A deep learning neural network (D-CNN) is proposed to predict the adsorption rate of ${\text{CO}}_2$ on zeolites. The adsorbed amount will be classified and predicted by the D-CNN. Three tree machine learning models, namely, gradient decision model (GDM), scalable boosting tree model (SBT), and gradient variant decision tree model (GVD), were fused. A feature importance metric was proposed using feature permutation, and the effect of each feature on the target output variable was investigated. The important extracted features from the three employed model were fused and used as the fusion feature set in our proposed model: fusion matrix deep learning model (FMDL). A dataset of 1400 data items, on adsorbent type and various adsorption pressure, is used as inputs for the D-CNN model. Comparison of the proposed model is done against the three tree models, which utilizes a single training layer. The error measure of the D-CNN and the tree model architectures utilize the mean square error confirming the efficiency of 0.00003 for our model, 0.00062 for the SBT, 0.00091 for the GDM, and 0.00098 for the GVD, after 150 epochs. The produced weight matrix was able to predict the ${\text{CO}}_2$ adsorption under diverse process settings with high accuracy of 96.4%.

Machine learning in natural and engineered water systems

Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons

Biomass waste-derived porous carbons (BWDPCs) are a class of complex materials that are widely used in sustainable waste management and carbon capture. However, their diverse textural properties, the presence of various functional groups, and the varied temperatures and pressures to which they are subjected during CO₂ adsorption make it challenging to understand the underlying mechanism of CO₂ adsorption. Here, we compiled a data set including 527 data points collected from peer-reviewed publications and applied machine learning to systematically map CO₂ adsorption as a function of the textural and compositional properties of BWDPCs and adsorption parameters. Various tree-based models were devised, where the gradient boosting decision trees (GBDTs) had the best predictive performance with R² of 0.98 and 0.84 on the training and test data, respectively. Further, the BWDPCs in the compiled data set were classified into regular porous carbons (RPCs) and heteroatom-doped porous carbons (HDPCs), where again the GBDT model had R² of 0.99 and 0.98 on the training and 0.86 and 0.79 on the test data for the RPCs and HDPCs, respectively. Feature importance revealed the significance of adsorption parameters, textural properties, and compositional properties in the order of precedence for BWDPC-based CO₂ adsorption, effectively guiding the synthesis of porous carbons for CO₂ adsorption applications.

Predicting Micropollutant Removal by Reverse Osmosis and Nanofiltration Membranes: Is Machine Learning Viable?

Predictive models for micropollutant removal by membrane separation are highly desirable for the design and selection of appropriate membranes. While machine learning (ML) models have been applied for such purposes, their reliability might be compromised by data leakage due to inappropriate data splitting. More importantly, whether ML models can truly understand the mechanisms of membrane separation has not been revealed. In this study, we evaluate the capability of the XGBoost model to predict micropollutant removal efficiencies of reverse osmosis and nanofiltration membranes. Our results demonstrate that data leakage leads to falsely high prediction accuracy. By utilizing a model interpretation method based on the cooperative game theory, we test the knowledge of XGBoost on the mechanisms of membrane separation via quantifying the contributions of input variables to the model predictions. We reveal that XGBoost possesses an adequate understanding of size exclusion, but its knowledge of electrostatic interactions and adsorption is limited. Our findings suggest that future work should focus more on avoiding data leakage and evaluating the mechanistic knowledge of ML models. In addition, high-quality data from more diverse experimental conditions, as well as more informative variables, are needed to improve the accuracy of ML models for predicting membrane performance.