A full-view management method based on artificial neural networks for energy and material-savings in wastewater treatment plants

Effective evaluation of greenhouse gases (GHGs) emissions from anoxic/oxic (A/O) process of regenerated papermaking wastewater treatment through hybrid deep learning techniques: Leveraging the critical role of water quality indicators

Accurate accounting of greenhouse gases (GHGs) emissions from industrial wastewater treatment processes/plants with high organic concentration and fluctuating inflows is crucial for the calculation and management of carbon emissions. The impacts of water quality indicators on GHGs emissions within the biological nutrient removal process are still unclear, which deserves intensive attention. Here, a lab-scale anoxic/oxic (A/O) process was constructed for raw regenerated papermaking wastewater treatment with different low/high-concentration influent stages for about 110 days to evaluate GHGs emissions. A high-quality dataset included 295 sets of the multi-factors (including COD, suspended solid (SS), NH4+-N, NO3--N, NO2--N, and pH/DO/Temperature) was built. Moreover, the corresponding proportion of GHGs emissions were analyzed and a novel hybrid deep learning model TCNA, which integrated the Temporal Convolutional Network (TCN) and Attention Mechanism (AM), was developed to explore the trends and predictions of GHGs emissions based on the dataset. A series of comparisons with model Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Temporal Convolutional Networks (TCN) were also conducted under the same conditions. The TCNA model showed an outstanding performance for CO2, CH4, and N2O emissions prediction, achieving the highest value of R2 score (CO2, 0.8014; CH4, 0.8839; N2O, 0.9354) and the lowest value of root mean squared error (RMSE) and mean absolute error (MAE) (CO2: 2.6137,1.9366; CH4: 1.929,0.7214; N2O: 0.8897, 0.5777) among the five models above. The results highlight the potential of the TCNA model for accurate and robust prediction of GHGs emissions from industrial wastewater treatment plants with the A/O treatment process, contributing to effective GHGs mitigation strategies and carbon management of industrial wastewater treatment.

A linguistic analysis of energy terminology in the wastewater literature

Recent wastewater treatment research has focused on technologies that can recover resources such as energy from the influent waste stream. Many unrelated studies have introduced or used energy-related terms to describe changes to wastewater treatment plant energy balances based on these technological innovations. Unfortunately, these wastewater energy-related terms are not well defined in the literature, with many used interchangeably and/or inconsistently. To address this shortcoming, this study (1) identified and defined the most prominent energy-related terms in academic literature, (2) proposed a classification schema, and (3) explored trends in term usage over time. Energy-related terms identified from the literature were defined and classified based on the term's functional role in the context of wastewater treatment plant energy use. Specifically, each term was classified as a wastewater treatment plant's long-term energy 'state', a descriptive short-term energy 'condition' at the plant, or an energy 'mechanism' that drives a plant from one state to another. The trend analysis concluded that the development of energy-related wastewater literature has generally outpaced the baseline rate of academic publishing in all fields. The results of this study can ensure clear communication between actors in the wastewater treatment sector by standardizing definitions for energy-related terms.

Prediction of metal recovery potential of end-of-life NEV batteries in China based on GRA-BiLSTM

As Chinese new energy vehicle (NEV) sales continue to grow, end-of-life batteries have great potential for recycling in the future. In this study, a combined model based on Gray Relation Analysis and Bi-directional Long Short-Term Memory (GRA-BiLSTM) is proposed for predicting NEV sales, and the NEV battery life is modeled using the Weibull distribution. Then, the amount of end-of-life batteries, secondary utilization and metal recycling are calculated. The impact of end-of-life battery recycling on the supply and demand of key metals is studied. The results show that in 2040, the secondary utilization of end-of-life batteries in the Standard Growth Rate-Lithium Iron Phosphate Battery Dominated-High Secondary Utilization rate scenario (SGR-LFPH) is 391.76 GWh. The recycling volumes of lithium, nickel and cobalt are 45,900 tons, 92,900 tons and 22,100 tons, respectively. In the Standard Growth Rate-lithium nickel cobalt manganese oxide Battery Dominated-Low Secondary Utilization rate scenario (SGR-NCML), the recycling of lithium, nickel and cobalt is even greater, at 62,600 tons, 372,200 tons and 71,700 tons, respectively. End-of-life batteries recycling can reduce the demand for metals. However, as NEV sales continue to grow, the gap between metal supply and demand remains significant. The findings urge the Chinese government develop appropriate battery management strategies to increase the recycling rate of end-of-life batteries; and to encourage enterprises to research new types of batteries to resolve the conflict between supply and demand for metals.

A novel hybrid variable cross layer-based machine learning model improves the accuracy and interpretation of energy intensity prediction of wastewater treatment plant

Energy intensity (EI) prediction in wastewater treatment plants (WWTPs) suffers from inaccuracy and non-interpretability due to poor data quality, complex mechanisms and various confounding variables. In this study, the novel hybrid variable cross layer-based machine learning (VCL-ML) model was devised, which generates new knowledge with monitoring indicators (e.g., COD, etc.) and then embeds both domain knowledge and monitoring indicators into the ML model. This novel hybrid VCL-ML model achieves a root-mean-square error (RMSE) of 0.021 kW h/m³ with an 8.7% improvement over the conventional ML (Con-ML) model. The Shapley additive explanation demonstrated that domain knowledge features are ranked high and have important interpretable implications for the model, such as capacity utilization (CU), which measures the efficiency of resource use, and total nitrogen remaining rate (TN_rr), which indicates the nitrogen retention in a system. Partially dependent interactions between domain knowledge (e.g., sludge yield) and monitoring indexes (e.g., influent pH) could contribute to the interpretation of reality. By comparing the feature categorization between VCL-ML and Con-ML models, temporal information (e.g., month) and removal information (e.g., TN_rr) played an important role in the model's performance improvement. This result highlights the strong correlation between wastewater treatment plant energy intensity with pollutant removal and temporal information while weakening the contribution of other redundant features. This VCL-ML model improves the predicting accuracy and interpretation of the EI of WWTPs, which can be used in the optimal operation and sustainable management of WWTPs.

Predictive modeling for the adsorptive and photocatalytic removal of phenolic contaminants from water using artificial neural networks

Numerous harmful phenolic contaminants are discharged into water that pose a serious threat to environment where two of the most important purification methodologies for the mitigation of phenolic contaminants are adsorption and photocatalysis. Besides cost, each process has drawbacks in terms of productivity, environmental impact, sludge creation, and the development of harmful by-products. To overcome these limitations, the modeling and optimization of water treatment methods is required. Artificial Intelligence (AI) is employed for the interpretation of treatment-based processes due to powerful learning, simplicity, high estimation accuracy, effectiveness, and improvement of process efficiency where artificial neural networks (ANNs) are most frequently employed for predicting and analyzing the efficiency of processes applied for the mitigation of these phenolic contaminants from water. ANNs are superior to conventional linear regression models because the latter are incapable of dealing with non-linear systems. ANNs can also reduce the operational cost of treating phenol-contaminated water. A correlation coefficient of >0.99 can be achieved using ANN with enhanced phenol mitigation percentage accuracy generally ranging from 80 % to 99.99 %. Using ANN optimization, the maximum phenol mitigation efficiencies achieved were 99.99 % for phenol, 99.93 % for bisphenol A, 99.6 % for nonylphenol, 97.1 % for 2-nitrophenol, 96.6 % for 4-chlorophenol and 90 % for 2,6-dichlorophenol. In numerous ANN models, Levenberg-Marquardt backpropagation algorithm for training was employed using MATLAB software. This study overviews their employment and application for optimization and modeling of removal processes and explicitly discusses the important input and output parameters necessary for better performance of the system. The comparison of ANNs with other AI techniques revealed that ANNs have better predictability for mitigation of most of the phenolic contaminants. Furthermore, several challenges and future prospects have also been discussed.

Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

Wastewater treatment process enhancement based on multi-objective optimization and interpretable machine learning

Optimization and control of wastewater treatment process (WTP) can contribute to cost reduction and efficiency. A wastewater treatment process multi-objective optimization (WTPMO) framework is proposed in this paper to provide suggestions for decision-making in setting parameters of WTP. Firstly, the prediction models based on Extreme Gradient Boosting (XGB) with Bayesian optimization (BO) are developed for predicting effluent water quality (EQ) and energy consumption (EC) for different influent quality and process parameter settings. Then, the SHapley Additive exPlanations (SHAP) algorithm is used to complement the interpretability of machine learning to quantitatively evaluate the impact of different features on the predicted targets. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Technique for Ordering Preferences on Similarity of Ideal Solutions (TOPSIS) is introduced to solve and make decisions on the multi-objective optimization problem. The WTPMO applicability is validated on Benchmark Simulation Model 1 (BSM1). The results show that BOXGB achieves accurate prediction for EQ and EC with R2 values of 0.923 and 0.965, respectively, indicating that BO can effectively select the model hyperparameters in XGB. Based on SHAP supplemented the interpretability of the model to fully explain how the influent water quality and decision variables affect the EQ and EC of the WTP. In addition, the optimized process parameters are determined based on NSGA-II and TOPSIS, and the EC optimization rate is 1.552% while guaranteeing water quality compliance. Overall, this research can effectively achieve the optimization of WTP, ensure that the effluent water quality meets the standards while reducing energy consumption, assist Wastewater treatment plants (WWTPs) to achieve more intelligent and efficient operation and maintenance management, and provide strong support for environmental protection and sustainable development goals.

Prophet time series modeling of waste disposal rates in four North American cities

In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R2 of the Prophet model during pre-COVID-19 were 4.3-22.2% and 0.71-0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast.

Effluent parameters prediction of a biological nutrient removal (BNR) process using different machine learning methods: A case study

This paper proposes a novel targeted blend of machine learning (ML) based approaches for controlling wastewater treatment plant (WWTP) operation by predicting distributions of key effluent parameters of a biological nutrient removal (BNR) process. Two years of data were collected from Plajyolu wastewater treatment plant in Kocaeli, Türkiye and the effluent parameters were predicted using six machine learning algorithms to compare their performances. Based on mean absolute percentage error (MAPE) metric only, support vector regression machine (SVRM) with linear kernel method showed a good agreement for COD and BOD5, with the MAPE values of about 9% and 0.9%, respectively. Random Forest (RF) and EXtreme Gradient Boosting (XGBoost) regression were found to be the best algorithms for TN and TP effluent parameters, with the MAPE values of about 34% and 27%, respectively. Further, when the results were evaluated together according to all the performance metrics, RF, SVRM (with both linear kernel and RBF kernel), and Hybrid Regression algorithms generally made more successful predictions than Light GBM and XGBoost algorithms for all the parameters. Through this case study we demonstrated selective application of ML algorithms can be used to predict different effluent parameters more effectively. Wider implementation of this approach can potentially reduce the resource demands for active monitoring the environmental performance of WWTPs.

Insights into the roles and mechanisms of a green-prepared magnetic biochar in anaerobic digestion of waste activated sludge

Methane is one of the most promising renewable energies to alleviate energy crisis or replace fossil fuels, which can be recovered from anaerobic digestion of bio-wastes. However, the engineering application of anaerobic digestion is always hindered by low methane yield and production rate. This study revealed the roles and mechanisms of a green-prepared magnetic biochar (MBC) in promoting methane production performance from waste activated sludge. Results showed that the methane yield reached 208.7 mL/g volatile suspended solids with MBC additive dosage of 1 g/L, increasing by 22.1 % compared to that in control. Mechanism analysis demonstrated that MBC could promote hydrolysis, acidification, and methanogenesis stages. This was because the properties of biochar were upgraded by loading nano-magnetite, such as specific surface area, surface active sites, and surface functional groups, which made MBC have greater potential to mediate electron transfer. Correspondingly, the activity of α-glucosidase and protease respectively increased by 41.7 % and 50.0 %, and then the hydrolysis performances of polysaccharides and proteins were improved. Also, MBC improved the secretion of electroactive substances like humic substances and cytochrome C, which could promote extracellular electron transfer. Furthermore, Clostridium and Methanosarcina, as well-known electroactive microbes, were selectively enriched. The direct interspecies electron transfer between them was established via MBC. This study provided some scientific evidences to comprehensively understand the roles of MBC in anaerobic digestion, with important implications for achieving resource recovery and sludge stabilization.

Development and application of random forest regression soft sensor model for treating domestic wastewater in a sequencing batch reactor

Small-scale distributed water treatment equipment such as sequencing batch reactor (SBR) is widely used in the field of rural domestic sewage treatment because of its advantages of rapid installation and construction, low operation cost and strong adaptability. However, due to the characteristics of non-linearity and hysteresis in SBR process, it is difficult to construct the simulation model of wastewater treatment. In this study, a methodology was developed using artificial intelligence and automatic control system that can save energy corresponding to reduce carbon emissions. The methodology leverages random forest model to determine a suitable soft sensor for the prediction of COD trends. This study uses pH and temperature sensors as premises for COD sensors. In the proposed method, data were pre-processed into 12 input variables and top 7 variables were selected as the variables of the optimized model. Cycle ended by the artificial intelligence and automatic control system instead of by fixed time control that was an uncontrolled scenario. In 12 test cases, percentage of COD removal is about 91. 075% while 24. 25% time or energy was saved from an average perspective. This proposed soft sensor selection methodology can be applied in field of rural domestic sewage treatment with advantages of time and energy saving. Time-saving results in increasing treatment capacity and energy-saving represents low carbon technology. The proposed methodology provides a framework for investigating ways to reduce costs associated with data collection by replacing costly and unreliable sensors with affordable and reliable alternatives. By adopting this approach, energy conservation can be maintained while meeting emission standards.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Anaerobic membrane bioreactor for carbon-neutral treatment of industrial wastewater containing N, N-dimethylformamide: Evaluation of electricity, bio-energy production and carbon emission

The feasibility of anaerobic membrane bioreactor (AnMBR) for the treatment of N, N-dimethylformamide (DMF)-containing wastewater was theoretically compared with the conventional activated sludge (CAS) process in this study. The electricity consumption and expenditure, bio-energy production and CO₂ emission were investigated using the operational results of a lab-scale AnMBR operated in a long-term operation. The AnMBR was capable of producing bio-methane from wastewater and generated 3.45 kWh/m³ of electricity as recovered bio-energy while the CAS just generated 1.17 kWh/m³ of electricity from the post-treatment of excessive sludge disposal. The large quantity of bio-methane recovered by the AnMBR can also be sold as sustainable bioresource for the use of household natural gas with a theoretical profit gain of 29,821 US$/year, while that of the CAS was unprofitable. The AnMBR was also demonstrated to significantly reduce the carbon emission by obtaining a theoretical negative CO₂ production of -2.34 kg CO₂/m³ with the recycle of bio-energy while that for the CAS was 4.50 kg CO₂/m³. The results of this study demonstrate that the AnMBR process has promising potential for the carbon-neutral treatment of high-strength DMF-containing wastewater in the future.

Prediction of wastewater treatment system based on deep learning

Introduction

In order to accurately model the IC reactor of the wastewater treatment system and efficiently control and adjust the water treatment process, this paper proposes a method to predict the operation effect of the IC reactor using an artificial neural network model. This paper takes the IC reactor section of a papermaking wastewater treatment plant as the research object, and predicts the COD value of its effluent through the neural network model established. The experimental results show that the simulation prediction value of BP neural network is basically consistent with the change trend of the actual value, and has a certain prediction ability. Among the 20 groups of sample data for simulation prediction, the prediction relative error value of 9 sample data pairs is less than 5%, that is, the prediction error of 45% sample data pairs is within 5%; The relative error value of 15 sample data pairs is less than 10%, that is, 75% of sample data pairs have a prediction error of less than 10%; The maximum relative error is 18.6%. Through the regression analysis of the real value and the predicted value, the correlation coefficient is 0.7431.

Conclusion

The BP neural network can capture the non-linear mapping relationship between the selected input factors and the output, and can predict the COD value of the effluent of IC reactor in advance.