Prediction Of Effluent Quality Research Articles

Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network

The accurate effluent prediction plays a crucial role in providing early warning for abnormal effluent and achieving the adjustment of feedforward control parameters during wastewater treatment. This study applied a dual-staged attention mechanism based on long short-term memory network (DA-LSTM) to improve the accuracy of effluent quality prediction. The results showed that input attention (IA) and temporal attention (TA) significantly enhanced the prediction performance of LSTM. Specially, IA could adaptively adjust feature weights to enhance the robustness against input noise, with R2 increased by 13.18%. To promote its long-term memory ability, TA was used to increase the memory span from 96 h to 168 h. Compared to a single LSTM model, the DA-LSTM model showed an improvement in prediction accuracy by 5.10%, 2.11%, 14.47% for COD, TP, and TN. Additionally, DA-LSTM demonstrated excellent generalization performance in new scenarios, with the R2 values for COD, TP, and TN increasing by 22.67%, 20.06%, and 17.14% respectively, while the MAPE values decreased by 56.46%, 63.08%, and 42.79%. In conclusion, the DA-LSTM model demonstrated excellent prediction performance and generalization ability due to its advantages of feature-adaptive weighting and long-term memory focusing. This has forward-looking significance for achieving efficient early warning of abnormal operating conditions and timely management of control parameters.

En-WBF: A Novel Ensemble Learning Approach to Wastewater Quality Prediction Based on Weighted BoostForest

With the development of urbanization, the accurate prediction of effluent quality has become increasingly critical for the real-time control of wastewater treatment processes. The conventional method for measuring effluent biochemical oxygen demand (BOD) suffers from significant time delays and high equipment costs, making it less feasible for timely effluent quality assessment. To tackle this problem, we propose a novel approach called En-WBF (ensemble learning based on weighted BoostForest) to predict effluent BOD in a soft-sensing manner. Specifically, we sampled several independent subsets from the original training set by weighted bootstrap aggregation to train a series of gradient BoostTrees as the base models. Then, the predicted effluent BOD was derived by weighting the base models to produce the final prediction. Experiments on real datasets demonstrated that on the UCI dataset, the proposed En-WBF approach achieved a series of improvements, including by 28.4% in the MAE, 40.9% in the MAPE, 29.8% in the MSE, 18.2% in the RMSE, and 2.3% in the R2. On the Fangzhuang dataset, the proposed En-WBF approach achieved a series of improvements, including by 8.8% in the MAE, 9.0% in the MAPE, 12.8% in the MSE, 6.6% in the RMSE, and 1.5% in the R2. This paper contributes a cost-effective and timely solution for wastewater treatment management in real practice with a more accurate effluent BOD prediction, validating the research in the application of ensemble learning methods for environmental monitoring and management.

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants’ operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

Prediction of artificial neural network for sulfate removal from wastewater and application analysis of key factors in anaerobic biological system

Prediction and application of the SO42- removal performance of an anaerobic biological system was conducted through the integration of artificial neural networks (ANNs). The present investigation involved the utilization of Radial Basis Function Neural Networks (RBFNN) and Multilayer Perceptron Neural Networks (MLPNN) for more rigorous prediction of SO42- removal rates. The findings of the study demonstrated that reducing the chemical oxygen demand (COD) to sulfate ratio (COD/SO42-) led to a consequent decline in the removal of SO42-. In the context of predicting SO42- reduction under conditions characterized by low COD/SO42-, MLPNN exhibited superior suitability when compared to RBFNN. The sulfate removal process was primarily influenced by various operation parameters, with the COD/SO42- being particularly influential. The proposed sensitive MLPNN model contributed to the prediction of effluent quality and potential real-time adjustments of wastewater treatment operations. The predictive evaluation of effective sulfate (SO42-) removal from sulfate-rich wastewater under various operation parameters is a significant point of guideline.

Enhancing wastewater treatment efficiency through machine learning-driven effluent quality prediction: A plant-level analysis

The main objective of this study was to develop, validate, and comprehend machine learning (ML) models capable of predicting chemical oxygen demand concentration in the effluent (CODout) of a given wastewater treatment plant (WWTP). The parameter CODout was chosen as a target due to its crucial role in controlling and optimizing water treatment plants, emphasizing its vital importance in maintaining the efficiency and quality of the water purification process. A calibrated WWTP model available on WEST software (DHI), developed using the Umbilo WWTP (South Africa) as a reference, served as a source for extracting influent and effluent data. The dataset was organized daily and hourly to train the ML predictive models. ML techniques used in this study include Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and Random Forest (RF). Exploratory data analysis revealed multicollinearity between some of the model's input variables, requiring a selection of these parameters. In the case of CODout predictions using the daily dataset, the MLP model proved more effective than other ML models. When the hourly dataset was applied, the LSTM models performed better by incorporating historical data into the model structure. When real effluent measurements were used to predict CODout, the SVM model had superior results, even outperforming traditional mechanistic models. Variable importance analysis showed the significant influence of influent TSS to predict CODout. In conclusion, the study demonstrates the successful applicability of ML models to predict CODout in WWTPs and provides valuable insights for optimizing wastewater treatment. The emphasis on model interpretability and validation with real data enriches the results' reliability.

A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant

Wastewater treatment plant (WWTP) operation is usually intricate due to large variations in influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTP effluent water quality can provide valuable decision-making support to facilitate their operations and management. In this study, we developed a novel hybrid deep learning model by combining the temporal convolutional network (TCN) model with the long short-term memory (LSTM) network model to improve the simulation of hourly total nitrogen (TN) concentration in WWTP effluent. The developed model was tested in a WWTP in Jiangsu Province, China, where the prediction results of the hybrid TCN-LSTM model were compared with those of single deep learning models (TCN and LSTM) and traditional machine learning model (feedforward neural network, FFNN). The hybrid TCN-LSTM model could achieve 33.1 % higher accuracy as compared to the single TCN or LSTM model, and its performance could improve by 63.6 % comparing to the traditional FFNN model. The developed hybrid model also exhibited a higher power prediction of WWTP effluent TN for the next multiple time steps within eight hours, as compared to the standalone TCN, LSTM, and FFNN models. Finally, employing model interpretation approach of Shapley additive explanation to identify the key parameters influencing the behavior of WWTP effluent water quality, it was found that removing variables that did not contribute to the model output could further improve modeling efficiency while optimizing monitoring and management strategies.

Effluent quality prediction of the sewage treatment based on a hybrid neural network model: Comparison and application

The accurate prediction and assessment of effluent quality in wastewater treatment plants (WWTPs) are paramount for the efficacy of sewage treatment processes. Neural network models have exhibited promise in enhancing prediction accuracy by simulating and analyzing diverse influent parameters. In this study, a back propagation neural network hybrid model based on a tent chaotic map and sparrow search algorithm (Tent_BP_SSA) was developed to predict the effluent quality of sewage treatment processes. The prediction performance of the propose hybrid model was compared with traditional neural network models using five performance indicators (MAE, RMSE, SSE, MAPE and R2). Specifically, in comparison with the prior Tent_BP_SSA, Tent_BP_SSA2 demonstrated notable enhancements, with the R2 increasing from 0.9512 to 0.9672, while MAE, RMSE, SSE, and MAPE decreased by 9.62%, 18.84%, 24.80%, and 47.10%, respectively. These indicators collectively affirm that the utilization of higher-order input parameters ensures improved accuracy of the Tent_BP_SSA2 hybrid model in predicting effluent quality. Moreover, the Tent_BP_SSA2 model exhibited robust prediction ability (R2 of 0.9246) when applied to assess the effluent quality of an actual sewage treatment plant. The incorporation of integrated models comprising the sparrow search optimizing algorithm, tent chaotic mapping, and higher-order magnitude decomposition of input parameters has demonstrated the capacity to enhance the accuracy of effluent quality prediction. This study illuminates novel perspectives on the prediction of effluent quality and the assessment of effluent warnings in WWTPs.

Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning

Precisely predicting the concentration of nitrogen-based pollutants from the wastewater treatment plants (WWTPs) remains a challenging yet crucial task for optimizing operational adjustments in WWTPs. In this study, an integrated approach using factor analysis (FA) and machine learning (ML) models was employed to accurately predict effluent total nitrogen (Ntoteff) and nitrate nitrogen (NO3-Neff) concentrations of the WWTP. The input values for the ML models were honed through FA to optimize factors, thereby significantly enhancing the ML prediction accuracy. The prediction model achieved a highest coefficient of determination (R2) of 97.43 % (Ntoteff) and 99.38 % (NO3-Neff), demonstrating satisfactory generalization ability for predictions up to three days ahead (R2 >80 %). Moreover, the interpretability analysis identified that the denitrification factor, the pollutant load factor, and the meteorological factor were significant. The model framework proposed in this study provides a valuable reference for optimizing the operation and management of wastewater treatment.

Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations. In this study, we aimed to develop an integrated method for predicting effluent COD and NH3 levels. We employed a 200 L pilot-scale sequencing batch reactor (SBR) to gather multimodal data from urban sewage over 40 d. Then we collected data on critical parameters like COD, DO, pH, NH3, EC, ORP, SS, and water temperature, alongside wastewater surface images, resulting in a data set of approximately 40246 points. Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network (BITF-CL) using this data. This innovative model synergized sewage imagery with water quality data, enhancing prediction accuracy. As a result, the BITF-CL model reduced prediction error by over 23% compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data. Consequently, this research presents a cost-effective and precise prediction system for sewage treatment, demonstrating the potential of brain-inspired models.

Ensemble water quality forecasting based on decomposition, sub-model selection, and adaptive interval

The prediction of effluent quality for wastewater treatment plants (WWTPs) has caused widespread concern due to its essential role in ensuring water quality standards and reducing energy consumption. However, the complex nonlinearity of WWTPs leads to difficulties in forecasting and less attention to forecast uncertainty. A novel ensemble water quality forecasting (EWQF) system is proposed that incorporates data preprocessing, point prediction and interval prediction. The system provides an accurate prediction of effluent quality and analyses this uncertainty, for enabling feed-forward control of WWTPs. Specifically, the original water quality data is decomposed into subsequences containing more information and less noise based on improved variational modal decomposition (IVMD). The optimal sub-model for each sub-series is selected from six prediction models based on the sub-model selection strategy, and the point prediction results for water quality are obtained by combining the prediction results of the sub-models. Robust and reliable prediction interval construction based on adaptive kernel density estimation. The results demonstrate that the EWQF achieves optimal point prediction results (R2 = 0.955). The EWQF interval prediction achieves the optimal coverage width criterion (CWC) for different confidence intervals and decision objectives. These results demonstrate that EWQF systems can perform excellent point and interval prediction.

Optimization of deep learning models for forecasting performance in the water industry using genetic algorithms

The objective of this study is to develop an optimization approach for performance prediction in the water industry by integrating feature selection (FS), machine learning and genetic algorithms (GAs). To select the best set of input variables, FS methods such as Pearson correlation coefficient (PCC), principal component analysis (PCA), and GAs were employed. Next, a GA method was used to optimize the hyperparameters and the architecture of a deep learning network. The methodology was tested using bi-directional long-short term memory networks for univariate prediction of effluent quality and biogas production. The results demonstrate that FS methods are not universal and depend on the desired goal. Additionally, optimized machine learning applications to wastewater process data may require a shallow hidden architecture consisting of two hidden layers with an average of 25 total neurons, resulting in models that are more computationally efficient and interpretable. This proposed methodology has a wide range of applications, including predictive big data analytics, optimal data-driven development of soft-sensors, multi-objective optimization, and model predictive controllers.

An integrated machine learning framework for effluent quality prediction in Sewage Treatment Units

ABSTRACT The influent and effluent data from wastewater treatment plants being highly correlated with multi-variable coupling and time-varying features may degrade the performance of conventional soft sensors over time. Adaptive strategies based on just-in-time learning (JIT), moving windows (MW), and time difference (TD) are used in this work to develop an adaptive soft sensor. Multi-output Gaussian-process regression (MGPR) is selected and hybrid methods such as TD JIT, MW TD, and JIT MW TD along with TD and MGPR methods are implemented. Data from the benchmark simulation model No.1, closed-loop architecture after applying PI controller, and real-time data from the Rithala Plant of Delhi are obtained. The improved error percentage is 15.03% for total phosphorus (open-loop) using the JIT TD method when compared with the MW TD method. Fair results are observed with JIT TD on real time data with a strong correlation between predicted and observed values, above 0.8 for any variable being estimated.

Construction and application of effluent quality prediction model with insufficient data based on transfer learning algorithm in wastewater treatment plants

Prediction of effluent water quality is important for the management of wastewater treatment plants (WWTPs). However, the accuracy of effluent quality prediction is poor owing to a lack of training data, and prediction accuracy is influenced. This study builds an effluent Chemical Oxygen Demand (COD) prediction model trained by insufficient data with influent and biochemical parameters as inputs, which is integrated with transfer learning algorithm Two-stage TrAdaBoost.R2. The model trained by the source WWTP is transferred to the target WWTP through transfer learning, it obtains better performance than boosting algorithm AdaBoost.R2 and decision tree algorithm. The coefficient of determination (R2) improves from 0.51 to 0.8, and the mean square error (MSE) decreases from 19.69 to 8.29. The application of Two-stage TrAdaBoost.R2 effectively excavates the data characteristics, which demonstrates the effectiveness of transfer algorithm. Moreover, the transfer algorithm is explored in other effluent indexes (TP, NH3-N, pH), and NH3-N obtains better prediction accuracy than that of TP and pH, possibly attributed to more useful knowledge of NH3-N obtained from source data to get excellent predicting effectiveness. The results indicate that the integrated method proposed in this study can relieve inferior predicting ability caused by insufficient data effectively. Thus, this provides a theoretical basis and technical support for the stable operation of WWTPs.

Deep learning-based prediction of effluent quality of a constructed wetland

Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands. However, the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction. To address this problem, in this study, we propose an approach based on multi-source data fusion that considers the following indicators: water quality indicators, water quantity indicators, and meteorological indicators. In this study, we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland: (1) multiple linear regression; (2) backpropagation neural network (BPNN); (3) genetic algorithm combined with the BPNN to solve the local minima problem; and (4) long short-term memory (LSTM) neural network to consider the influence of past results on the present. The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method, with a satisfactory R2. Additionally, given the huge fluctuation of different pollutant concentrations in the effluent, we used a moving average method to smooth the original data, which successfully improved the accuracy of traditional neural networks and hybrid neural networks. The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.

Evaluation of auto regressive integrated moving average (arima) and artificial neural networks (ann) in the prediction of effluent quality of a wastewater treatment system

The main objective of wastewater treatment is to purify the water by degradation of organic matter in the water to anenvironmentally friendly status. To achieve this objective, some effluent (waste water) quality parameters such asChemical oxygen demand (COD) and Biochemical oxygen demand (BOD5) should be measured continuously in orderto meet up with the said objective and regulatory demands. However, through the prediction on water qualityparameters, effective guidance can be provided to comply with such demand without necessarily engaging in rigorouslaboratory analysis. Box-Jenkin’s Auto Regressive Integrated Moving Average (ARIMA) technique is one of the mostrefined extrapolation techniques for prediction while Artificial Neural Network (ANN) is a modern non-linear methodalso used for prediction. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root MeanSquare Error (RMSE) and Correlation coefficient (r) are used to evaluate the accuracy of the above-mentionedmodels. This paper examined the efficiency of ARIMA and ANN models in prediction of two major water qualityparameters (COD and BOD5) in a wastewater treatment plant. With the aid of R software, it was concluded that in allthe error estimates, ANNs models performed better than the ARIMA model, hence it can be used in the operation ofthe treatment system.

Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling

Improving the operation, management, and consequent performance of wastewater treatment plants (WWTPs) for conserving the water environment is crucial. Recent advancements in artificial intelligence (AI) modeling have shown the potential to solve the non-linear simulation of processes in WWTPs and facilitate real-time operational adjustments. In this study, a dynamic nonlinear autoregressive network with an exogenous input (NARX) model was established for predicting effluent quality. The performance was optimized with different time-delay parameters and training algorithms. Then, a PCA-NARX hybrid model was established for high performance and comparison with two static artificial neural network (ANN) models. The BR algorithm exhibited the highest performance among the four training algorithms for the NARX model. The dynamic PCA-NARX model was significantly superior to static models in modeling effluent quality. The PCA-NARX model predicted the effluent chemical oxygen demand (CODcr) and total nitrogen (TN) with high accuracy (RMSECOD = 2.9 mg/L, RMSETN = 0.8 mg/L). Therefore, we propose a stable and sensitive dynamic neural network model for predicting effluent quality and potential real-time adjustment of wastewater treatment operations.

Adaptive dynamic prediction of effluent quality in wastewater treatment processes using partial least squares embedded with relevance vector machine

Recycling of water resources through wastewater treatment process (WWTP) is widely considered for a viable option for different water demands and sustainable production due to water shortage and pollution. Thus, accurate and real-time prediction of effluent quality is of vital importance to maintain the stable process control of WWTP system. Taking into account the complex biochemical characteristics of WWTP, an adaptive dynamic nonlinear partial least squares (PLS) model has been proposed to improve the prediction performance and stability of effluent quality indexes. First, an adaptive matrix expansion scheme is introduced to exploit the specific dynamic features of different data sets. Then the linear PLS model is conducted and the inner function between each pair of latent variables is rebuilt by relevance vector machine (RVM) with sparse and probabilistic structure, which is robust for the nonlinearity and random noises among process variables. The performance of the proposed adaptive dynamic RVM-based PLS (D-RVM-PLS) model is evaluated through two case studies based on benchmark simulation model No.1 and a real industrial WWTP, respectively. Experimental results show the superiority of the proposed model in prediction accuracy, stability, and execution efficiency. For the modeling accuracy of effluent chemical oxygen demand in real industrial case, adaptive D-RVM-PLS provides the lowest root-mean-square error, which is decreased by approximately 5.01%–49.19% in comparison with linear PLS model, Gaussian process regression-based PLS model, and their dynamic models without adaptive selection procedure.

Effluent quality prediction in papermaking wastewater treatment processes using dynamic Bayesian networks

Effective online modeling of papermaking wastewater treatment processes (WWTPs) is an important means to ensure wastewater recycling and harmless discharge. A composite model integrating variable importance in projection with dynamic Bayesian networks (VIP-DBN) is proposed to improve the modeling ability and reliability in WWTPs. First, a variable selection method is applied to simplify the network structure and reduce modeling costs. Then the augmented matrices technique is embedded into the Bayesian networks to cope with the dynamic characteristics, nonlinearity, and uncertainty simultaneously. The modeling performance of VIP-DBN is evaluated through two case studies, a simulated WWTP based on benchmark simulation model no. 1 (BSM1) and a real papermaking WWTPs, in which the VIP-DBN model shows better modeling performance than its rivals. Specifically, compared with partial least squares, artificial neural networks, and Bayesian networks, the determination coefficient value of VIP-DBN is increased by 34.36%, 20.55%, and 3.30%, respectively, for the prediction of effluent nitrate in BSM1. The VIP-DBN can be used to deal with complex WWTPs in industrial applications, which provides a better practical method for soft sensor modeling and a guarantee for the effective decision-making of wastewater treatment processes for papermaking enterprises.

Improved support vector machine using optimization techniques for an aerobic granular sludge

Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is a good prediction tool in handling limited data set. In this paper, an improved SVM using optimization approaches for better predictions is proposed. Two different types of optimization are built which are particle swarm optimization (PSO) and genetic algorithm (GA). The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid Search are developed and compared prior to several feature analysis for verification purposes. The experimental data under hot temperature of 50˚C obtained from sequencing batch reactor is used. From simulation results, the proposed SVM with optimizations improve the prediction of chemical oxygen demand compared to the conventional grid search method and hence provide better prediction of effluent quality using AGS wastewater treatment systems.

Towards more predictive clarification models via experimental determination of flocculent settling coefficient value

Improved settleability has become an essential feature of new wastewater treatment innovations. To accelerate adoption of such new technologies, improved clarifier models are needed to help with designing and predicting improvement in settleability. In general, the level of mathematics of settling clarifier models has gone far beyond the level of existing experimental methods available to support these models. To date, even for simple one-dimensional (1D) clarifier models, no experimental method has been described for flocculent settling coefficient (rp). As a consequence, rp cannot be considered as a sludge characteristic and is used as a calibration parameter to achieve observed effluent quality. In this study, we focused on the development of an empirical function based on a simple and practical experimental approach for the calculation of the rp value from sludge characteristics. This approach provided a similar approach as currently taken for hindered settling coefficient calculations (Veslind equation) and allowed for the model to predict effluent quality, thus increasing the power of the 1D model. The threshold of flocculation (TOF), which describes the collision efficiency of particles, directly correlated with the effluent quality of the five tested activated sludge systems and was selected as experimental method. The proposed empirical function between TOF and rp was validated for four years of validating data with five different sludge types operated under different operational conditions and configurations. The good effluent quality prediction with this approach brings us one step closer in making the clarification models more predictive towards effluent quality and clarifier performance.