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Dantas MS, Christofaro C, Oliveira SC. Artificial neural networks for performance prediction of full-scale wastewater treatment plants: a systematic review. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 88:1447-1470. [PMID: 37768748 PMCID: wst_2023_276 DOI: 10.2166/wst.2023.276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Wastewater treatment plants (WWTPs) are complex systems that must maintain high levels of performance to achieve adequate effluent quality to protect the environment and public health. Artificial intelligence and machine learning methods have gained attention in recent years for modeling complex problems, such as wastewater treatment. Although artificial neural networks (ANNs) have been identified as the most common of these methods, no study has investigated the development and configuration of these models. We conducted a systematic literature review on the use of ANNs to predict the effluent quality and removal efficiencies of full-scale WWTPs. Three databases were searched, and 44 records of the 667 identified were selected based on the eligibility criteria. The data extracted from the papers showed that the majority of studies used the feedforward neural network model with a backpropagation training algorithm to predict the effluent quality of plants, particularly in terms of organic matter indicators. The findings of this research may help in the search for an optimum design modeling process for future studies of similar prediction problems.
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Affiliation(s)
- Marina Salim Dantas
- Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, MG CEP 31270-901, Brazil E-mail:
| | - Cristiano Christofaro
- Department of Forestry Engineering, Federal University of Jequitinhonha and Mucuri Valleys, Road MG 367, 5000, Diamantina, MG CEP 39100-000, Brazil
| | - Sílvia Corrêa Oliveira
- Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, MG CEP 31270-901, Brazil
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Pattnaik BS, Pattanayak AS, Udgata SK, Panda AK. Machine learning based soft sensor model for BOD estimation using intelligence at edge. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-020-00259-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
AbstractReal-time water quality monitoring is a complex system as it involves many quality parameters to be monitored, the nature of these parameters, and non-linear interdependence between themselves. Intelligent algorithms crucial in building intelligent systems are good candidates for building a reliable and convenient monitoring system. To analyze water quality, we need to understand, model, and monitor the water pollution in real time using different online water quality sensors through an Internet of things framework. However, many water quality parameters cannot be easily measured online due to several reasons such as high-cost sensors, low sampling rate, multiple processing stages by few heterogeneous sensors, the requirement of frequent cleaning and calibration, and spatial and application dependency among different water bodies. A soft sensor is an efficient and convenient alternative approach for water quality monitoring. In this paper, we propose a machine learning-based soft sensor model to estimate biological oxygen demand (BOD), a time-consuming and challenging process to measure. We also propose a system architecture for implementing the soft sensor both on the cloud and edge layers, so that the edge device can make adaptive decisions in real time by monitoring the quality of water. A comparative study between the computational performance of edge and cloud nodes in terms of prediction accuracy, learning time, and decision time for different machine learning (ML) algorithms is also presented. This paper establishes that BOD soft sensors are efficient, less costly, and reasonably accurate with an example of a real-life application. Here, the IBK ML technique proves to be the most efficient in predicting BOD. The experimental setup uses 100 test readings of STP water samples to evaluate the performance of the IBK technique, and the statistical measures are reported as correlation coefficient = 0.9273, MAE = 0.082, RMSE = 0.1994, RAE = 17.20%, RRSE = 37.62%, and edge response time = 0.15 s only.
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Prediction of BOD Concentration in Wastewater Treatment Process Using a Modular Neural Network in Combination with the Weather Condition. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217477] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Since weather has a huge impact on the wastewater treatment process (WWTP), the prediction accuracy for the Biochemical Oxygen Demand (BOD) concentration in WWTP would degenerate if using only one single artificial neural network as the model for soft measurement method. Aiming to solve this problem, the present study proposes a novel hybrid scheme using a modular neural network (MNN) combining with the factor of weather condition. First, discriminative features among different weather groups are selected to ensure a high accuracy for sample clustering based on weather conditions. Second, the samples are clustered based on a density-based clustering algorithm using the discriminative features. Third, the clustered samples are input to each module in MNN, with the auxiliary variables correlated with BOD prediction input to the corresponding model. Finally, a constructive radial basis function neural network with the error-correction algorithm is used as the model for each subnetwork to predict BOD concentration. The proposed scheme is evaluated on a standard wastewater treatment platform—Benchmark Simulation Model 1 (BSM1). Experimental results demonstrate the performance improvement of the proposed scheme on the prediction accuracy for BOD concentration in WWTP. Besides, the training time is shortened and the network structure is compact.
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A Review of the Artificial Neural Network Models for Water Quality Prediction. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10175776] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.
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Xin C, Shi X, Wang D, Yang C, Li Q, Liu H. Multi-grained cascade forest for effluent quality prediction of papermaking wastewater treatment processes. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2020; 81:1090-1098. [PMID: 32541125 DOI: 10.2166/wst.2020.206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The real time estimation of effluent indices of papermaking wastewater is vital to environmental conservation. Ensemble methods have significant advantages over conventional single models in terms of prediction accuracy. As an ensemble method, multi-grained cascade forest (gcForest) is implemented for the prediction of wastewater indices. Compared with the conventional modeling methods including partial least squares, support vector regression, and artificial neural networks, the gcForest model shows prediction superiority for effluent suspended solid (SSeff) and effluent chemical oxygen demand (CODeff). In terms of SSeff, gcForest achieves the highest correlation coefficient with a value of 0.86 and the lowest root-mean-square error (RMSE) value of 0.41. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 46.05% to 50.60%. In terms of CODeff, gcForest achieves the highest correlation coefficient with a value of 0.83 and the lowest root-mean-square error value of 4.05. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 10.60% to 18.51%.
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Affiliation(s)
- Chen Xin
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China E-mail:
| | - Xueqing Shi
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China E-mail:
| | - Dongsheng Wang
- School of Automation, Nanjing University of Posts and Telecommunication, Nanjing 210023, China
| | - Chong Yang
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China E-mail:
| | - Qian Li
- Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Yongin 446701, Korea
| | - Hongbin Liu
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China E-mail:
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Liu ZJ, Wan JQ, Ma YW, Wang Y. Online prediction of effluent COD in the anaerobic wastewater treatment system based on PCA-LSSVM algorithm. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2019; 26:12828-12841. [PMID: 30887455 DOI: 10.1007/s11356-019-04671-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 02/21/2019] [Indexed: 06/09/2023]
Abstract
Since anaerobic wastewater treatment is a nonlinear and complex biochemical process, reasonable monitoring and control are needed to keep it operating stably and efficiently. In this paper, a least-square support-vector machine (LS-SVM) was employed to construct models for the prediction of effluent chemical oxygen demand (COD) in an anaerobic wastewater treatment system. The result revealed that the performance of the steady-state model based on LS-SVM for predicting effluent COD was acceptable, with the maximum relative error (RE) of 11.45%, the mean average percentage error (MAPE) of 0.79% and the root mean square error (RMSE) of 3.08 when training, and the performance fell slightly when testing. Even though, the correlation coefficient value (R) between the predicted value and the actual value of 0.9752 could be achieved, which means this model can predict the variation of effluent COD in general. The dynamic-state models under three kinds of shock loads, which were concentration, hydraulic, and bicarbonate buffer absent, showed good forecasting performance, the correlation coefficient values (R) all excelled 0.99. Among these three shocks, the dynamic LS-SVM model under bicarbonate buffer absent shock achieved the optimal performance and followed by the dynamic-state model under hydraulic shock. This paper provides a meaningful reference to improve the monitoring level of the anaerobic wastewater treatment process.
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Affiliation(s)
- Ze-Jun Liu
- School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, People's Republic of China
| | - Jin-Quan Wan
- School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, People's Republic of China.
- Guangdong Plant Fiber High-Valued Cleaning Utilization Engineering Technology Research Center, Guangzhou, 510640, People's Republic of China.
- Sino-Singapore International Joint Research Institute, Guangzhou, 511356, People's Republic of China.
| | - Yong-Wen Ma
- School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, People's Republic of China
- Guangdong Plant Fiber High-Valued Cleaning Utilization Engineering Technology Research Center, Guangzhou, 510640, People's Republic of China
- Sino-Singapore International Joint Research Institute, Guangzhou, 511356, People's Republic of China
| | - Yan Wang
- School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, People's Republic of China
- Guangdong Plant Fiber High-Valued Cleaning Utilization Engineering Technology Research Center, Guangzhou, 510640, People's Republic of China
- Sino-Singapore International Joint Research Institute, Guangzhou, 511356, People's Republic of China
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Chlorine Soft Sensor Based on Extreme Learning Machine for Water Quality Monitoring. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018. [DOI: 10.1007/s13369-018-3253-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Using Artificial Neural Networks to Solve the Problem Represented by BOD and DO Indicators. WATER 2017. [DOI: 10.3390/w10010004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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