Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal

Kennedy, Marla J.; Gandomi, Amir H.; Miller, Christopher M.

doi:10.1016/j.jece.2015.10.010

Cited by 53 publications

(15 citation statements)

References 43 publications

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“…An ANN has the ability to learn and model non-linear and complex relationships. Several studies have been carried out on the prediction of the coagulant dose for particular WTP [9][10][11][12][13][14][15]. RBFNNs and GRNNs have shown good performance capabilities for predicting residual chlorine in WTP [16].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks

Kote

Wadkar²

2019

Eng. Technol. Appl. Sci. Res.

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Coagulation and chlorination are complex processes of a water treatment plant (WTP). Determination of coagulant and chlorine dose is time-consuming. Many times WTP operators in India determine the coagulant and chlorine dose approximately using their experience, which may lead to the use of excess or insufficient dose. Hence, there is a need to develop prediction models to determine optimum chlorine and coagulant doses. In this paper, artificial neural networks (ANN) are used for prediction due to their ability to learn and model non-linear and complex relationships. Separate ANN models for chlorine and coagulant doses are explored with radial basis neural network (RBFNN), feed-forward neural network (FFNN), cascade feed forward neural network (CFNN) and generalized regression neural network (GRNN). For modeling, daily water quality data of the last four years are collected from the plant laboratory of WTP in Maharashtra (India). In order to improve performance, these models are established by varying input variables, hidden nodes, training functions, spread factor, and epochs. The best models are selected based on the comparison of performance measures. It is observed that the best performing chlorine dose model using defined statistics is found to be RBFNN with R=0.999. Similarly, the CFNN coagulant dose model with Bayesian regularization (BR) training function provided excellent estimates with network architecture (2-40-1) and R=0.947. Based on the above models, two graphical user interfaces (GUIs) were developed for real-time prediction of chlorine and coagulant dose, which will be useful for plant operators and decision makers.

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Section: Introductionmentioning

confidence: 99%

Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks

Kote

Wadkar²

2019

Eng. Technol. Appl. Sci. Res.

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“…In addition, Bello et al, [6] suggested various predictive control models for coagulant dose based on a blurred switching strategy. In the coagulation method in Akron WTP, Ohio, USA, Kennedy et al, [7] have assessed ANN models in predicting organic dissolved matter and turbidity. In both seawater and brackish waters, Gao Larry et al [8] have created a real-time dose strategy using ultrafiltration therapy.…”

Section: Introductionmentioning

confidence: 99%

Application of Feed Forward Neural Network for Prediction of Optimum Coagulant Dose in Water Treatment Plant

Kote*¹,

Wadkar²

2019

IJITEE

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Coagulation is anecessary process used mainly to reduce turbidity and natural organic matter in water treatment. The dosage of coagulant required is conventionally determined by carrying out jar tests which consume time and chemicals.In India, coagulant dose in a WTP remains constant during certain periods due to delay in jar testing, which may lead to under-dosing or over-dosing of coagulant. This research work is focused on applying artificial neural network (ANN) approach to predict coagulant dose in a WTP. Forty-eight months daily water testing data concerning inlet & outlet water turbidity and coagulant dose were obtained from the plant laboratory for ANN modelling. The appropriate architecture of feed forward neural network (FFNN) coagulant models were established with several steps of training and testing by applying various training algorithms vizLevenberg-Marquardt (LM) and Bayesian regularization (BR), resilient back propagation (RBP), one step secant(OSS),variants of conjugate gradient(CG) and modifications of gradient descent (GD) with evaluating coefficient of correlation (R) & mean square error (MSE). Further, best performed LM and BR training algorithm were used for development of four ANN models of FFNN for prediction of coagulant dose at WTP. FFNN coagulant model with BR training algorithm provided excellent estimates with network architecture (2-50-1) for coagulant dose with maximum value of R= 0.943 (training) and R = 0.945 (testing). Thus, ANN provided an effective diagnosing tool to understand the non-linear behavior of the coagulation process, and can be used as a valuable performance assessment tool for plant operators and decision makers.

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“…The use of artificial neural networks (ANN) has obtained popularity in modeling of coagulation process in WTP (Gagnon et al 1997;Robenson et al 2009;Kennedy et al 2015). ANNs have a great potential for representing nonlinear complex processes without structural knowledge of the processes.…”

Section: Introductionmentioning

confidence: 99%

“…Griffiths and Andrews (2011a) developed both process and inverse process seasonal ANN models to predict settled water turbidity and optimal alum dosage at Elgin Area WTP, Canada. Kennedy et al (2015) evaluated four different hybrid ANN process models for predicting turbidity and dissolve organic matter removal during coagulation process using daily full-scale data at Akron WTP, Ohio, USA.…”

Section: Introductionmentioning

confidence: 99%

Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

Kim

Parnichkun

2017

Appl Water Sci

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Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system (kmeans-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of kmeans-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

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Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal

Cited by 53 publications

References 43 publications

Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks

Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks

Application of Feed Forward Neural Network for Prediction of Optimum Coagulant Dose in Water Treatment Plant

Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

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