Modeling and control of a chemical waste water treatment plant

Miller, Randy; Itoyama, K.; Uda, Akifumi; Takada, Haruo; Bhat, N.P.

doi:10.1016/s0098-1354(97)87624-7

Cited by 12 publications

(5 citation statements)

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“…El-Din [8] developed ANN model(s) to predict SS and COD for effluent from a full-scale primary sedimentation tank using flow and influent SS and COD as input parameters. A simplified hybrid neural net approach was applied for the modeling and subsequent analysis of a chemical wastewater treatment plant to reduce the occurrences of overflow in the clarifier caused by filamentous bulking and, thereby, increases wastewater treatment capacity [9]. However, neural network models are unable to determine the validity of data.…”

Section: Introductionmentioning

confidence: 99%

Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks

Luo

et al. 2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

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Effluent ammonia-nitrogen (NH3),chemical oxygen demand (COD) and total nitrogen (TN) removals are the most common environmental and process performance indicator for all types of wastewater treatment plants (WWTPs). In this paper, a soft computing approach based on the back propagation (BP) neural networks and fuzzy-rough sets (FR-BP) has been applied for forecasting effluent NH3-N, COD and TN concentration of a real WWTP, in which the fuzzy-rough sets theory is employed to perform input selection of neural network which can reduce the influence due to the drawbacks of BP such as low training speed and easily affected by noise and weak interdependency data. The model performance is evaluated with statistical parameters and the simulation results indicates that the FR-BP modeling approach achieves much more accurate predictions as compared with the other traditional modeling approaches.

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Section: Introductionmentioning

confidence: 99%

Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks

Luo

et al. 2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

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“…Walsh and Perkins (1994) designed an integrated process control system for wastewater neutralization processes. Miller et al (1997) conducted research on the control of a chemical wastewater treatment plant through dynamic simulation. More recently, Moreno and Buitron (1998) developed a respirometry-based optimal control system for an aerobic bioreactor that is used for the treatment of industrial wastewater.…”

Section: Point Sourcesmentioning

confidence: 99%

Barriers to sustainable water-quality management

Huang

Xia

2001

Journal of Environmental Management

139

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“…Hence, many alternative schemes and operational strategies can be evaluated without the need for physical trials of each scenario (Thalla et al 2010;Pai et al 2011). By simulating the performance assessment models using suitable influential variables, one can rapidly respond to any changes in the processes and devise operational strategies to shift the plant to new operating conditions which improves its stability, the quality of the effluent and at the same time achieve reduction in the running costs (Miller et al 1997;Nair et al 2016;Kumar and Saravanan 2009). Several deterministic, stochastic and time series-based models have been developed for predicting the performance of WWTPs, (Guo et al 2014;Ráduly et al 2007;Denai et al 2004;Erdirencelebi and Yalpir 2011;González et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater

2017

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The current work demonstrates the support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) modeling to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant. The influent variables such as pH, chemical oxygen demand, total solids (TS), free ammonia, ammonia nitrogen and Kjeldahl Nitrogen are used as input variables during modeling. Model development focused on postulating an adaptive, functional, realtime and alternative approach for modeling the removal efficiency of Kjeldahl Nitrogen. The input variables used for modeling were daily time series data recorded at wastewater treatment plant (WWTP) located in Mangalore during the period June 2014-September 2014. The performance of ANFIS model developed using Gbell and trapezoidal membership functions (MFs) and SVM are assessed using different statistical indices like root mean square error, correlation coefficients (CC) and Nash Sutcliff error (NSE). The errors related to the prediction of effluent Kjeldahl Nitrogen concentration by the SVM modeling appeared to be reasonable when compared to that of ANFIS models with Gbell and trapezoidal MF. From the performance evaluation of the developed SVM model, it is observed that the approach is capable to define the interrelationship between various wastewater quality variables and thus SVM can be potentially applied for evaluating the efficiency of aerobic biological processes in WWTP.

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Modeling and control of a chemical waste water treatment plant

Cited by 12 publications

References 1 publication

Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks

Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks

Barriers to sustainable water-quality management

Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater

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