Application of RBF Network Based on Immune Algorithm to Predicting of Wastewater Treatment

Ye, Hongtao; Luo, Fei; Xu, Yuge

doi:10.1007/978-3-642-01513-7_133

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Although the current SSTP situation satises the standards imposed, the current processes need to be closely monitored to ensure that the SSTP development will not signicantly increase environmental and public health risks in Kuching. As the study carried out by Ye, Luo, and Xu (2009) 13 showed, effluent quality is the most important criterion of a wastewater treatment plant. In this study, an INA-based SSTP was developed to investigate the compliance of effluent discharge with the standards and monitoring requirements.…”

Section: Methodsmentioning

confidence: 99%

Prediction analysis of effluent removal in a septic sludge treatment plant: a biomimetics engineering approach

Chun

Malek

Ismail

2014

Environ. Sci.: Processes Impacts

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Effluent discharge from septic tanks is affecting the environment in developing countries. The most challenging issue facing these countries is the cost of inadequate sanitation, which includes significant economic, social, and environmental burdens. Although most sanitation facilities are evaluated based on their immediate costs and benefits, their long-term performance should also be investigated. In this study, effluent quality-namely, the biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS)-was assessed using a biomimetics engineering approach. A novel immune network algorithm (INA) approach was applied to a septic sludge treatment plant (SSTP) for effluent-removal predictive modelling. The Matang SSTP in the city of Kuching, Sarawak, on the island of Borneo, was selected as a case study. Monthly effluent discharges from 2007 to 2011 were used for training, validating, and testing purposes using MATLAB 7.10. The results showed that the BOD effluent-discharge prediction was less than 50% of the specified standard after the 97(th) month of operation. The COD and TSS effluent removals were simulated at the 85(th) and the 121(st) months, respectively. The study proved that the proposed INA-based SSTP model could be used to achieve an effective SSTP assessment and management technique.

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Section: Methodsmentioning

confidence: 99%

Prediction analysis of effluent removal in a septic sludge treatment plant: a biomimetics engineering approach

Chun

Malek

Ismail

2014

Environ. Sci.: Processes Impacts

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“…Neural networks played an important role in solving these problems. The model has been widely applied to various fields of mathematics, engineering, economics [22,33,34]. It can model any nonlinear system to a high degree of accuracy by adjusting the network parameters and uses the steepest descent method to search for the optimal solution.…”

Section: Radical Basis Function (Rbf) Neuralmentioning

confidence: 99%

“…Xu et al employed the artificial immune algorithm to calculate the optimal setting value of the control variables [21]. Ye et al combined the RBF network and immune algorithm to establishing the wastewater treatment process models [22]. Guan et al and Wang et al proposed the soft-sensing method for predicting the quality parameters of wastewater treatment [23,24].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

Zou

Wang

2019

Mathematical Problems in Engineering

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Wastewater treatment plant (WWTP) is the energy-intensive industries. Energy is consumed at every stage of wastewater treatment. It is the main contributor to the costs of WWTP. Analysis and forecasting of energy consumption are critical to energy-saving. Many factors influence energy consumption. The relationship between energy consumption and wastewater is complex and challenging to identify. This article employed the fuzzy clustering method to categorize the sample data of WWTP and analyzed the relationship between energy consumption and the influence factors in different categories. The study found that energy efficiency in various categories was changed and the same influence factors in different types had different influence intensity. The Radial Basis Function (RBF) neural network was used to forecast energy consumption. The data from the complete set and categories was adopted to train and test the model. The results show that the RBF model using the date from the subset has better performance than the multivariable linear regression (MLR) model. The results of this study provided an essential theoretical basis for energy-saving in WWTP.

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Dissolved oxygen generic model control of wastewater treatment process based on immune optimization least squares support vector machine

Luo

2010

2010 8th World Congress on Intelligent Control and Automation

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Application of RBF Network Based on Immune Algorithm to Predicting of Wastewater Treatment

Cited by 4 publications

References 3 publications

Prediction analysis of effluent removal in a septic sludge treatment plant: a biomimetics engineering approach

Prediction analysis of effluent removal in a septic sludge treatment plant: a biomimetics engineering approach

Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

Dissolved oxygen generic model control of wastewater treatment process based on immune optimization least squares support vector machine

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