Intelligent system for improving dosage control

Coagulation is one of the most important processes in a drinking-water treatment plant, and it is applied to destabilize impurities in water for the subsequent flocculation stage. Several techniques are currently used in the water industry to determine the best dosage of the coagulant, such as the jar-test method, zeta potential measurements, artificial intelligence methods, comprising neural networks, fuzzy and expert systems, and the combination of the above-mentioned techniques to help operators and engineers in the water treatment process. Current paper presents an artificial neural network approach to evaluate optimum coagulant dosage for various scenarios in raw water quality, using parameters such as raw water color, raw water turbidity, clarified and filtered water turbidity and a calculated Dose Rate to provide the best performance in the filtration process. Another feature in current approach is the use of a backpropagation neural network method to estimate the best coagulant dosage simultaneously at two points of the water treatment plant. Simulation results were compared to the current dosage rate and showed that the proposed system may reduce costs of raw material in water treatment plant.

[1]  G. Annadurai,et al.  Floc Characteristics and Removal of Turbidity and Humic Acid from High-Turbidity Storm Water , 2003 .

[2]  Shang-Lien Lo,et al.  Effects of data normalization and inherent-factor on decision of optimal coagulant dosage in water treatment by artificial neural network , 2010, Expert Syst. Appl..

[3]  K. Mostafa,et al.  Optimization of conventional water treatment plant using dynamic programming , 2015, Toxicology and industrial health.

[4]  Robert C. Andrews,et al.  The application of artificial neural networks for the optimization of coagulant dosage , 2011 .

[5]  Fn Ogwueleka,et al.  OPTIMIZATION OF DRINKING WATER TREATMENT PROCESSES USING ARTIFICIAL NEURAL NETWORK , 2009 .

[6]  Joao P. Hespanha,et al.  Multi-model adaptive control of a simulated pH neutralization process , 2007 .

[7]  Dongsheng Wang,et al.  pH modeling for maximum dissolved organic matter removal by enhanced coagulation. , 2012, Journal of environmental sciences.

[9]  James K. Edzwald,et al.  Selection of alum and polyaluminum coagulants: principles and applications , 2006 .

[10]  S. Heddam,et al.  ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study , 2012, Environmental Monitoring and Assessment.

[11]  Dayong Luo,et al.  Application of an expert system using neural network to control the coagulant dosing in water treatment plant , 2004 .

[12]  Shang-Lien Lo,et al.  Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system , 2008, Eng. Appl. Artif. Intell..

[13]  Thierry Denoeux,et al.  A neural network-based software sensor for coagulation control in a water treatment plant , 2001, Intell. Data Anal..

[14]  Dong-Jin Choi,et al.  The effects of data preprocessing in the determination of coagulant dosing rate , 2000 .

[15]  S. Strugholtz,et al.  Artificial neural networks for cost optimization of coagulation, sedimentation and filtration in drinking water treatment , 2008 .

[16]  Hua Li,et al.  Machine learning approaches to predict coagulant dosage in water treatment plants , 2013, International Journal of System Assurance Engineering and Management.