Intelligent Control of Flocculation Process Based on Radial Basis Probabilistic Neural Network for Sewage Treatment

Abstract:

The flocculating process of sewage treatment is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. The radial basis probabilistic neural network (RBPNN) has the ability of strong function approach and fast convergence. In this paper, an intelligent optimized control system based on radial basis probabilistic neural network is presented. We constructed the structure of radial basis probabilistic neural network that used for controlling the flocculation process, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. We given the architecture of control system and analyzed the working process of system. In this system, the parameters of flocculation process were measured using sensors, and then the control system can control the flocculation process real-time. The system was used in the sewage treatment plant. The experimental results prove that this system is feasible.

• Periodical: Applied Mechanics and Materials (Volumes 44-47)
• Pages: 3289-3293
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.44-47.3289
• Online since: December 2010
• Authors: Jing Wen Tian, Mei Juan Gao
• Keywords: Control, Flocculation Process, Radial Basis Probabilistic Network, Sewage Treatment

References

[1] Wu Daoji et al., Study of Kinetics Mechanism of flocculation and controlling Index, ENVIRONMENTAL ENGINEERING (2000), pp.22-25.

[2] Shen H, Iha H, Yaguchi S, Tsurugi K, The mechanism by which overexpression of Gts1p induces flocculation in a FLO8-inactive strain of the yeast Saccharomyces cerevisiae. FEMS Yeast Res Vol. 6(2006), pp.914-923.

DOI: 10.1111/j.1567-1364.2006.00086.x

[3] Kaseamchochoung, Chudapak et al., Influence of Chitosan Characteristics and Environmental Conditions on Flocculation of Anaerobic Sludge, Water Environment Research Vol. 78(2006), pp.2210-2216.

DOI: 10.2175/106143005x72830

[4] Kobayashi O, Suda H, Ohtani T, Sone H. Molecular cloning and analysis of the dominant flocculation gene FLO8 from Saccharomyces cerevisiae. Mol Gen Genet Vol. 251(1996), pp.707-715.

DOI: 10.1007/s004380050220

[5] XIANG Zhuang-li. On flocculation control technology. Engineering design and construction Vol. 36(2004), pp.40-42.

[6] Quan Jiping, Huang Xiaodong, Xiao Weigui, et al., Comparison between FCD and SCD two automic feeding systems. Industrial Water Treatment Vol. 25(2005), pp.72-74.

[7] Jiao Licheng. Neural network system theory (Xi an electronic science and technology university press, Xian 1995).

[8] Zhao Zhenning, Xu Yongmao. Introduction to fuzzy theory and neural networks and their application (Tsinghua University Press, Beijing 1996).

[9] Li Shiyong. Fuzzy Control Neurocontrol and Intelligent Cybernetics (Harbin Institute of Technology publishing house, Harbin 1996).