GA-based fuzzy neural controller design for municipal incinerators

The successful operation of mass burn municipal incinerators for solid waste management involves many uncertain factors. Not only the physical composition and chemical property of waste streams but also the complexity of combustion mechanism would significantly influence the performance of waste incineration. Due to the rising concerns of dioxin/furan emissions from those incineration facilities in the metropolitan region, the applications of fuzzy control technologies for reducing the operational risks have gradually received wide attention in the scientific community. Recent advances of intelligent combustion control (ICC) technologies indicate that hybrid fuzzy control schemes, integrating some ideas and paradigms existing in different soft computing approaches, may provide more reliable control performance of combustion process. The proposed integrated methodology, using genetic algorithms and neural networks as tools to aid in fuzzy logic control, therefore employs a three-stage analysis. It applies three soft computing approaches simultaneously for generating a representative state function, searching for a set of multi-objective control strategies, and auto-tuning the fuzzy control rule base for use in the case study. The findings from this research clearly indicate that the control performance at least in three types of municipal incinerators (i.e., patented stokers are Takuma, Volund, and Martin) is greatly enhanced via the use of ICC technologies. By using this integrated methodology, the case study not only verifies the applicability and suitability for controlling municipal incinerators but also presents application potentials for controlling many other types of industrial incinerators, such as modular, rotary kiln, and fluidized bed incinerators.

[1]  Kazuyuki Suzuki,et al.  Stable Combustion in Sludge Melting Furnace by Fuzzy Logic Control , 1993 .

[2]  H. Ishigami,et al.  Structure optimization of fuzzy neural network by genetic algorithm , 1995 .

[3]  Kumpati S. Narendra,et al.  Neural Networks In Dynamical Systems , 1990, Other Conferences.

[4]  T. Fukuda,et al.  Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm , 1995 .

[5]  Ni-Bin Chang,et al.  A chemometric approach for the verification of dioxin/furan formation mechanism in municipal waste incinerators , 1996 .

[6]  Miyamoto Yuichi,et al.  Dynamic characteristic analysis and combustion control for a fluidized bed incinerator , 1996 .

[7]  L. P. Holmblad,et al.  The FLS application of fuzzy logic , 1995 .

[8]  Takanori Shibata,et al.  Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives , 1997 .

[9]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Miyamoto Yuichi,et al.  Dynamic Characteristic Analysis and Combustion Control for a Fluidized Bed Incinerator , 1996 .

[11]  C. L. Karr,et al.  Fuzzy control of pH using genetic algorithms , 1993, IEEE Trans. Fuzzy Syst..

[12]  Ni-Bin Chang,et al.  Prediction of PCDDs/PCDFs emissions from municipal incinerators by genetic programming and neural network modeling , 2000 .

[13]  Kenji Baba,et al.  Integration of Multi AI Paradigms for Intelligent Operation Support Systems – Fuzzy Rule Extraction from a Neural Network , 1993 .

[14]  ANDREAS BASTIAN,et al.  Selection of input variables for model identification of static nonlinear systems , 1996, J. Intell. Robotic Syst..

[15]  Charles L. Karr,et al.  Genetic algorithms for fuzzy controllers , 1991 .

[16]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[17]  Wu Zhi Qiao,et al.  A rule self-regulating fuzzy controller , 1992 .

[18]  Bryn V. Williams.  Evolutionary neural networks : models and applications , 1995 .

[19]  H. Ono,et al.  Combustion control of refuse incineration plant by fuzzy logic , 1989 .

[20]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[21]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[22]  Ni-Bin Chang,et al.  Statistical modelling for the prediction and control of PCDDs and PCDFs emissions from municipal solid waste incinerators , 1995 .

[23]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[24]  Derek A. Linkens,et al.  Fuzzy-neural control: principles, algorithms and applications , 1995 .

[25]  Charles L. Karr,et al.  Design of a cart-pole balancing fuzzy logic controller using a genetic algorithm , 1991, Defense, Security, and Sensing.

[26]  M. Sugeno,et al.  ErratumFuzzy modelling and control of multilayer incinerator , 1988 .