Combustion control of refuse incineration plant by fuzzy logic

Ono, Hitoi; Ohnishi, Takashi; Terada, Yuuzi

doi:10.1016/0165-0114(89)90254-6

Cited by 31 publications

(6 citation statements)

References 2 publications

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“…Advanced fuzzy-logic process control system New solutions to the highly non-linear nature of the incineration process are offered by the use of advanced control methods (Takagi and Sugeno 1985, Onto et al 1989, Raeth 2002. In particular, fuzzy logic, using linguistic variables with membership functions in 'if-then' rules, is an approved methodology to deal with such problems (at least theoretically).…”

Section: The Process Control Systemmentioning

confidence: 99%

Process Control in Municipal Solid Waste Incinerators: Survey and Assessment

Asri

Baxter

2004

Waste Manag Res

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As there is only rare and scattered published information about the process control in industrial incineration facilities for municipal solid waste (MSW), a survey of the literature has been supplemented by a number of waste incineration site visits in Belgium and The Netherlands, in order to make a realistic assessment of the current status of technology in the area. Owing to the commercial character, and therefore, the confidentiality restrictions imposed by plant builders and many of the operators, much of the information collected has either to be presented in a generalized manner, and in any case anonymously. The survey was focused on four major issues: process control strategy, process control systems, monitors used for process control and finally the correlation between the 850 degrees C/2 s rule in the European waste incineration directive and integrated process control. The process control strategies range from reaching good and stable emissions at the stack to stabilizing and maximizing the energy output from the process. The main indicator to be monitored, in cases in which the focus is controlling emissions, is the oxygen content in the stack. Keeping the oxygen concentration in a determined range (usually between 8 and 12 vol.%) ensures stable and tolerated concentrations of the gaseous emissions. In the case for which stabilization of energy production is the principal aim, the main controlled parameter is the steam temperature and flow-rate, which is usually related to the fuel energetic input. A lot of other parameters are used as alarm criteria, the most common of which is the carbon monoxide concentration. The process control systems used most commonly feature partially automated classical proportional integral derivative (PID) controllers. New and innovative process control systems, such as fuzzy-logic control systems, are still unknown to most plant managers while their performance is reported to be unsatisfactory in plants in which such systems have been tested or are in use. Monitoring components used in process control are still based on classical tools such as thermocouples. The use of modern and more reliable sensors is very limited due to the high initial investment cost or simply the fear of using non-standard technologies. Complying with the 850 degrees C/2 s rule in the European waste incineration directive generally is seen to be a handicap for the process control, either in terms of cost, or flexibility of reaction, or both, particularly in old incineration facilities where such restrictions were not planned in the design.

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Section: The Process Control Systemmentioning

confidence: 99%

Process Control in Municipal Solid Waste Incinerators: Survey and Assessment

Asri

Baxter

2004

Waste Manag Res

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“…Subsequently, control systems have been developed based on fuzzy logic applied to the incineration of municipal waste (Tanabe et al, 1996). Presently there are applications that combine the use of models based on fuzzy logic rules with image analysis by means of neural networks have been developed (Jager and Lohf, 1994;Keller and Fick, 1998;Keller and Mü ller, 2000;Keller et al, 2001;Miyamoto et al, 1996;Ono et al, 1989) to control waste incineration pilot plants. In addition, expert systems combined with neural networks have been used for the intelligent control of a rotary lime kiln using the application development environment G2 (Järvensivu and Ruusunen, 1999).…”

Section: Introductionmentioning

confidence: 98%

A Methodological Approach to Knowledge-Based Control and Its Application to a Municipal Solid Waste Incineration Plant

Carrasco

Llauró

Poch

2006

Combustion Science and Technology

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In this work we have developed a knowledge-based combustion control system for a municipal solid waste incinerator plant. A dynamic model for a grate furnace has been proposed to analyse the relationships between the variables and obtain the rules for the knowledge bases. The input variables used were: furnace temperature, furnace pressure drop, power generated and oxygen content in the combustion gases. On the other hand, the output variables were: the actions that the operator should perform for waste feeding, the flow and the combustion air temperature with the aim of controlling the furnace temperature. The support tools used to control the process were abstractors, ALCMEN (Automaticians Language for Causal Modelling for Expert Knowledge) and SimCEES (C þþ Embedded Expert System). To validate them, the real values of the furnace temperature have been compared with those obtained using a simplified combustion mathematical model and those obtained using the expert system.

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“…a linear multiple regression model) to illustrate the consequence part of fuzzy control rules that may increase the realization potential during the fuzzy reasoning process. Ono et al (1989) described the applicability of fuzzy combustion control logic to the MSW incineration plants. Yi et al ( 1990) addressed the use of fuzzy reasoning for controlling an activated sludge process in a wastewater treatment plant.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy controller design for municipal incinerators with the aid of genetic algorithms and genetic programming techniques

Chang

Chen

2000

Waste Manag Res

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Mass bum waterwall incinerator has been the most widely used technology for processing the municipal solid waste (MSW) streams in many metropolitan regions since 1970. Not only the heterogeneity of physical composition and chemical property of the MSW but also the complexity of combustion mechanism would significantly influence the performance of waste incineration. As a result, a successful operation requires an advanced combustion control system for handling many uncertain factors. Conventional automatic combustion control (ACC) technology was found to be insufficient to handle such a highly nonlinear system. The successful applications of those intelligent combustion control (ICC) technologies, such as fuzzy control technology, for reducing the operational risk have received wide attention in recent years. This paper illustrates the principles, algorithms, and application potentials of the genetic fuzzy controller that is particularly designed for improving the traditional fuzzy control logic with the aid of genetic algorithms/genetic programming techniques. Practical implementation of this methodology was assessed by a simulation analysis based on three representative types of mass bum waterwall incinerators in Taiwan. It indicates that the quality of combustion control in those three municipal incinerators can be possibly enhanced via the use of genetic fuzzy control logic. The application of such a hybrid fuzzy control technology can be easily extended to many other types of industrial incinerators, such as modular, rotary kiln, and fluidized bed incinerators, by a slightly different approach.

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Combustion control of refuse incineration plant by fuzzy logic

Cited by 31 publications

References 2 publications

Process Control in Municipal Solid Waste Incinerators: Survey and Assessment

Process Control in Municipal Solid Waste Incinerators: Survey and Assessment

A Methodological Approach to Knowledge-Based Control and Its Application to a Municipal Solid Waste Incineration Plant

Fuzzy controller design for municipal incinerators with the aid of genetic algorithms and genetic programming techniques

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