Fuzzy temperature control of industrial refineries furnaces through combined feedforward/feedback multivariable cascade systems

Abilov, Abdulrza G.; Zeybek, Z.; Tuzunalp, O.; Telatar, Ziya

doi:10.1016/s0255-2701(01)00119-2

Cited by 35 publications

(19 citation statements)

References 8 publications

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“…For such a nonlinear, large delay, large inertia, time-varying and unidirectional rising control object [1,5], it is difficult to establish accurate mathematical model, so the traditional control theory and methods depend on the precise mathematical model is difficult to achieve good ccontrol effect [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Temperature Control System for Electric Heating Furnace

Zhang¹,

Xu²

2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

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Abstract. Accurate and rapid control of the temperature of electric heating furnace in industrial production has important practical significance and application value. In this paper, the electric heating furnace temperature control system is designed based on C51 microcontroller, equipped with temperature sensors, AD conversion module and display module. The incremental PID algorithm is adopted to realize the temperature control of the electric heating furnace and the experimental study for the controlling accuracy and stability of this system has been done, Experiments show that: in the 0-500 ℃ range, electric heating furnace temperature control time is 60s, temperature control relative error of up to 1% and the stability is controlled within 2.3%.

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Section: Introductionmentioning

confidence: 99%

Experimental Study of Temperature Control System for Electric Heating Furnace

Zhang¹,

Xu²

2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

View full text Add to dashboard Cite

show abstract

“…For such a nonlinear, large delay, large inertia, time-varying and unidirectional rising control object [1,3], it is difficult to establish accurate mathematical model, so the traditional control theory and methods depend on the precise mathematical model is difficult to achieve good ccontrol effect [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Design of Temperature Control System for Electric Heating Furnace Based on C51 Microcontroller

Guo¹,

Chu²

2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

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Abstract. Accurate and rapid control of the temperature of electric heating furnace in industrial production has important practical significance and application value. In this paper, the electric heating furnace temperature control system is designed based on C51 microcontroller, equipped with temperature sensors, AD conversion module and display module. The PWM control signal, generated by the microcontroller, is used to controlling the working time of the heating resistance wire and the average power output of the resistance wire. the incremental PID algorithm is adopted to realize the temperature control of the electric heating furnace. Experiments show that: in the 0-500 ℃ range, electric heating furnace temperature control time is 60s, temperature control relative error of up to 1%. The system has the characteristics of low hardware cost, high temperature control accuracy, good reliability and strong anti -interference ability.

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“…Feed-forward control is a model-based control strategy where predictive models are used to anticipate disturbances before they result in a deviation of the process [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

XDEM for Tuning Lumped Models of Thermochemical Processes Involving Materials in the Powder State

Copertaro¹,

Chiariotti²,

Donoso³

et al. 2016

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Abstract. Processes involving materials in gaseous and powder states cannot be modelled without coupling interactions between the two states. XDEM (Extended Discrete Element Method) is a valid tool for tackling this issue, since it allows a coupled CFD-DEM simulation to be run. Such strength, however, mainly finds in long computational times its main drawback. This aspect is indeed critical in several applications, since a long computational time is in contrast with the increasing demand for predictive tools that can provide fast and accurate results in order to be used in new monitoring and control strategies.This paper focuses on the use of the XDEM framework as a tool for fine tuning a lumped representation of the non-isothermal decarbonation of a CaCO3 sample in powder state. The tuning of the lumped model is performed exploiting the multi-objective optimization capability of genetic algorithms.Results demonstrate that such approach makes it possible to estimate fast and accurate models to be used, for instance, in the fields of virtual sensing and predictive control.Keywords: Extended discrete element method, computational fluid dynamics, discrete element method, lumped element modelling, multi-objective model optimization, virtual sensing.

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Fuzzy temperature control of industrial refineries furnaces through combined feedforward/feedback multivariable cascade systems

Cited by 35 publications

References 8 publications

Experimental Study of Temperature Control System for Electric Heating Furnace

Experimental Study of Temperature Control System for Electric Heating Furnace

Design of Temperature Control System for Electric Heating Furnace Based on C51 Microcontroller

XDEM for Tuning Lumped Models of Thermochemical Processes Involving Materials in the Powder State

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