Generalized predictive control of a thermal plant using fuzzy model

Matko, Drago; Kavšek-Biasizzo, Katarina; Škrjanc, Igor; Mušič, Gasper

doi:10.1109/acc.2000.879562

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…Later, the proposed approach (Aurora et al 2004) not only facilitated the online estimation of time-varying model parameters but also provided additional degrees-of-freedom for obtaining unbiased estimates of measured outputs and offset-free, steady-state controlled responses. Matko et al (2000) presented the generalized predictive control (GPC) of temperature in a thermal power plant. GPC is a Takagi-Sugeno fuzzy model linked to a model-based predictive control algorithm.…”

Section: Review Of Control Of Boilers In a Thermal Power Plantmentioning

confidence: 99%

Modeling, identification, and control of coal-fired thermal power plants

Chandrasekharan

Panda

Swaminathan³

2014

Reviews in Chemical Engineering

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The major portion of total captive power comes from thermal power from coal, oil, natural gas, etc., of which coal-fired units contribute 41% of global electricity. The overall efficiency of such power plants is 33% due to variations in power plant design, use of wide fuel ranges, different steam turbines, and lack in modeling and control configurations. To improve this efficiency, knowledge on material and energy flows across subsystems, including fuel handling, boiler, turbine, and air management need to be known properly. In addition, there is a widespread lack in measurement and instrumentation systems in power plants across the fleet and operators. This scenario makes the operational adjustments more difficult. Again, the strictness in environmental compliances (sulfur and nitrogen components) is met at the cost of limiting thermal efficiency. Moreover, to comply with faster load change, less fuel consumption and more efficient supercritical boilers have been studied. Hence, to improve the efficiency level, more research starting from the state-ofthe-art review on modeling, identification, and control methods of coal-fired boilers and the integrated plant is aimed in this work. This will also address the issue of global climate change and reduction in emission of greenhouse gases.

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Section: Review Of Control Of Boilers In a Thermal Power Plantmentioning

confidence: 99%

Modeling, identification, and control of coal-fired thermal power plants

Chandrasekharan

Panda

Swaminathan³

2014

Reviews in Chemical Engineering

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“…The heat exchanger system is a process that is used to change the temperature distribution of two materials, when they are in direct or indirect contacts [27][28][29][30][31][32]. It has both the inner and the shell tubes with concurrent reactions.…”

Section: Tubular Heat Exchanger System Modelingmentioning

confidence: 99%

An intelligent multiple models based predictive control scheme with its application to industrial tubular heat exchanger system

Mazinan

Sadati

2009

Appl Intell

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The purpose of this paper is to deal with a novel intelligent predictive control scheme using the multiple models strategy with its application to an industrial tubular heat exchanger system. The main idea of the strategy proposed here is to represent the operating environments of the system, which have a wide range of variation in the span of time by several local explicit linear models. In line with this strategy, the well-known linear generalized predictive control (LGPC) schemes are initially designed corresponding to each one of the linear models of the system. After that, the best model of the system and the LGPC control action are precisely identified, at each instant of time, by an intelligent decision maker scheme (IDMS), which is playing the so important role in realizing the finalized control action for the system. In such a case, as soon as each model could be identified as the best model, the adaptive algorithm is implemented on the both chosen model and the corresponding predictive control schemes. In conclusion, for having a good tracking performance, the predictive control action is instantly updated and is also applied to the system, at each instant of time. In order to demonstrate the effectiveness of A.H. Mazinan ( ) Islamic Azad University (IAU), South Tehran Branch, Tehran, Iran

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“…This technique is termed instantaneous linearization technique. It has been successfully applied in many control applications [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Predictive Control of Liquid‐Liquid Extractors Using Neural‐Based Instantaneous Linearization Technique

Mjalli

2006

Chem Eng & Technol

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Nonlinearity of the extraction process is addressed via the application of instantaneous linearization to control the extract and raffinate concentrations. Two feed-forward neural networks with delayed inputs and outputs were trained and validated to capture the dynamics of the extraction process. These nonlinear models were then adopted in an instantaneous linearization algorithm into two control algorithms. The self-tuning adaptive control strategy was compared to an approximate model predictive control in terms of set point tracking capability, efficiency and stability. For the case of large, abrupt set point changes, the performance of the self-tuning algorithm was poor, especially for the raffinate control. The approximate model predictive control strategy was superior to the self-tuning control in terms of its ability to force the output to following the set point trajectory efficiently with smooth controller moves.

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Generalized predictive control of a thermal plant using fuzzy model

Cited by 15 publications

References 9 publications

Modeling, identification, and control of coal-fired thermal power plants

Modeling, identification, and control of coal-fired thermal power plants

An intelligent multiple models based predictive control scheme with its application to industrial tubular heat exchanger system

Adaptive and Predictive Control of Liquid‐Liquid Extractors Using Neural‐Based Instantaneous Linearization Technique

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