Boiler Modelling and Optimal Control of Steam Temperature in Power Plants

Valsalam, S Rominus; Anish, S; Singh, Batwinder

doi:10.3182/20090705-4-sf-2005.00024

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…This design chooses 4-5-3 BP neural network structure. Because the three coefficients must be positive, the activation function of the neurons in the output layer is a non-negative 5 . The formula is as follows:…”

Section: -2 Algorithm Of Pid Control System Based On Bp Neural Networkmentioning

confidence: 99%

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Simulation of Main Steam Temperature Control System Based on Neural Network

Dai¹,

Yan²,

Wei³

et al. 2018

JRNAL

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This paper designs a PID control system based on the BP neural network. The control system can adjust three adjustable parameters of PID controller through BP neural network algorithm. The main steam temperature control system of the thermal power plant was taken as the object, and simulation analysis was performed through MATLAB. The experimental results show that the design scheme has good performance and anti-interference ability, and has certain practicability to the main steam temperature control system of thermal power plant.

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Section: -2 Algorithm Of Pid Control System Based On Bp Neural Networkmentioning

confidence: 99%

“…Select the performance index function E(k), our goal is to minimize E (k): (5) Use gradient descent method to correct neural network weight coefficient:…”

Section: -2 Algorithm Of Pid Control System Based On Bp Neural Networkmentioning

confidence: 99%

Simulation of Main Steam Temperature Control System Based on Neural Network

Dai¹,

Yan²,

Wei³

et al. 2018

JRNAL

View full text Add to dashboard Cite

show abstract

“…The constraints defined in the OCL file, are reflected in the ultimate code that is generated. Many control systems like the steam temperature control system of the boiler component of thermal power plant have been developed using genetic algorithms [36]. Many works have been carried out in coal-mill modeling.…”

Section: Code Generationmentioning

confidence: 99%

Fault Detection of Constrained Object Models Using Object Modeling Techniques

Priya¹

2018

INDJCSE

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This paper proposes a new efficient methodology for fault detection from the structural modeling of a complex system using the UML class diagram. The crisp and precise constraints on the attributes and relationships are represented using the formal specification language, Object Constraint Language (OCL). Model verification is done by converting the model to Constraint Satisfaction Problem(CSP) and by solving the problem. Performance is analysed by both bounded and unbounded validation.

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“…In the same way, Krishna et al [19] correlated the variables using statistical models for predicting output in degradation of pollutants and analyzed the models. Valsalam et al [20] developed mathematical model for the 210 MW coal-fired boiler for which MPC controller was designed to control the steam from superheater. A general investigation on modeling, identification and control of the coal fired thermal power plants are provided by Sreepradha et al [21] .…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling of an integrated boiler for coal fired thermal power plant

Chandrasekharan

Panda

Swaminathan

2017

Heliyon

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The coal fired thermal power plants plays major role in the power production in the world as they are available in abundance. Many of the existing power plants are based on the subcritical technology which can produce power with the efficiency of around 33%. But the newer plants are built on either supercritical or ultra-supercritical technology whose efficiency can be up to 50%. Main objective of the work is to enhance the efficiency of the existing subcritical power plants to compensate for the increasing demand. For achieving the objective, the statistical modeling of the boiler units such as economizer, drum and the superheater are initially carried out. The effectiveness of the developed models is tested using analysis methods like R2 analysis and ANOVA (Analysis of Variance). The dependability of the process variable (temperature) on different manipulated variables is analyzed in the paper. Validations of the model are provided with their error analysis. Response surface methodology (RSM) supported by DOE (design of experiments) are implemented to optimize the operating parameters. Individual models along with the integrated model are used to study and design the predictive control of the coal-fired thermal power plant.

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Boiler Modelling and Optimal Control of Steam Temperature in Power Plants

Cited by 14 publications

References 4 publications

Simulation of Main Steam Temperature Control System Based on Neural Network

Simulation of Main Steam Temperature Control System Based on Neural Network

Fault Detection of Constrained Object Models Using Object Modeling Techniques

Statistical modeling of an integrated boiler for coal fired thermal power plant

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