Warning: This page contains copyrighted materials !

 

Power Plant Modeling and Control

1- Steam Turbine model

Over the past 100 years, the steam turbines have been widely employed to power generating due to their efficiencies and costs. With respect to the capacity, application and desired performance, a different level of complexity is offered for the structure of steam turbines. For power plant applications, steam turbines generally have a complex feature and consist of multistage steam expansion to increase the thermal efficiency. It is always more difficult to predict the effects of proposed control system on the plant due to complexity of turbine structure. Therefore, developing nonlinear analytical models is necessary in order to study the turbine transient dynamics. In order to characterize the transient dynamics of steam turbines subsections, in this work, nonlinear mathematical models are first developed based on the energy balance, thermodynamic principles and semi-empirical equations. Then, the related parameters of developed models are either determined by empirical relations or they are adjusted by applying genetic algorithms (GA) based on experimental data obtained from a complete set of field experiments. In the intermediate and low-pressure turbines where, in the sub-cooled regions, steam variables deviate from prefect gas behavior, the thermodynamic characteristics are highly dependent on pressure and temperature of each region. Thus, nonlinear functions are developed to evaluate specific enthalpy and specific entropy at these stages of turbines. The parameters of proposed functions are individually adjusted for the operational range of each subsection by using genetic algorithms.

read more in:

Chaibakhsh, A., Ghaffari, A., Steam turbine model, Simulation Modelling Practice and Theory, 16 (2008) 1145-1162.     

LINK: doi:10.1016/j.simpat.2008.05.017  

SteamTurbine

2- Boiler model

The boiler-turbine modeling has a wide application in power plant control and process study. The dynamics of most power plants are highly nonlinear with numerous uncertainties. Thus, no mathematical model can exactly describe such a complicated physical process, and there will always be modeling errors due to un-modeled dynamics and parametric uncertainties. Besides, detailed modeling of plants dynamics is often not efficient for control synthesis. The plant model should describe the plant dynamics with sufficient accuracy and not describe the microscopic details occurring within individual plant components. In this work, based on the physical rules, thermodynamics principles and energy mass balance, the simulation models are developed and applied to electrical power generating plants in order to characterize the essential dynamic behavior of the boiler subsystems and to use the corresponding models for the power plant processes. These models are developed for a sub-critical once through Benson type boiler based on the experimental data obtained from a complete set of field experiments. An optimization approach based on genetic algorithm (GA) is executed to estimate the model parameters and fit the models response on the real system dynamics. Comparison between the responses of the corresponding models with the response of the real plants validates the accuracy and performance of modeling approach. A similar comparison between the responses of these models with linear parametric models shows the effectiveness and feasibility of the developed model in term of more accurate and less deviation between the responses of the models and the corresponding subsystems.

read more in:

Chaibakhsh, A., Ghaffari, A., Moosavian, S.A.A., A simulated model for a once-through boiler by parameter adjustment based on genetic algorithms, Simulation Modelling Practice and Theory, 15 (2007) 1029-1051.

LINK: doi:10.1016/j.simpat.2007.06.004

 

BoilerModel

3- HRSG model

Developing accurate nonlinear dynamical models for heat recovery steam generator units (HRSG) is presented in this paper. The common nonlinear autoregressive with exogenous input (NARX) system topology was employed to develop the neuro-fuzzy models based on experimental data taken from field experiments. In this structure, the nonlinear behaviours of the HRSG unit can be characterized through interpolation of local linear models associated with different operating regions via fuzzy inference mechanism. The operating regimes were recognized by applying a genetic algorithm (GA) based fuzzy clustering technique to the prepared datasets. The structures of the fuzzy models are defined with respect to the obtained optimal cluster centres and corresponding membership functions. The parameters of fuzzy rules were adjusted by recursive least square estimation method to fit the model responses on real data. The performances of developed models were evaluated by performing a comparison between the model responses and the responses of the real plant. In addition, the stability of the developed models was assessed by perturbing the model from the nominal values to guarantee the models long-term simulation capabilities. A comparison between the responses of the corresponding models and the models obtained from some recent modelling approach was performed to show the advantages of the developed models. The results show the accuracy and reliability of the developed models at transient and steady state conditions.

read more in:

Chaibakhsh, A., Modelling and long-term simulation of a heat recovery steam generator, Mathematical and Computer Modelling of Dynamical Systems, iFirst, 2012, 1-24.

LINK: http://dx.doi.org/10.1080/13873954.2012.698623

 

 

 

 

 

 

Wastewater Treatment Processes (WWTP) Modeling and Control

 

 

 

 

 

 

 

Soft Computing

1- Genetic Algorithm Based Fuzzy Clustering

The GA based fuzzy clustering was implemented in two files named as "anfsgaclstrngx.m" and "anfsgaclstrngencoded.p", in which a simple Picard iteration algorithm is replaced by GA . .

Please write "help anfsgaclstrngx" on the MATLAB command window to see the help.

download files (pass: 1239): HERE

read more in:

Chaibakhsh, A., Modelling and long-term simulation of a heat recovery steam generator, Mathematical and Computer Modelling of Dynamical Systems, iFirst, 2012, 1-24.

LINK: http://dx.doi.org/10.1080/13873954.2012.698623