A dynamic model of supercritical boiler-turbine unit based on immune genetic algorithm parameter identification

2021 ◽  
Author(s):  
Guolian Hau ◽  
Zhiyan Tang ◽  
Linjuan Gong ◽  
Huilin Su ◽  
Bo Hu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Dynamic Model ◽  
Turbine Unit ◽  
Immune Genetic Algorithm ◽  
Genetic Algorithm Parameter

scholarly journals Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm

2019 ◽  
Vol 11 (18) ◽  
pp. 5102
Author(s):  
Hongxia Zhu ◽  
Gang Zhao ◽  
Li Sun ◽  
Kwang Y. Lee
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Turbine Unit ◽  
Optimization Method ◽  
System Stability ◽  
Local Model ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Model Network ◽  
Local Model Network

This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

scholarly journals Dynamic Model Parameter Identification and Simulation of SCR Based on Genetic Algorithm§

2015 ◽  
Vol 9 (1) ◽  
pp. 62-66
Author(s):  
Ren Hongjuan ◽  
Lou Diming ◽  
Zhu Jian ◽  
Luo Yiping
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Kinetic Model ◽  
Parameter Identification ◽  
Dynamic Model ◽  
Unknown Parameters ◽  
Exhaust Temperature ◽  
Allowable Error ◽  
Simulation Results ◽  
Model Equations

The Selective Catalytic Reduce (SCR) is studied. The unknown parameters of the SCR kinetic model equations are fitted based on the Genetic Algorithm (GA), which is in the range of the allowable error, compared to the experimental data. Then in AVL Boost software, the simulation results of SCR reaction are obtained. Compared to the test data, the simulation results prove that the parameter identification is effective. At last, the SCR reaction is simulated in AVL Boost, and at the same exhaust temperature, the effect of GHSV and NSR on the SCR reaction is studied.

scholarly journals Parameter Estimation of a Countershaft Brake for Heavy-Duty Vehicles with Automated Mechanical Transmission

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1036
Author(s):  
Yunxia Li ◽  
Lei Li
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
Dynamic Model ◽  
Control Flow ◽  
Mechanical Transmission ◽  
Equivalent Resistance ◽  
Heavy Duty ◽  
Characteristic Parameters ◽  
Resistance Torque ◽  
Heavy Duty Vehicles

A countershaft brake is used as a transmission brake (TB) to realize synchronous shifting by reducing the automated mechanical transmission (AMT) input shaft’s speed rapidly. This process is performed to reduce shifting time and improve shifting quality for heavy-duty vehicles equipped with AMT without synchronizer. To improve controlled synchronous shifting, the AMT input shaft’s equivalent resistance torque and the TB’s characteristic parameters are studied. An AMT dynamic model under neutral gear position is analyzed during the synchronous control interval. A dynamic model of the countershaft brake is discussed, and its control flow is given. The parameter identification method of the AMT input shaft’s equivalent resistance torque is given on the basis of the least squares algorithm. The parameter identification of the TB’s characteristic parameters is proposed on the basis of the recursive least squares method (RLSM). Experimental results show that the recursive estimations of the TB’s characteristic parameters under different duty cycles of the TB solenoid valve, including brake torque estimation, estimation accuracy, and braking intensity estimation, can be effectively estimated. The research provides some reliable evidence to further study the synchronous shifting control schedule for heavy-duty vehicles with AMT.

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