scholarly journals Fuzzy modelling of combustion efficiency and control of excess air flow case study: 320-MW steam unit/Isfahan Power Plant/Iran

Clean Energy ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 229-242
Author(s):  
Ahmad Kermani ◽  
Seyed Mohamad Kargar
Keyword(s):  
Genetic Algorithm ◽  
Power Plant ◽  
Fuzzy Model ◽  
Combustion Efficiency ◽  
Fuzzy Rules ◽  
Fuzzy Modelling ◽  
Loop Control ◽  
Factors Affecting ◽  
Excess Air ◽  
Boiler Efficiency

Abstract One of the most critical factors affecting boiler efficiency and hazardous-gas-emission reduction is the volume of excess air mixed with fuel. A knowledge-based approach is proposed to model the efficiency of a 320-MW natural-gas-fired steam power plant in Isfahan, Iran by applying fuzzy-modelling techniques to control the boiler efficiency. This model is based on fuel and air entering the boiler. First, the fuzzy-model structure is identified by applying the fuzzy rules obtained from an experienced human operator. The proposed method is then optimized using a genetic algorithm to increase the fuzzy-model accuracy. The results indicate that, by applying a genetic algorithm, the precision of the proposed fuzzy model increases. The error between the actual efficiency of the plant and the output efficiency of the proposed model is low. This model is developed by applying the fuzzy rules and modelling-related calculations. Finally, to optimize the efficiency of the boiler, a fuzzy proportional-integral controller is designed. The closed-loop control simulations are run by applying both the proposed controller and the manual controller to demonstrate the influence of the suggested method. The simulation outcomes indicate that the recommended controller adjusts the excess-air percentage correctly and increases the unit efficiency by 0.70%, significantly reducing fuel consumption.

The Influence of Excess Air Coefficient of Boiler Efficiency

2013 ◽  
Vol 409-410 ◽  
pp. 548-552
Author(s):  
Jiu Sheng Shi ◽  
Fei Peng ◽  
Bing Wen Zhang
Keyword(s):  
Thermal Efficiency ◽  
Linear Relation ◽  
Combustion Efficiency ◽  
Efficiency Analysis ◽  
Furnace Temperature ◽  
Excess Air ◽  
Important Impact ◽  
Boiler Efficiency ◽  
Excess Air Coefficient ◽  
One To One

Excess air coefficient has an important impact on the combustion conditions of boiler and thermal efficiency, analysis shows that the furnace temperature and the combustion efficiency is the linear relation of one to one correspondence. Any combustion conditions, there is an optimum excess air coefficient makes the top of furnace temperature, thus it can establish a control relationship, furnace temperature is optimization index, excess air coefficient is disturbance.It can achieve the purpose of improving the efficiency of boiler combustion.

Structure Organization of Hierarchical Fuzzy Model Using Genetic Algorithm

1995 ◽  
Vol 7 (1) ◽  
pp. 29-35
Author(s):  
Toshio Fukuda ◽  
◽  
Yasuhisa Hasegawa ◽  
Koji Shimojima
Keyword(s):  
Genetic Algorithm ◽  
Hierarchical Structure ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Numerical Data ◽  
Fuzzy Rules ◽  
Input And Output ◽  
The Hierarchical Structure ◽  
Input Variables ◽  
The Relationship

This paper proposes a method to organize the hierarchical structure of fuzzy model using the Genetic Algorithm and back-propagation method. The number of fuzzy rules increases exponentially with the number of input variables. Thus, a fuzzy system with many input variables has an extremely large number of fuzzy rules. Hierarchical structure of fuzzy reasoning is one of the methods to reduce the number of fuzzy rules and membership functions. However, it is very difficult to organize the hierarchical structure because the hierarchical structure cannot be constructed without considering the relationship among input and output variables. The proposed method can organize the suitable hierarchical structure for the relationship among input and output variables in teaching numerical data. It is based on the Genetic Algorithm with an evaluation function as a strategy that adopts a system with fewer fuzzy rules and more accurate outputs. The proposed method is applied to the approximation problems of multi-dimensional nonlinear functions in order to demonstrate its effectiveness.

A Model for Combustion Losses in a Pulverized Fuel Fired Power Plant Boiler

1988 ◽  
Vol 202 (4) ◽  
pp. 215-224
Author(s):  
V Krishna ◽  
P B Sharma
Keyword(s):  
Particle Size ◽  
Power Plant ◽  
Heat Rate ◽  
Average Particle ◽  
Fuel Particle ◽  
Excess Air ◽  
Power Plant Boiler ◽  
Boiler Efficiency ◽  
Pulverized Fuel ◽  
Plant Boiler

A model for the estimation of combustion losses in a pulverized fuel power plant boiler is presented. The model is based on the formulation of a probability density function which relates the probability of a fuel particle remaining unburnt to the combustion and resident times. An empirical model is also presented which relates the unburnt carbon loss to average particle size and excess air. The two models are shown to be in close agreement with each other. The models are validated from the experiments on a power plant boiler. The dependence of boiler efficiency on particle size and excess air is also examined and an empirical correlation between optimum excess air and particle size is derived. The mechanism of two-way coupling between boiler and turbine side parameters is also illustrated. It has been shown that the optimum excess air levels for maxima in plant heat rate and boiler efficiency are not the same, since the two-way coupling influences both the turbine heat rate and boiler efficiency. The effect of two-way coupling has been found to be more predominant for particle sizes of the order of 200 μm.

Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling

2020 ◽  
Vol 9 (2) ◽  
pp. 31-58
Author(s):  
Sharifa Rajab
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Fuzzy Model ◽  
Fuzzy Rules ◽  
Data Driven ◽  
Similarity Analysis ◽  
Rule Base ◽  
Generalization Capability ◽  
Fuzzy Modelling ◽  
Neuro Fuzzy

Neuro-fuzzy systems based on a fuzzy model proposed by Takagi, Sugeno and Kang known as the TSK fuzzy model provide a powerful method for modelling uncertain and highly complex non-linear systems. The initial fuzzy rule base in TSK neuro-fuzzy systems is usually obtained using data driven approaches. This process induces redundancy into the system by adding redundant fuzzy rules and fuzzy sets. This increases complexity which adversely affects generalization capability and transparency of the fuzzy model being designed. In this article, the authors explore the potential of TSK fuzzy modelling in developing comparatively interpretable neuro-fuzzy systems with better generalization capability in terms of higher approximation accuracy. The approach is based on three phases, the first phase deals with automatic data driven rule base induction followed by rule base simplification phase. Rule base simplification uses similarity analysis to remove similar fuzzy sets and resulting redundant fuzzy rules from the rule base, thereby simplifying the neuro-fuzzy model. During the third phase, the parameters of membership functions are fine-tuned using a constrained hybrid learning technique. The learning process is constrained which prevents unchecked updates to the parameters so that a highly complex rule base does not emerge at the end of model optimization phase. An empirical investigation of this methodology is done by application of this approach to two well-known non-linear benchmark forecasting problems and a real-world stock price forecasting problem. The results indicate that rule base simplification using a similarity analysis effectively removes redundancy from the system which improves interpretability. The removal of redundancy also increased the generalization capability of the system measured in terms of increased forecasting accuracy. For all the three forecasting problems the proposed neuro-fuzzy system demonstrated better accuracy-interpretability tradeoff as compared to two well-known TSK neuro-fuzzy models for function approximation.

Modeling and Experiment on Combustion Characteristics for Biomass Rotary Burner

2020 ◽  
Vol 04 ◽  
Author(s):  
Guohai Jia ◽  
Lijun Li ◽  
Li Dai ◽  
Zicheng Gao ◽  
Jiping Li
Keyword(s):  
Combustion Chamber ◽  
Combustion Temperature ◽  
Combustion Efficiency ◽  
Middle Part ◽  
Combustion Characteristics ◽  
Maximum Temperature ◽  
Excess Air ◽  
Element Simulation ◽  
Excess Air Coefficient ◽  
Secondary Air

Background: A biomass pellet rotary burner was chosen as the research object in order to study the influence of excess air coefficient on the combustion efficiency. The finite element simulation model of biomass rotary burner was established. Methods: The computational fluid dynamics software was applied to simulate the combustion characteristics of biomass rotary burner in steady condition and the effects of excess air ratio on pressure field, velocity field and temperature field was analyzed. Results: The results show that the flow velocity inside the burner gradually increases with the increase of inlet velocity and the maximum combustion temperature is also appeared in the middle part of the combustion chamber. Conclusion: When the excess air coefficient is 1.0 with the secondary air outlet velocity of 4.16 m/s, the maximum temperature of the rotary combustion chamber is 2730K with the secondary air outlet velocity of 6.66 m/s. When the excess air ratio is 1.6, the maximum temperature of the rotary combustion chamber is 2410K. When the air ratio is 2.4, the maximum temperature of the rotary combustion chamber is 2340K with the secondary air outlet velocity of 9.99 m/s. The best excess air coefficient is 1.0. The experimental value of combustion temperature of biomass rotary burner is in good agreement with the simulation results.

scholarly journals Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1036 ◽  
Author(s):  
Xinying Xu ◽  
Qi Chen ◽  
Mifeng Ren ◽  
Lan Cheng ◽  
Jun Xie
Keyword(s):  
Power Plant ◽  
Combustion Process ◽  
Optimization Method ◽  
Combustion Efficiency ◽  
Pollutant Emissions ◽  
Combustion Model ◽  
Nox Emissions ◽  
Coupling Characteristics ◽  
Learning Machine ◽  
Combustion Optimization

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.

Integrated analysis of the excess air on steam power plant CFB boiler using mathematical and CFD modeling

2018 ◽  
Author(s):  
Hariyotejo Pujowidodo ◽  
Ahmad Indra Siswantara ◽  
Budiarso ◽  
Asyari Daryus ◽  
Gun Gun Ramdlan Gunadi
Keyword(s):  
Power Plant ◽  
Cfd Modeling ◽  
Integrated Analysis ◽  
Steam Power ◽  
Cfb Boiler ◽  
Steam Power Plant ◽  
Excess Air

scholarly journals Further results on robust fuzzy dynamic systems with LMI D-stability constraints

2014 ◽  
Vol 24 (4) ◽  
pp. 785-794 ◽  
Author(s):  
Wudhichai Assawinchaichote
Keyword(s):  
Dynamic Systems ◽  
Fuzzy Controller ◽  
Sufficient Conditions ◽  
Fuzzy Model ◽  
Local System ◽  
Linear Matrix ◽  
Fuzzy Modelling ◽  
Ts Fuzzy Model ◽  
Input Noise ◽  
Takagi Sugeno

Abstract This paper examines the problem of designing a robust H∞ fuzzy controller with D-stability constraints for a class of nonlinear dynamic systems which is described by a Takagi-Sugeno (TS) fuzzy model. Fuzzy modelling is a multi-model approach in which simple sub-models are combined to determine the global behavior of the system. Based on a linear matrix inequality (LMI) approach, we develop a robust H∞ fuzzy controller that guarantees (i) the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, and (ii) the closed-loop poles of each local system to be within a specified stability region. Sufficient conditions for the controller are given in terms of LMIs. Finally, to show the effectiveness of the designed approach, an example is provided to illustrate the use of the proposed methodology.

Sign in / Sign up

Export Citation Format

Share Document