Abstract
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.
Highlights
Coal-fired power plants have the characteristics of high power, stability, low cost and short construction period, so they occupy a leading position in many countries around the world
Comparing with the 412 mg/m3 of NOx emissions and 93.39% of boiler combustion efficiency under the initial conditions, after optimization, the NOx emissions are reduced by 19.8% and the boiler combustion efficiency is improved by 0.45%, which is in line with the purpose of reducing NOx emission and improving boiler combustion efficiency
Boiler combustion efficiency under the initial conditions, after optimization, the NOx emissions are reduced by 19.8% and the boiler combustion efficiency is improved by 0.45%, which is in line with the purpose of reducing NOx emission and improving boiler combustion efficiency
Summary
Coal-fired power plants have the characteristics of high power, stability, low cost and short construction period, so they occupy a leading position in many countries around the world. In the thermal power generation industry, there are a series of problems such as low boiler combustion efficiency and serious pollutant emissions. With the continuous improvement of statistical research and the rise of artificial intelligence, support vector machine (SVM) and BP neural network (BPNN) have been widely used in coal-fired boiler modeling [3,4,5]. In these applications, because of the quadratic programming characteristics of support vector machine, it is mainly suitable for small sample data modeling, while BPNN has the shortcomings of falling into a local
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