Research On Automatic Control For Feeding Materials And Air Of Biomass Heating Boilers

With China's rapid economic development, urbanization countries develop and people's living standard continues to improve, and how to improve the heating quality of urban residents will be attract more and more people's attention.Currently, the main cities in North China are mainly using coal-burning heating still, and this will consume a large amount of coal resources. Under the background of the increasing tension in the world's energy, facing with the existing conventional energy depletion and the rising coal prices each year, using biomass briquettes of this low-cost, efficient fuel for heating has become the inevitable trend; what's more,conventional energy use produces a lot of"CO2", a huge threat to the global environment, and therefore, biomass briquettes "zero discharge" of advantages will help alleviate the damage to the environment of human.This article is based on "National Spark Program (2007EA105008)" scientific research projects to study the basis of existing transformation of biomass heating boilers, which is able to feed materials with air volume control, to not only save fuel, but also improve the heating efficiency. Through the analysis of the distribution on the combustion velocity of the wind in the biomass briquette combustion, the heat of fuel combustion are calculated, and the selection of feeding were calculated; the central heating boiler biomassfeed and air distribution control system design program is provided; design the hardware and software of the lower computer PLC control system. According to the difficulties in the mathematical model of the boiler combustion system and the large and unsteady heat transfer delay characteristics, this paper uses the RBF(Radial Basis Function) neural network as a controller algorithm; based on the learning algorithm in the study, the Fusion of the clustering algorithm, orthogonal least squares learning algorithm and the incremental gradient descent method are applied to the RBF neural networkcontrol system. Verify the feasibility of this incremental algorithm through the simulation by using MATLAB.There has a lot of research focusing on biomass heating boilers in the country.This is the first time to utilize RBF neural network to feed the biomass boiler heating system with air control, and to train the neural network. The simulation results have achieved the expected goal, which provide automated operationgood guidance and reference in the future of biomass heating boilers.