Energy Efficiency Optimization Of Horseshoe Flame Glass Kiln Based On Improved Particle Swarm Optimization Algorithm

Glass melting furnace is the core equipment of glass production,and its melting effect directly affects its product quality and enterprise production cost.However,the traditional fusion technology is mainly based on artificial experience to adjust the r elated process parameters.This method is not only inefficient,but also difficult to meet the diversified needs of glass production.At present,the research on the combination of energy consumption and quality during the melting process is very little.In addition,the study on the quality of clarification in the process of melting process mostly ignored the change of the radius of the bubble during the movement,and the flow law of the production flow in the environment.Therefore,the study of the energ y consumption and quality of the horseshoe flame glass furnace has become a very valuable topic at the present stage.In this paper,the energy consumption and the quality of the clarification process are studied.And then an energy consumption optimization model with clarified quality constraints is set up with the top temperature to arch,excess air coefficient and fuel flow as the independent variable,and the adaptive particle swarm optimization algorithm is improved to solve this model.Finally,the optimization of energy consumption simulation system of horseshoe flame glass furnace is set up by MATLAB.The main contents of this paper are as follows:1.The structure and working principle of the horseshoe flame glass tank kiln are analyzed,and its energy consumption model is established by combining the energy consumption and expenditure of each structure,and the key influencing factors of the thermal efficiency are obtained from this model.2.By analyzing the defects in the process of glass production,the main defects in the melting process are the quality defects of the bubbles,and th is quality defects are located in the clarified area with the flow law and process requirements of the glass flow.After that the relationship between the bubble radius and the clarification temperature and the relationship between the air surplus coefficient and the temperature are applied to the analysis of the gas bubble escape law,and the relationship between the temperature and the viscosity is applied to the study of the movement law of the production flow.And then a clarification quality model is put forward by combining the escaping law of the bubble and the movement law of the production flow.And from this model the key factors for evaluating the quality of the clarification and the range of clarification factor under different quality grades are analyzed.3.Aiming at the stagnation state of particle swarm optimization in multi-objective optimization,the fitness variance of convergence factor an d particle are used as indexes to evaluate evolution state.It is used to distinguish whether the distribution of particles is aggregation or dispersion when the algorithm stagnates,and adaptively adjust the integral inertia weight and other parameters according to different evolutionary states.And then based on the theory proposed in the second and third chapters,an energy consumption optimization model under the quality constraint is established,and then we combine the multiplicative penalty function with this improved particle swarm optimization algorithm to solve this model.At last,the obtained solution is compared with the actual production data.The results show that the optimized combination of param eters improves the thermal efficiency significantly in the case of ensuring the quality of the product.Based on the model and method mentioned above,the simulation system of energy consumption optimization for glass furnace of horseshoe flame is built by MATLAB simulation platform,which provides theoretical support and technical reference for the energy consumption optimization of the glass furnace of horseshoe flame glass.