Research On Optimization Of Dust-cleaning Performance Of Bag Filter Based On Surrogate Model

Bag filter has the characteristics of high dust removal efficiency and safe operation,and it has been widely used in industrial flue gas dedusting.Dust-cleaning is a process of filtration and regeneration of the bag filter,and its performance is directly related to whether the bag filter can maintain stable operation for a long time.However the process of dust-cleaning is short,and factors that have complex coupling relationships affect the dust-cleaning performace.At present,the methods for optimizing the performance of cleaning are mainly experimental methods and numerical simulation methods.However,the experimental method has a long cycle and high cost,it is difficult to cover the whole working condition,which makes the numerical simulation method gradually become the main method for the research of cleaning performance.But,the numerical simulation focuses on the influence of single parameters on the cleaning performance.The multi-parameter coupling optimization is inefficient and it is difficult to obtain global optimization results.In recent years,the optimization technology based on surrogate model has attracted people’s attention because of its high efficiency and high adaptability.In this paper,the optimization technology based on surrogate model was introduced into the field of bag filter to solve the problem of optimization of dust-cleaning performance.The main research work is listed as follows:1.The complexity of the dust-cleaning performance of bag filter and the deficiency of existing research methods for dust-cleaning performance were analyzed.An optimization method for dust-cleaning performance of bag filter based on surrogate model was proposed to solve the problem of nonlinear and multi-parameter coupling.The optimization method solved the problem by constructing a proxy model first,and then using an optimization algorithm to perform optimization to obtain a global optimal solution.2.The experimental design method was studied for the optimization of the dust-cleaning performance of the bag filter.The Latin hypercube design was used to select sample points,and it saved the number of trials.The numerical simulation of the cleaning process was studied,the numerical simulation model was established,and the response value of the sample points was obtained.Different model construction methods were studied and the proxy model between the cleaning parameters and the optimization target was constructed by the sample points and corresponding response values.Based on the surrogate model,the global optimization of the cleaning parameters by genetic algorithm was studied,and the best combination of cleaning parameters was obtained.3.On the comprehensive experimental platform of the bag filter,the dust-cleaning optimization experiment module was expanded,the software and hardware system was developed.The cleaning experiment scheme was designed,and the proposed optimization method for dust-cleaning performance of bag filter based on surrogate model was tested.The feasibility and effectiveness of the optimization method were verified.This paper proposed an optimization method for dust-cleaning performance of bag filter based on surrogate model.Compared with experimental methods and numerical simulation methods.The method can effectively solve the nonlinear and multi-parameter coupling problems under the complicated working conditions,can significantly shorten the development cycle,reduce the experimental cost,and lay a foundation for the optimization and innovative research and development of the bag filter.