In this paper,data fusion and optimization are performed on the collected raw field data from a thermal power plant,and the collected dust samples are analyzed for particle size using an LS-909 laser particle size analyzer.This paper describes the process of establishing a dataset for a thermal power plant dust environment,with a capacity of 28 780.The weights of the dataset are then optimized using the golden jackal optimization algorithm.The optimal initial weights for the limit learning machine are determined based on the solution results.The limit learning machine is then trained using the sample data to create a dust concentration prediction model.Compared with the field data collected,the model shows an error rate of 0.72％.The dust concentration prediction algorithm can predict the dust concentration after 1 minute per iteration.By repeating the iteration,the dust concentration after a longer time interval can be obtained.However,the longer the time interval,the lower the accuracy of the prediction results.The model can more accurately predict the dust concentration within 30 minutes.In addition,the Ansys Fluent platform is used to simulate the movement and distribution of dust in the thermal power plant.The results show a high risk of dust explosion in the area downwind of the dust leakage source,as well as a high concentration of dust in areas where personnel are present.The simulation results are extracted from the CFD-Post software,and relevant data,such as the concentration,movement velocity,and distribution height of dust particles,are derived from 30 randomly selected positions in the flow field.These results are then compared and analyzed with the dust concentration prediction model established in this paper.The validation results show that the dust concentration prediction model has good accuracy,with a relative error of 2.1％.It can effectively predict the dust concentration in areas downwind of the leakage source,as well as areas with a high concentration of dust.Overall,the model can accurately predict the dust environment in the thermal power plant.
关键词
安全工程/粉尘防爆/粉尘危害/粉尘质量浓度在线检测/工厂环境/劳动者安全/危险预知
Key words
safety engineering/protection against dust explosion/dust hazards/on-line detection of dust mass concentration/factory environment/worker safety/hazard prediction
维普
万方数据