摘要
为减少厂房正常工作时产生的高浓度烟尘对人体造成危害,提出一种基于响应面和改进粒子群的厂房烟尘浓度控制优化方法。首先,用单因素实验测定不同环境下的烟尘浓度,确定温度、气压、相对湿度和风速对烟尘浓度的影响。然后,采用Design Expert软件进行Box-Behnken实验设计,构建厂房环境因素对烟尘影响的实验设计方案,通过响应面分析得到烟尘与环境因素之间的拟合回归方程。最后,用改进的粒子群算法对烟尘与环境因素回归方程进行寻优,确定烟尘浓度最优控制方案。研究结果表明:温度、气压、相对湿度和风速对烟尘浓度的影响显著,烟尘浓度最小值为3.7862 mg/m^(3),对应环境参数为温度26℃、气压970.2 hPa、相对湿度70%、风速1.25 m/s。通过控制厂房内温度、气压、相对湿度和风速,可将烟尘浓度控制在国标范围内,减少厂房高浓度烟尘对作业人员身体造成的危害。
To reduce the harm of high concentrations of smoke and dust generated during the operation of factory buildings to human health,we propose a response surface and particle swarm optimization method for factory smoke and dust concentration control.First,the single factor experiments are employed to determine environmental parameters and the specific range of temperature,relative humidity,wind speed,and air pressure,all of which exert significant impacts on smoke and dust concentration.Temperature,wind speed,relative humidity,and air pressure are selected as optimization parameters,with the concentration of factory smoke and dust as the optimization objective.Then,based on the response surface method,the regression equation between temperature,relative humidity,wind speed,and pressure and smoke concentration is obtained and variance analysis is performed.The regression equation is optimized using particle swarm optimization algorithm.Our results show when the temperature is 26℃,the air pressure is 970.2 hPa,the relative humidity is 70%,and the wind speed is 1.25 m/s,the optimal solution for the objective optimization function of reducing smoke and dust concentration is 3.7862 mg/m^(3).Compared with the traditional response surface method,the combination of particle swarm optimization algorithm is introduced to control the concentration of smoke and dust within the national standards and thus better protect the workers’health.
作者
蔡平
殷雄
余明俊
漆启华
姚道金
CAI Ping;YIN Xiong;YU Mingjun;QI Qihua;YAO Daojin(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China;Hubei Huazhong Electrical Power Development Co.,Ltd.,Wuhan 430000,China)
出处
《重庆理工大学学报(自然科学)》
北大核心
2024年第12期224-231,共8页
Journal of Chongqing University of Technology:Natural Science
基金
国家自然科学基金地区基金项目(52365003)
江西省重点研发计划项目(20212BBE51010)
江西省主要学科学术和技术带头人培养项目(20232BCJ23027)
江西省自然科学基金项目(20232BAB214045)
国家重点研发计划项目(2022YFC3302205)
中央高校基本科研业务费专项(2023RC38)。
