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饲料粉碎过程工艺参数多目标优化设计OACSTPCD

Multi-objective optimization design of feed crushing process parameters

中文摘要英文摘要

试验旨在对饲料粉碎过程工艺参数进行优化设计,以提高粉碎机工作性能.试验以生产率、吨料电耗为优化目标,以主轴转速、筛网孔径、物料含水率、喂料量和回料管直径为优化变量,采用响应面试验原理(BBD)设计试验并构建数据集;基于反向传播神经网络算法(BPNN)和粒子群优化算法(PSO-BPNN)建立饲料粉碎过程工艺参数的多 目标优化综合模型;结合快速精英多 目标遗传算法(NSGA-Ⅱ)进行多 目标寻优,得到Pareto解集;通过CRITIC-TOPSIS评价模型对符合实际生产要求的Pareto解集筛选.结果发现,经过PSO优化的BP神经网络算法误差指标更小、预测精度更高,粉碎机的生产率、吨料电耗、粒度的平均优化幅度分别达到了 61.07%、38.58%、54.31%;目标寻优后粉碎机最佳工艺参数组合为主轴转速2 697 r/min、筛网孔径5.8 mm、物料含水率10%、喂料量19.3 kg/min、回料管直径67 mm.研究表明,经优化设计后,粉碎机生产率增加了 5.92%,吨料电耗降低了 2.29%.

The purpose of the experiment was to optimize the process parameters of feed crushing,so as to improve the working performance of the crusher.Productivity and power consumption per ton of material were studied as optimization objectives.With spindle speed,screen diameter,material moisture content,feed rate,and return pipe diameter as optimization variables,the response surface test principle(BBD)was used to design the test and construct the data set.Based on Back-Propagation Neural Network(BPNN)and Particle Swarm Optimization-Back-Propagation Neural Network(PSO-BPNN),the multi-objective optimization model of feed crushing process parameters was established.The Non-dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ)was used for multi-objective optimization,and the Pareto solution set was obtained.Through CRITIC-TOPSIS evaluation model,Pareto solution sets meeting the actual production requirements were screened.The results show that the error index of the BP neural network algorithm optimized by PSO is smaller and the prediction accuracy was higher,and the average optimization range of the pulverizer's productivity,power consumption per ton of material and particle size reached 61.07%,38.58%,and 54.31%,respectively.After the target optimization,the optimal process parameters of the crusher were combined with spindle speed of 2 697 r/min,screen aperture of 5.8 mm,material moisture content of 10%,feeding capacity of 19.3 kg/min,and diameter of return pipe of 67 mm.The results show that the productivity of pulverizer is increased by 5.92%and the power consumption per ton is reduced by 2.29%.

李春东;周杨;曹丽英;白永强;王跃

内蒙古科技大学工程训练中心,内蒙古包头 014010内蒙古科技大学机械工程学院,内蒙古包头 014010内蒙古第一机械集团有限公司科研所,内蒙古包头 014032

畜牧业

锤片式粉碎机工艺参数多目标优化多目标决策

hammer millprocess parametersmulti-objective optimizationmulti-objective decision

《饲料研究》 2024 (005)

127-132 / 6

内蒙古自治区自然科学基金资助(项目编号:2021 MS05065、2022MS05030);内蒙古自治区高等学校青年科技英才支持计划资助(项目编号:NJYT23046)

10.13557/j.cnki.issn1002-2813.2024.05.024

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