Optimisation of parameters of a dual-axis soil remediation device based on response surface methodology and machine learning algorithm  

作　　者：Zhipeng Wang Tong Zhu Youzhao Wang Feng Ma Chaoyue Zhao Xu Li Yanping Zhang 

机构地区：[1]Institute of Process Equipment and Environmental Engineering,School of Mechanical Engineering and Automation,Northeastern University,Shenyang,110819,China

出　　处：《Particuology》2025年第1期26-43,共18页颗粒学报(英文版)

基　　金：funding from the National Key Research and Development Plan of China(grant No.2020YFC1806402);the Shenyang Science and Technology Plan Project(grant No.20–202–4–37).

摘　　要：To accelerate the recycling of black soil,it is necessary to develop a new type of soil remediation equipment to improve its working efficiency.The one-way test was used to determine the mean level value of the steepest climb test,and the combined equilibrium method was used to determine the upper and lower interval levels of the response surface test for parameter optimisation.Based on the results of the response surface indices,machine learning was performed and the optimal model was determined.The results show that the predictive ability and stability of the decision tree model for the two indicators are better than that of random forest and support vector machine.The optimal parameter combinations determined using the decision tree model are:speed 73 rpm,homogenisation pitch 183 mm,homogenisation time 1 s,descent speed 0.06 m/s.The error between the optimal value of the machine learning prediction model and the actual simulation is 1.1%and 5.72%,respectively.The results of the study show that the effect of optimizing the parameters through machine learning achieves a satisfactory prediction accuracy.

关 键 词：Black soil Machine learning Mixing homogeneity Discrete element method Response surface methodology 

分 类 号：X53[环境科学与工程—环境工程]