Prediction and validation of parameters of multi-axis soil remediation equipment based on machine learning algorithms--颗粒学报PARTICUOLOGY

a Institute of Process Equipment and Environmental Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819, China
b The School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
c School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan, 030024, China

10.1016/j.partic.2025.01.007

Volume 98, March 2025, Pages 41-55

Received 10 December 2024, Revised 18 January 2025, Accepted 21 January 2025, Available online 9 February 2025, Version of Record 13 February 2025.

E-mail: wangyz@me.neu.edu.cn; tongzhu@mail.neu.edu.cn

Highlights

• Machine learning and response surface methodology study of soil-agent mixing characteristics.

• Predictive performance of five machine learning models is compared.

• Improved mixing homogeneity in a new nine-axis in-situ soil remediation equipment.

• Provides reference values for the design of multi-axis soil remediation equipment.

Abstract

To facilitate the recycling of polluted soils, the development of innovative multi-axial soil remediation machinery is essential for achieving a uniform blend of soil with remediation chemicals. The mean level of the steepest climb test was set using the mean level derived from the orthogonal test, and then the range of optimum values was determined based on the results of the steepest climb test, and the upper and lower bound intervals of the response surface test were set accordingly. The most optimal model is identified by applying machine learning algorithms to the response surface data. The results show that the Decision Tree model outperforms Random Forest, SVR, KNN and XG Boost in terms of accuracy and stability in predicting dual indicators. Analysis of the decision tree model yields the following optimal parameter settings: homogenisation time of 1.7 s, homogenisation spacing of 181 mm, crusher spacing of 156 mm, and speed of 113 rpm. In the final test prototype, the error rates of the machine learning prediction models were 3.01% and 3.88% respectively. The experimental data confirms that the prediction accuracy reaches a satisfactory level after applying machine learning to optimise the parameters. This study will provide a reference for the design and optimisation of new in situ multi-axial soil remediation devices.

Graphical abstract

Keywords

Contaminated soil; Mixing homogeneity; Machine learning; Steepest climb test; Nine-axis

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