Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods

J Tian, C Qi, Y Sun, ZM Yaseen, BT Pham - Engineering with Computers, 2021 - Springer
Permeability prediction is crucial in shale gas and CO 2 geological sequestration. However,
the intricate pore structure complicates the prediction of permeability. Machine learning (ML) …

[HTML][HTML] Machine learning-based constitutive models for cement-grouted coal specimens under shearing

G Li, Y Sun, C Qi - International Journal of Mining Science and …, 2021 - Elsevier
Cement-based grouting has been widely used in mining engineering; its constitutive law has
not been comprehensively studied. In this study, a novel constitutive law of cement-grouted …

Integrated and intelligent design framework for cemented paste backfill: A combination of robust machine learning modelling and multi-objective optimization

C Qi, Q Chen, SS Kim - Minerals Engineering, 2020 - Elsevier
Modern mining industry thrives for energy-efficient, clean and sustainable mining processes.
The cemented paste backfill (CPB) technology, which may constitute 25–30% of the total …

Towards Intelligent Mining for Backfill: A genetic programming-based method for strength forecasting of cemented paste backfill

C Qi, X Tang, X Dong, Q Chen, A Fourie, E Liu - Minerals Engineering, 2019 - Elsevier
As cemented paste backfill (CPB) plays an increasingly important role in minerals
engineering, forecasting its mechanical properties becomes a necessity for efficient CPB …

Pressure drops of fresh cemented paste backfills through coupled test loop experiments and machine learning techniques

C Qi, Q Chen, X Dong, Q Zhang, ZM Yaseen - Powder Technology, 2020 - Elsevier
In this paper, test loop experiments and machine learning techniques were combined to
investigate pressure drops of fresh cemented paste backfill (CPB) mixes. The influence of …

Pollution level mapping of heavy metal in soil for ground-airborne hyperspectral data with support vector machine and deep neural network: A case study of …

M Wang, C Wang, J Ruan, W Liu, Z Huang… - Environmental …, 2023 - Elsevier
Heavy metal in soil is a significant issue with the urban development in China, and
traditional ground spectra are difficult to satisfy the demands for heavy metal monitoring and …

Predicting the nutrition deficiency of fresh pear leaves with a miniature near-infrared spectrometer in the laboratory

X Jin, L Wang, W Zheng, XD Zhang, L Liu, S Li, Y Rao… - Measurement, 2022 - Elsevier
Nutrient deficiencies often occur during the growth of pear trees; therefore, rapid, cost-
effective monitoring of the nutritional deficiency status of pear leaves is of great value for …

Particulate matter concentration from open-cut coal mines: A hybrid machine learning estimation

C Qi, W Zhou, X Lu, H Luo, BT Pham, ZM Yaseen - Environmental pollution, 2020 - Elsevier
Particulate matter (PM) emission is one of the leading environmental pollution issues
associated with the coal mining industry. Before any control techniques can be employed …

Quantitative analysis of heavy metals in soil via hierarchical deep neural networks with X-ray fluorescence spectroscopy

W Yang, F Li, S Lyu, Q Zhang, Y Zhao - Journal of Analytical Atomic …, 2023 - pubs.rsc.org
Soil is an important source of potentially toxic metal intake for humans through the food
chain. Accurate determination of elemental concentrations in soil is of great significance to …

Prediction of soil-available potassium content with visible near-infrared ray spectroscopy of different pretreatment transformations by the boosting algorithms

X Jin, S Li, W Zhang, J Zhu, J Sun - Applied Sciences, 2020 - mdpi.com
Featured Application Quantitative models for visible near-infrared ray spectroscopy have
rarely been exploited for the measurement of soil-available potassium. These results show …