@article{ZHANG2026, 
author = {Cong-rui ZHANG and Jia-lin BAI and Hao-yu WANG and Cheng CHEN and Ji-min LI and Lang QIU and Zhong ZHENG and Gao-feng REN and Liang ZHAO},
title = {Optimization of Blasting Parameters based on Multi-process Collaboration under Low-carbon Objectives},
year = {2026},
journal = {BLASTING},
volume = {43},
number = {2},
pages = {44-57},
keywords = {rock fragmentation, blasting parameter optimization, carbon emission reduction, multi-objective optimization, orthogonal experiment},
url = {https://www.sciopen.com/article/10.3963/j.issn.1001-487X.2026.02.005},
doi = {10.3963/j.issn.1001-487X.2026.02.005},
abstract = {Given the pressing demand for eco-friendly and low-carbon development in mining operations, decarbonizing the blasting techniques has emerged as a pivotal challenge. The effectiveness of rock fragmentation critically determines the ore size distribution, thereby exerting a decisive influence on the energy requirements of subsequent processing stages. Using Chengchao Iron Mine as a case study, this study developed a stope production carbon emissions model that quantitatively correlates D50 particle size with critical operational parameters, including drilling power consumption, blasting explosive usage, and haulage equipment energy demand. By integrating carbon emission coefficients for associated energy and materials, the research systematically quantified process-wide carbon emissions influenced by fragmentation performance, ultimately determining 32.55 cm as the optimal D50 particle size for minimizing carbon emissions. Subsequently, sixteen groups of orthogonal experiments were designed by varying blasthole length, stemming length, and toe spacing. A fluid-solid coupling algorithm was implemented to characterize the dynamic constitutive behavior of formations. Building on this foundation, ANSYS/LS-DYNA simulations were conducted to analyze the distribution of blast-induced fractures across various design schemes. Grayscale processing and binarization were applied to simulated fracture patterns to enhance rock block boundary contrast, followed by an adaptive multi-scale Canny algorithm for precise extraction of fragment-fracture interfaces. Finally, the boulder yield, fines fraction, and D50 particle-size distribution for each experimental configuration were statistically analyzed to enable precise calculation of associated carbon emission intensities. Simulation data analysis reveals that carbon emissions across the 16 schemes range from 1.4391 kg CO2/t to 1.6296 kg CO2/t, with a pronounced inverse relationship between the oversize fragment proportion and fine ore generation efficiency. Subsequently, a fragmentation prediction model was developed using a PSO-ELM algorithm based on the experimental datasets. The NSGA-Ⅱ optimization method was employed to refine blasting parameters, yielding an optimal configuration that simultaneously minimizes carbon emissions and enhances fragmentation performance: a 166 m blasthole length, a 21.6 m stemming length, and a 2.0 m toe spacing. This configuration achieves a carbon emission intensity of 1.43617 kg CO2/t, with an oversize fragment ratio of 18.83926% and a fine ore production rate of 17.28788%. The results confirm that the developed collaborative optimization approach substantially reduces whole-process carbon emission intensity during stope production while maintaining consistent operational efficiency. This research provides both a measurable technical framework that combines sustainable transformation with intelligent control to achieve the “dual carbon” target and actionable implementation guidelines for industrial practice.}
}