PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Application of Artificial Intelligence Technique and Multi-Objective Grasshopper Optimization Algorithm for Mine-to-Crusher Optimization in Surface Mines
Version 1
: Received: 9 August 2023 / Approved: 10 August 2023 / Online: 11 August 2023 (08:36:01 CEST)
How to cite:
Hosseini, S.; Monjezi, M. Application of Artificial Intelligence Technique and Multi-Objective Grasshopper Optimization Algorithm for Mine-to-Crusher Optimization in Surface Mines. Preprints2023, 2023080892. https://doi.org/10.20944/preprints202308.0892.v1
Hosseini, S.; Monjezi, M. Application of Artificial Intelligence Technique and Multi-Objective Grasshopper Optimization Algorithm for Mine-to-Crusher Optimization in Surface Mines. Preprints 2023, 2023080892. https://doi.org/10.20944/preprints202308.0892.v1
Hosseini, S.; Monjezi, M. Application of Artificial Intelligence Technique and Multi-Objective Grasshopper Optimization Algorithm for Mine-to-Crusher Optimization in Surface Mines. Preprints2023, 2023080892. https://doi.org/10.20944/preprints202308.0892.v1
APA Style
Hosseini, S., & Monjezi, M. (2023). Application of Artificial Intelligence Technique and Multi-Objective Grasshopper Optimization Algorithm for Mine-to-Crusher Optimization in Surface Mines. Preprints. https://doi.org/10.20944/preprints202308.0892.v1
Chicago/Turabian Style
Hosseini, S. and Masoud Monjezi. 2023 "Application of Artificial Intelligence Technique and Multi-Objective Grasshopper Optimization Algorithm for Mine-to-Crusher Optimization in Surface Mines" Preprints. https://doi.org/10.20944/preprints202308.0892.v1
Abstract
Blasting pattern optimization is an attempt to optimize blast design parameters aiming to achieve optimum fragmentation, reduce mining operating costs as well as environmental side consequences. The present study aims to propose a multi-objective optimization model, employing artificial intelligence and metaheuristics, to simultaneously minimize the mine-to-crusher operating costs and the impact of blasting consequences including fly-rock and back-break. To achieve the purpose of the study, a multi-variable regression model was developed to model total costs from drilling to crushing. In addition to the costs, multilayer perception neural networks were implemented to predict the blast-induced back-break and fly-rock as a function of collected variables such as number of holes, hole length, burden, spacing, hole slope, stemming, blasted rock per hole, powder factor, and charge per delay. High-precise estimations for both back-break and fly-rock were achieved with the average of 99% coefficient of determination for train, test and validation data sets. Then, the developed regression model and the neural networks were used in an optimization framework, employing Grasshopper algorithm, to find the optimum blast design satisfying practical constraints. The proposed model was tested on a lead-zinc open-pit mine, where 1032 blasting patterns were recorded and analyzed. The results of the optimization model provide a Pareto set of solutions, such that any of these solutions can be implemented according to the strategy of the mining operation management team. The blast pattern with the lowest cost, result in a relatively high fly-rock and back-break, while the pattern with low fly-rock and back-break raises the cost by 20.13 % compared the minimum cost blast design.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
