Processes 2023, 11, 381. https://doi.org/10.3390/pr11020381 www.mdpi.com/journal/processes Article Geometallurgical Detailing of Plant Operation within Open-Pit Strategic Mine Planning Aldo Quelopana 1,2 , Javier Órdenes 2 , Rodrigo Araya 3 and Alessandro Navarra 2, * 1 Department of Systems and Computer Engineering, Universidad Católica del Norte, 0610 Angamos, Antofagasta 1270709, Chile 2 Department of Mining and Materials Engineering, Faculty of Engineering, McGill University, 3610 University Street, Montreal, QC H3A 0C5, Canada 3 SNC Lavalin Inc., Mining and Metallurgy, 445 René-Lévesque Blvd. West, Montreal, QC H2Z 1Z3, Canada * Correspondence: alessandro.navarra@mcgill.ca Abstract: Mineral and metallurgical processing are crucial within the mineral value chain. These processes involve several stages wherein comminution is arguably the most important due to its high energy consumption, and its impact on subsequent extractive processes. Several geological properties of the orebody impact the efficiency of mineral processing and extractive metallurgy; scholars have therefore proposed to deal with the uncertain ore feed in terms of grades and rock types, incorporating operational modes that represent different plant configurations that provide coordinated system-wide responses. Even though these studies offer insights into how mine plan- ning impacts the ore fed into the plant, the simultaneous optimization of mine plan and metallurgi- cal plant design has been limited by the existing stochastic mine planning algorithms, which have only limited support for detailing operational modes. The present work offers to fill this gap for open-pit mines through a computationally efficient adaptation of a strategic mine planning algo- rithm. The adaptation incorporates a linear programming representation of the operational modes which forms a Dantzig-Wolfe decomposition, nested within a high-performing stochastic mine planning algorithm based on a variable neighborhood descent metaheuristic. Sample calculations are presented, loosely based on the Mount Isa deposit in Australia, in which a metallurgical plant upgrade is evaluated, showing that the upgraded design significantly decreases the requirement on the mining equipment, without significantly affecting the NPV. Keywords: open-pit mine planning; metallurgical plant; geometallurgy; stochastic optimization; metaheuristics; linear programming 1. Introduction The design and redesign of a metallurgy plant is not a trivial decision for a mining company since it significantly impacts its mineral value chain. It is known from general system theory that separate-optimized subsystems do not guarantee an entire-optimized system [1]; therefore, the analysis of any change in a plant must be addressed strategically from a holistic point of view by supporting internal optimization and addressing the in- terdependency between adjacent processes. This challenging decision must consider sev- eral risks such as geology complexity (complex geometric shapes of deposits, deep-seated deposits, geotechnical challenges, etc.), lower ore grades, ore quality variability (textural complexities, mineralogical variability, etc.), and large production volumes [2]. Comminution is essential to mineral processing and metallurgical extraction because it liberates the sought-after minerals needed for their subsequent selective recovery through processes such as flotation and leaching [3,4]. However, comminution consumes a considerable amount of energy, in the order of 50–80% of the mine site [5,6], as so much of this energy is dissipated as heat into the rock. Moreover, some decades ago, scholars Citation: Quelopana, A.; Órdenes, J.; Araya, R.; Navarra, A. Geometallurgical Detailing of Plant Operation within Open-Pit Strategic Mine Planning. Processes 2023, 11, 381. https://doi.org/10.3390/ pr11020381 Academic Editor: Haiping Zhu Received: 4 January 2023 Revised: 20 January 2023 Accepted: 23 January 2023 Published: 26 January 2023 Copyright: © 2023 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https://cre- ativecommons.org/licenses/by/4.0/).