Integrated Artificial Neural Network and Discrete Event Simulation Framework for Regional Development of Refractory Gold Systems

Abstract

Mining trends in the gold sector indicate a growing imbalance in global supply and demand chains, especially in light of accelerated efforts towards industrial electrification and automation. As such, it is important that research and development continue to focus on processing options for more complex and refractory ores. Unlike conventional (i.e., free-milling) ore feeds, refractory gold is not amenable to standard cyanidation, and requires additional pretreatment prior to leaching and recovery. With recent technological advancements, such as sensor-based ore sorting, there is opportunity to advance the development of smaller untapped refractory resources with marginal economics, particularly those in proximity to processing infrastructure within major gold districts. However, it will be critical that the necessary tools are developed to capture the potential system-wide effects caused by varied ore feeds and improve related decision-making processes earlier in the value chain. Discrete event simulation (DES) is a powerful computational technique that can be used to monitor the interactions between important processes and parameters in response to random natural variations; the approach is thus suitable for the modelling of complex mining systems that deal with significant geological uncertainty. This work implements an integrated artificial neural network (ANN) and DES framework for the regional coordination of conventional and preconcentrated refractory gold ores to be processed at a centralized plant. Sample calculations are presented that are based on a generated dataset reflective of sediment-hosted refractory gold systems.

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

“…Modern computational methods underlying neural networks include discrete event simulation, which has made it possible, based on the sensors' swarm, to coordinate processes in the development of depleted gold deposits with a relatively low metal content and profitability compared to rich gold deposits [78]. Furthermore, deep learning based on lightweight convolutional neural networks allowed for the timely detection of the damage of machines and equipment by analyzing a large number of images (up to 100) every second with a test accuracy of 93.22% when integrating MobileNet and Yolov4 networks [79].…”

Technology Upgrade Assessment for Open-Pit Mines through Mine Plan Optimization and Discrete Event Simulation

“…These modes represent processing configurations for the plant by which it receives the determined capacity to react to geological uncertainty while not violating any constraints (purpose of the second sums set of Equation ( 1)). This approach has led the efforts of several investigations in the medium-term using the DES method [21,22,[44][45][46] and more recently, the longer term [41]. This latter study depicts the tactical alternation of modes over periods by embedding a linear programming formulation into a parallelized implementation of the variable neighborhood descent (VND) metaheuristic [47].…”

“…The simulation of extended operating periods allows for the identification of potential deficiencies or bottlenecks in coordinating unit processes within the target system. DES models are thus proper risk assessment and decision-making tools in any mining system [45]. The framework designed for the present study receives the ore blocks determined by the long-term planning algorithm (after passing through the ore sorter if implemented) along with the parameters previously defined, such as the operational modes, metallurgical recovery, metal price, maximum processing capacity, and associated cost.…”

Sensor-Based Ore Sorting—A Review of Current Use of Electromagnetic Spectrum in Sorting

“…Recently some review articles have published in the same area [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] creating an exchange platform for cost effective engineering solutions in sensor based ore sorting. More beneficiation and advanced techniques for minerals like copper, coal, and diamond have also been proposed in [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], and [36], but none of these approaches intently reviews a generalized approach to sensor based ore sorting through the application of the interaction of electromagnetic waves with matter.…”