Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry

Abstract

The implementation of artificial intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with autonomous trucks. Artificial intelligence, machine learning, and autonomous technologies provide many economic benefits for the mining industry through cost reduction, efficiency, and improving productivity, reducing exposure of workers to hazardous conditions, continuous production, and improved safety. However, the implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This article discusses the current status of AI, machine learning, and autonomous technologies implementation in the mining industry and highlights potential areas of future application. The article presents the results of interviews with some of the stakeholders in the industry and what their perceptions are about the threats, challenges, benefits, and potential impacts of these advanced technologies. The article also presents their views on the future of these technologies and what are some of the steps needed for successful implementation of these technologies in this sector.

Safety Measurements and Risk Assessment of Coal Mining Industry Using Artificial Intelligence and Machine Learning

“…What are the factors which make mining threatening? Poor lighting, inadequate working space, toxic gases, radioactive materials, dust scraps from metals, poor air supply, unstable roofs, and use of explosives (Hyder, Siau, & Nah, 2019). In 2019, the number of fatalities in China was 248, on the other hand, the number in USA was reduced to 12 which is 20 times less than China and 96 in India which is 2.58 times as many as that of China.…”

“…These trucks can work 24/7 to increase the efficiency of operations (Dyson, 2017). Artificial Intelligence and Machine learning can produce a modish revolution to the mining industry by using intelligent systems (Hyder, Siau, & Nah, 2019). Singh et al (2016) presented a real time monitoring system using AI and WSNs.…”

A Comprehensive Review on Internet of Things (IoT) and its Implications in the Mining Industry

“…Mine engineers have to review the applicability of these novel methods and educate operating crews accordingly. Several studies introduced and discussed a specific modern technique using recent technologies such as AI and its challenges at a particular stage of mining (Ghasemi et al, 2014;Muduli et al, 2018;Ghaychi Afrouz and Westman, 2018;Chakravorty, 2019;Hyder et al, 2019;McGaughey, 2020). Few studies also specifically introduced IoT for a single task in mining such tailing dam monitoring (Sun et al, 2012), coal mine monitoring system (Hu et al, 2013;Bo et al, 2014;Zhou et al, 2017), positioning systems in coal mines (Liu et al, 2010;Liu and Liu, 2014), ventilation and gas monitoring (Qin et al, 2011;Qian et al, 2016;Gillies et al, 2004;Jo and Khan, 2018), roof support (Singh et al, 2018), maintenance and machinery safety (Atkins et al, 2010;Zhang et al, 2014;McNinch et al, 2019).…”

“…Few studies also specifically introduced IoT for a single task in mining such tailing dam monitoring (Sun et al, 2012), coal mine monitoring system (Hu et al, 2013;Bo et al, 2014;Zhou et al, 2017), positioning systems in coal mines (Liu et al, 2010;Liu and Liu, 2014), ventilation and gas monitoring (Qin et al, 2011;Qian et al, 2016;Gillies et al, 2004;Jo and Khan, 2018), roof support (Singh et al, 2018), maintenance and machinery safety (Atkins et al, 2010;Zhang et al, 2014;McNinch et al, 2019). Hyder et al (2019) investigated the application of modern techniques in automation of mining industry; nevertheless, the challenges of applications of IoT are not evaluated in the literature. This study has a unique perspective on reviewing the general application of IoT in different stages of mining considering its challenges and probable feasible solutions.…”

Machine Learning—A Review of Applications in Mineral Resource Estimation

“…Since 1993, many authors have been exploring the potential of ML in resource estimation, resulting in several research publications. Even though the implementation of artificial intelligence and autonomous technologies in the mining industry began decades ago [26,27], it was not until 1993 that ML applications in mineral resource estimation gained enormous research interest. Zhang et al [28] noted that ML improves resource estimation in the following ways: (i) samples that are rejected in conventional resource estimates because they do not satisfy all quality control requirements can be used provided that the geological descriptions and measurements are reliable; and (ii) resource estimation block models can be constructed using fewer assays and more geology, leading to a reduction in operational costs.…”