عباس برآبادی

The mining industry operates in a complex and dynamic environment and faces many challenges that can negatively affect sustainable development goals. To avoid these effects, mining needs to adopt strategic decisions. Therefore, it requires effective decision-making processes for resource optimization, operational efficiency, and sustainability. Multicriteria decision-making methods (MCDM) have been considered valuable decision-support tools in the mining industry. This article comprehensively examines MCDM methods and their applications in the mining industry. This article discusses the basic principles and concepts of MCDM methods, including the ability to prioritize and weigh conflicting, multiple criteria and support decision-makers in evaluating diverse options. According to the results, 1579 MCDM articles in mining have been published from the beginning to April 15, 2023, and a scientometric analysis was done on these articles. In another part of this article, 19 MCDM methods, among the most important MCDM methods in this field, have been examined. The process of doing work in 17 cases of the reviewed methods is presented visually. Overall, this paper is a valuable resource for researchers, mining industry professionals, policymakers, and decision-makers that can lead to a deeper understanding of the application of MCDM methods in mining. By facilitating informed decision-making processes, MCDM methods can potentially increase operational efficiency, resource optimization, and sustainable development in various mining sectors, ultimately contributing to mining projects' long-term success and sustainability.

Implementing maintenance protocols for industrial machinery is essential since a well-thought-out plan may support and improve machinery dependability, production quality, and safety precautions. Implementing a maintenance plan that considers the equipment's actual functional behavior and the effects of failures will be easier and more practical. Engineers must consider environmental conditions when studying in hostile environments such as mine. The major goal of this study is to create a mining equipment maintenance program that is as effective as possible while incorporating risk and performance indicators and taking environmental factors into account. The study uses the “reliability-centered maintenance” method, which combines the reliability operating index and risk. The Cox model also includes the risk factors associated with environmental conditions in the reliability analysis. The proposed approach was implemented in a 5-758 Komatsu dump-truck case study at the Sungun copper mine in Iran. The reliability-centered maintenance approach is implemented for dump-truck in three scenarios based on risk factors: 1- baseline, 2- First semi-annual, cheap maintenance, and 3- second semi-annual, expensive maintenance. All failure modes are low-risk, making corrective maintenance appropriate. In Scenario 1, electrical-electrical, electrical-start, mechanical, and pneumatic-related failures are low-risk, making corrective maintenance suitable. In Scenario 2, corrective maintenance is recommended for pneumatic-related failure. In Scenario 3, the fuel-related failure has a high criticality number and failure intensity, indicating a high-risk situation. Time-based preventive maintenance is the most appropriate strategy for this scenario.

The Remaining Useful Life (RUL) valuation of mining machinery is a principal to ensure the production/output and customer satisfaction in the mining zone. In many cases, it may be of attention to know the expected value of the remaining life of the item before it fails from an arbitrary time that known RUL. The system's failure is also evaluated with the reliability index, which describes up-times. An individual unit's reliability during field use is essential in many mining equipment applications. This index, especially in industrial systems, and being affected by the internal condition also affects operating environmental conditions. For example, the loader performance in cold weather will be different from that of warm, which will affect the machine's reliability and thus the RUL. In reliability engineering, operating environmental conditions are considered "Risk factors or Covariates". Therefore, in this paper, an approach is proposed first to analyze the system's reliability considering covariates' effect and then estimate the RUL for different scenarios. The proportional hazard model was used in reliability analysis to be realistic and take the operational influencing factors in calculation. Methods are presented for calculating the reliability function and computing the RUL as a function of the current conditions to guarantee the desired output. The remaining useful life estimation of a Komatsu PC-1250 from the Sungun copper mine was evaluated as a case study of this approach. Systems operating environmental factors such as shift, dump-truck kind, rock kind, … (known as covariates) are assumed to influence covariate in this context. As a result, the Weibull proportional hazard model was fitted to describe the failure behavior, and the RUL of four selected scenarios was evaluated. Presented results can be used, e.g., for developing operational performance, planning of maintenance activities, spare parts provision, and the profitability of the owner of an asset.

The Residual Useful Life (RUL) provides an estimate of the amount of remaining useful life before a system failure depends on present conditions and past operating profiles. In this paper, RUL is estimated based on reliability and for convergence to more realistic results, the effect of operating environment conditions in the form of risk factors (covariates) is also involved in the analysis. These environmental conditions are sometimes tangible and "Observable" and sometimes it is not possible to determine and include them in the analysis and are analyzed under the heading of “Un-observed risk factors” using the frailty model. RUL analysis of a case study of the loading system (Shovel Komatsu 1250) of the Sungun copper mine, based on 8-months information, has shown that Weibull mix proportional hazard (W-MPHM) has the best fit on the data. In this model, four factors including shift risk, rock kind, load system type and rainfall with risk rates of 2.66, 3.79, 0.204 and 1.18 have been obtained as the most effective factors. System RUL became zero in the next two scenarios in about 40 hours. Comparing W-MPHM and Ex-PHM over 80 hours showed that the concurrence of the reliability curve in the second model is greater than the first model and in fact, the reliability decline rate is higher at the beginning of the range. This result reflects the impact of intangible risk factors on system performance analysis, regardless of which will result in significant inconsistency of the results.