The primary aim of this study was to define the Final pit boundaries utilizing the maximum flow Pseudoflow method in an open-pit mining context. Our methodology encompassed exploratory data analysis (EDA), establishing geomechanical and economic factors, and assessing the Final pit. The study was conducted using Python 3.11 and SGeMS 3.0. We discovered that our block model comprised 480,000 blocks of 10x10x10 m dimensions. We generated 20 pits with revenue factors between 0.1 to 2, increasing by increments of 0.1. The Study indicated that pit 20 was optimal, with an estimated NPV of 17855 MUSD, extracting 212 million tons of ore and 58 million tons of waste rock, achieving a stripping ratio based on block model and market conditions, and is subject to change with further block sequencing analysis. Nevertheless, pit 20 emerged as the most advantageous when considering economic feasibility, given its high estimated NPV and favorable stripping ratio.

The Sungun copper deposit is located in about 40km North West of Ahm city in the east Azarbaigan province of Iran. The total geological ore deposit is estimated to be 740 million tons. In this study using all information from exploration and geotechnical studies of the area, Final pit limits of Sungun copper deposit is designed by the 2-D Lerchs & Grossmann Algorithm. By this method, from 740 million tons of geological ore reserve, 411 million tons can be mined by open-pit method with overall stripping ratio of 2.14:1. Cut off grade is found to be 0.32% Cu. The average ore grade is found to be 0.665% Cu.

The Sungun Copper Mine is located in a highly hazardous seismic area. Most recently, the Ahar-Varzeqan earthquake, M 6. 2, occurred on August 11, 2012; at a distance about 40 kilometers away from the mine. Nevertheless, the seismic stability of the Final pit wall was not comprehensively reviewed. In this research, the southwestern wall of Sungun’ s Final pit was investigated through pseudo-static, Newmark and numerical methods. Moreover, some of the difficulties in accomplishing and interpreting the results in dynamic analyses of a mining slope is explained. Pseudo-static analyses revealed that the Final pit wall is not safe against the design earthquake, and it is highly prone to failure. The results of Newmark analysis proved that a large failing region would form in the case of design earthquake. Based on a dynamic numerical analysis on the pit wall, continuous forward and downward earthquake-induced movements were observed, as well, which might be a sign of pit wall instability. The effect of underground water drainage on stability of the pit wall was investigated in the numerical model. Resulted time histories of displacements in the drained pit wall proved that only water drainage is sufficient for protecting the pit wall against the design earthquake.

The large-scale open-pit mine production planning problem is an NP-hard issue. That is, it cannot be solved in a reasonable computational time. To solve this problem, various methods, including metaheuristic methods, have been proposed to reduce the computation time. One of these methods is the genetic algorithm (GA) which can provide near-optimal solutions to the problem in a shorter time. This paper aims to evaluate the efficiency of the GA technique based on the pit values and computational times compared with other methods of designing the ultimate pit limit (UPL). In other words, in addition to GA evaluation in UPL design, other proposed methods for UPL design are also compared. Determining the UPL of an open-pit mine is the first step in production planning. UPL solver selects blocks whose total economic value is maximum while meeting the slope constraints. In this regard, various methods have been proposed, which can be classified into three general categories: Operational Research (OR), heuristic, and metaheuristic. The GA, categorized as a metaheuristic method, Linear Programming (LP) model as an OR method, and Floating Cone (FC) algorithm as a heuristic method, have been employed to determine the UPL of open-pit mines. Since the LP method provides the exact answer, consider the basics. Then the results of GA were validated based on the results of LP and compared with the results of FC. This paper used the Marvin mine block model with characteristics of 53271 blocks and eight levels as a case study. Comparing the UPL value's three ways revealed that the LP model received the highest value by comparing the value obtained from GA and the FC algorithm's lowest value. However, the GA provided the results in a shorter time than LP, which is more critical in large-scale production planning problems. By performing the sensitivity analysis in the GA on the two parameters, crossover and mutation probability, the GA's UPL value was modified to 20940. Its UPL value is only 8% less than LP's UPL value.