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Multi-criterion analysis of land reclamation methods at Klenovnik open pit mine, Kostolac coal basin
Abstract
Klenovnik open pit mine in the Kostolac coal basin was closed after coal reserves had been depleted. The land reclamation and renovation project is in process. The key concern is selection of the land reclamation method. Considering objectives of the reclamation project and the environmental conditions, 10 alternative scenarios are offered based on analysis of the influences and the project feasibility criteria. The ranging of the alternatives and the sound selection of the best scenario is possible using the multi-attribute decision making techniques Promethee and Electre.
... Decommissioning activities are carried out in many economic sectors. One can decide, for instance, to deactivate a highway (Thompson and Sessions, 2008;Weaver and Hagans, 1999), a nuclear plant (Suh et al., 2018;Sudholt, 2013), a solar power generation facility (Gu� edez et al., 2015;Søndergaard et al., 2014), or a mining complex (Amirshenava and Osanloo, 2018;Masoumi et al., 2014;Dimitrijevic et al., 2014), among other infrastructures. This paper is particularly interested in the decommissioning process of oil and gas (O&G) production facilities (e. g., Chandler et al., 2017;Fowler et al., 2014;Henrion et al., 2015;Na et al., 2017), with an emphasis on offshore installations. ...
... Also known as scoring method or weighted linear combination, Articles (Cripps and Aabel, 2002) Oil & Gas SQ (Fowler et al., 2014) Oil & Gas ✓ (Henrion et al., 2015) 2 Oil & Gas ✓ (Na et al., 2017) Oil & Gas ✓ (Kanmkamnerd et al., 2016) Oil & Gas NEBA (Smyth et al., 2015) Wind power SWOT analysis (Kerkvliet and Polatidis, 2016) Wind power ✓ (Shaw et al., 2001) Mining ✓ (Bascetin, 2007) Mining ✓ (Soltanmohammadi et al., 2008) Mining ✓ ELECTRE (Soltanmohammadi et al., 2009) Mining ✓ ✓ (Narrei and Osanloo, 2011) Mining ✓ ✓ TOPSIS (Bangian et al., 2012) Mining ✓ (Dimitrijevic et al., 2014) Mining ✓ ELECTRE (Masoumi et al., 2014) Mining ✓ TOPSIS (Amirshenava and Osanloo, 2018) Mining ✓ ✓ TOPSIS Nuclear ✓ (Kim and Song, 2009) Nuclear MAUT (Zachar et al., 2011) Nuclear OMEGA (Jeong et al., 2012) Nuclear ✓ (Poskas et al., 2013) Nuclear ✓ (Jarjies et al., 2013) Nuclear Priorisation methodology (Ilg et al., 2017) Nuclear Cost-benefit analysis (Thompson and Sessions, 2010) Road MIP (Allison et al., 2004) Road Decision tree (Mergias et al., 2007) Transport ✓ (Ahmed et al., 2016) Transport ✓ ( Simple Additive Weighting (SAW) is a simple and often used multi attribute decision technique. The method is based on the weighted average (Hwang and Yoon, 1981b). ...
... See for example (Lee and Chang, 2018;Fetanat and Khorasaninejad, 2015), which aimed to facilitate the selection of energy sources and wind farm locations, respectively. Despite that, when it comes to decommissioning problems, only mine closure studies that made use of this framework were identified (Soltanmohammadi et al., 2008;Dimitrijevic et al., 2014). The study in (Soltanmohammadi et al., 2008) noted that, being an outranking method, ELECTRE allows for the decision-maker to assume that certain pairs of criteria cannot be compared. ...
Regardless of the economic activity, decommissioning decisions are often highly complex. This is due to the diversity of operational and local parameters, as well as the multitude of stakeholders involved, who generally have conflicting interests. This sets up a challenging multi-criteria decision problem on the activities to be carried out during the decommissioning process. This paper aims to present an overview of decision-support tools applied to decommissioning, and covers many economic sectors, with a focus on the oil and gas sector and on multi-criteria decision analysis (MCDA) methods. The paper delves deep into the aspects to be considered before reaching a decision, examining the experiences and methods found both in industrial reports and in academic papers.
... Multi-Criteria Decision Making (MCDM) methods are regarded as the best tool for solving this problem (Zimmerman, 2016). A large number of researchers implemented decision-making methods in order to select PMLU (Bascetin, 2007;Soltanmohammadi et al., 2008aSoltanmohammadi et al., , 2008bSoltanmohammadi et al., , 2009Narrei and Osanloo, 2011;Betrie et al., 2013;Dimitrijevic et al., 2014;Yavuz and Altay, 2015;Zimmerman, 2016;Shenavar and Osanloo, 2016). Using at least two methods and comparing their results can obtain more accurate outcomes due to the advantages and disadvantages of MCDM methods (Narrei and Osanloo, 2011). ...
... This equation is inverted to a negative nature for the criterion. Table 4 The key objectives of mine reclamation (Osanloo, 2017;Dimitrijevic et al., 2014). ...
... The majority of them focus in ranking or prioritizing a number of potential land uses (alternatives) leading to the selection of a single land use for the whole mined area. Such a selection is achieved by applying multicriteria decision-making (MCDM) approaches [1,3,[7][8][9]]. An example of the MCDM approach is the development of the analytical hierarchy process (AHP) methodology that assesses the priorities based on the inputs of specialists in mining industry activities. ...
... If p mut F, then the land use l in the square is changed else it remains the same. The new land use l (mutated value) is derived from the empirical distribution used in the initialization step according to Eqs. (9) and (10). If the new land use (mutated) is identical to the initial, the mutation procedure is repeated until the mutated value become different from the initial one. ...
A methodology for the selection of the optimal land uses of the reclamation of mined areas is proposed. It takes into consideration several multi-nature criteria and constraints, including spatial constrains related to the permissible land uses in certain parts of the mined area. The methodology combines desirability functions and evolution searching algorithms for selection of the optimal reclamation scheme. Its application for the reclamation planning of the Amynteon lignite surface mine in Greece indicated that it handles effectively spatial and non-spatial constraints and incorporates easily the decision-makers preferences regarding the reclamation strategy in the optimization procedure.
... Indeed, it is a part of the mining operation that begins in the exploration stage and continues until the decommissioning of the mineral processing plant. Due to the importance of mine reclamation, many researchers have worked on this issue, and many of them have considered Multi-Criteria Decision-Making (MCDM) methods to select the most suitable PMLU (Dimitrijevic et al., 2014;Narrei and Osanloo, 2011;Shenavar and Osanloo, 2016;Yavuz and Altay, 2015). Ruiz et al. (2020) proposed the construction of technosols from human and animal waste as a low-cost strategy for waste management and reclamation of mined lands, because these technosols create a favorable environment in mined areas for plant growth. ...
The mining industry has traditionally relied on conventional fossil-based fuel sources to meet its growing energy demand. The industry is now tasked with responding to the challenges of increasing fuel prices while commodity prices tighten, resulting in ever-narrowing operating margins and increased opposition from communities to new conventional energy sources. So far, research about such decision-making on the use of renewable energy in production scheduling (PS) problem for open pit mining operations is underdeveloped. Due to the conflicting nature of economic and environmental objectives, the PS becomes a multi-objective problem. In this paper, a multi-objective gravitational search algorithm is used to provide Pareto optimal solutions which present the possible tradeoff between the cost and environmental objectives of the PS problem. To solve the problem, the weighted sum method is applied to convert multi-objective optimization to scalar optimization. The numerical results demonstrate the
effectiveness of the proposed approach in solving multi-objective PS problems.
... Another group is the studies that determine mined-land suitability through Multi-Criteria Decision-Making (MCDM) methods [3,6,16,18,20,[29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Generally, these studies' philosophy is similar. ...
Mined Land Suitability Assessment (MLSA) is a critical step in mine reclamation planning for ensuring the sustainability of Post-Mining Land-Use (PMLU). In this study, a general semi-quantitative MLSA approach based on the 2D assessment matrix is developed to calculate the mined land suitability score and determine the suitability level. To this end, a new PMLU classification is suggested. The proposed approach is verified in a real case, and the results’ reliability is confirmed by considering this mine’s characteristics. This new approach will facilitate choosing the suitable PMLU with high accuracy due to the assurance of mined land’s suitability.
... Hudej et al. [4] determined the position of the main mining shaft using multiple criteria analyses simultaneously (PROMETHEE, ELECTRE, AHP, and VIKOR), and the final model was obtained by the weighting method. Dimitrijević et al. [5] used PROME-THEE and ELECTRE methods to select the best land reclamation method of open-pit coal mine between 10 offered alternatives. Multi-criteria analysis was used by Šubaranović et al. [6] to choose between the two variants of the groundwater penetration protection system modification of the surface coal mine Drmno. ...
Mining design is usually evaluated with different multiple-criteria decision-making (MCDM) methods when it comes to large open pit or underground ore mines, but it is not used on quarry sites. Since Croatia is mostly mining stone, the implementation of such methods in decision making of the quarry mine design is imperative but left out. In this paper, the PROMETHEE II and AHP decision-making methods are implemented on the quarry site to find out the best final quarry design contour. By implementing the MCDM methods, the best quarry model was chosen based on 22 different criteria parameters out of three final quarry designs. The chosen model is not only financially sound but also has the least environmental impact.
... Na, Lee, Liew, & Zawawi, 2017;Repsol, 2017;Xodus, 2017 ), ELECTRE (e.g. Dimitrijevic et al., 2014;Soltanmohammadi, Osanloo, Rezaei, & Bazzazi, 2008 ) and PROMETHEE (e.g. Kerkvliet & Polatidis, 2016;Mergias, Moustakas, Papadopoulos, & Loizidou, 2007 ). ...
This paper is motivated by decommissioning studies in the field of oil and gas, which comprise a very large number of installations and are of interest to a large number of stakeholders. Generally, the problem gives rise to complicated multi-criteria decision aid tools that rely upon the costly evaluation of multiple criteria for every piece of equipment. We propose the use of machine learning techniques to reduce the number of criteria by feature selection, thereby reducing the number of required evaluations and producing a simplified decision aid tool with no sacrifice in performance. In addition, we also propose the use of machine learning to explore the patterns of the multi-criteria decision aid tool in a training set. Hence, we predict the outcome of the analysis for the remaining pieces of equipment, effectively replacing the multi-criteria analysis by the computational intelligence acquired from running it in the * corresponding author training set. Computational experiments illustrate the effectiveness of the proposed approach.
... Betrie et al (2013) chooses the optimal remediation option for Acid Rock Drainage (ARD) from mine wastes using AHP and PROMETHEE methods. Dimitrijevic et al (2014) determines the PMLU in Klenovnik open pit mine in the Kostolac coal basin using MCDM methods, PROMETHEE and ELECTRE. They recommended to include at least two MCDM techniques in order to improve the reliability of ranking. ...
... [5], [8]. ...
Surface mines are generally open and very dynamic systems influenced by a large number of technical, economic, environmental and safety factors and limitations in all stages of the life cycle. In this paper the dynamic compliance period surface mining phases and of the reclamation. Also, an analysis of the reclamation of surface mines, and process flow management project recultivation is determined by the principled management model reclamation. The analysis of the planning process is defined by the model optimization recultivation surface mine.
Due to its wide application, multi-criteria decision-making has easily found its way to the mining industry as well. Mining is particularly interesting from the multi-criteria analysis aspect, as mining projects require analyzing numerous factors affecting the exploitation process. However, multi-criteria problems are among the poorly structured decision-making models, given they take place in stochastic conditions. This primarily refers to the number of criteria and the complexity of their mutual relationships, which can be characterized by a complete contradiction, and the methodologically diverse space for determining preferences or weight factors that significantly affect decision-making results. In this paper, the use of EDAS++, normalized matrix data values, and interval criteria, presented through a mathematical model and example, allows ranking in conditions where some of the criteria have an optimal value interval of attributes that are equally relevant and therefore invariant with respect to the decision. EDAS++ was used as a comparative method in analyzing open-pit limestone mines to obtain the broadest possible range of ranking methods while working on a doctoral dissertation entitled "Decision-making in case of different mining project solutions by comparative single and multi-criteria modeling."
To avoid a climate crisis, major industrial economies are being urged to reduce their dependence on coal-fired power generation. Given decarbonisation pressures, many coal mining regions worldwide are facing the prospect of mine closures. However, few viable planning mechanisms are available to assess potential post-mining alternatives and enhance regional transition. To optimise transition outcomes and maximise stakeholder acceptance, mechanisms that incorporate input from a diverse range of stakeholders and disciplinary perspectives are needed at a regional scale. These mechanisms need to compute seemingly disparate types of multi-disciplinary and multi-scale data in a robust, coherent, and transparent fashion. This paper presents a conceptual mixed-method framework for post-mining land-use planning that integrates stakeholders’ involvement, GIS, multi-criteria decision-making and fuzzy logic. The framework utilise environmental and socio-technical data to support the decision-making process. This work is driven by the urgency to offer mining regions in transition a tool for planning their post-mining future. The proposed framework builds on previous literature and has the ability to support a wide variety of institutions and professionals in their efforts to facilitate the post-mining planning process in mining-dependent regions toward a low-carbon future.
The development of short- and medium-term mine production schedules in isolation from each other has meant that only a local optimum can be achieved when each scheduling phase is carried out. The globally optimal solution, however, can be achieved when integrating scheduling phases and accounting for the interaction between short-term and medium-term activities simultaneously. This paper addresses the task of integrating short- and medium-term production plans by combining the short-term objective of minimizing deviation from targeted mill feed grade with the medium-term objective of maximizing net present value (NPV) into a single mathematical optimization model. A conceptual sublevel stoping operation comprising 30 stopes is used for trialling segregated and integrated scheduling approaches. Segregated medium- and short-term scheduling using separate models achieved an NPV of 42 823 657 with smoother mill feed grade. The larger the stope data set, the larger the difference between the two scheduling approaches is likely to be. At the very least, an integrated approach ensures feasibility across the two scheduling horizons, which cannot always be assumed when using a segregated approach. © The Southern African Institute of Mining and Metallurgy, 2012.
The article points at advisability of implementing geoecological valuation of the environment in mineral extraction areas within the limits of the already formed natural and mine engineering systems, since the area of geochemical mining impact on the environment is always larger than non-mining impact area (this is valid for long-standing local nature-and-mine engineering systems).
Mixed integer programming (MIP) has been used for optimizing production schedules of mines since the 1960s and is recognized as having significant potential for optimizing production scheduling problems for both surface and underground mining. The major problem in long-term production scheduling for underground orebodies generally relate to the large number of variables needed to formulate a MIP model, which makes it too complex to solve. As the number of variables in the model increase, solution times are known to increase at an exponential rate. In many instances the more extensive use of MIP models has been limited due to excessive solution times. This paper reviews production schedule optimization studies for underground mining operations. It also presents a classical MIP model for optimized production scheduling of a sublevel stoping operation and proposes a new model formulation to significantly reduce solution times without altering results while maintaining all constraints. A case study is summarized investigating solution times as five stopes are added incrementally to an initial ten stope operation, working up to a fifty stope operation. It shows substantial improvement in the solution time required when using the new formulation technique. This increased efficiency in the solution time of the MIP model allows it to solve much larger underground mine scheduling problems within a reasonable time frame with the potential to substantially increase the net present value (NPV) of these projects. Finally, results from the two models are also compared to that of a manually generated schedule which show the clear advantages of mathematical programming in obtaining optimal solutions. The Southern African Institute of Minig and Metallurgy, 2010.
A parallel genetic algorithm for the graph partitioning problem is presented, which combines general heuristic algorithms with techniques that are described in evolution theory. In the parallel genetic algorithm the selection of a mate is restricted to a local neighborhood. In addition, the parallel genetic algorithm executes an adaptation step after an individual is generated, with the genetic operators crossover and mutation. During the adaptation step the solution is improved by a common algorithm. Another selection step decides if the adapted descendant should replace the parent individual. Instead of using a uniform crossover operator a more intelligent crossover operator, which copies subsets of nodes, is used. Basic parameters of the parallel genetic algorithm are determined for different graphs. The algorithm found for a large sample instance a new unknown solution.
Mixed integer programming (MIP) has been used for optimizing production schedules of mines since the 1960s. The major problem in. the long-term production scheduling for an entire orebody is that the number of integer variables needed to formulate an MIP model is too large to solve the formulation. This number may reach well over one hundred thousand. To overcome this difficulty, this paper presents two new algorithms to reduce the size of the problem. These algorithms assign an earliest and latest possible start date for each machine placement, eliminating the integer variables that correspond to machine placement before its early start date and after its late start date. A case study based on Kiruna Mine, the second largest underground mine in the world, is summarized in the paper. it shows substantial improvement in the solution time required using the new algorithms. This increased efficiency in the solution time of the MIP model allows it to be applied to Kiruna Mine, with the potential to increase substantially the net present value (NPV) of the project.
The use of geographic information systems (GIS) as a tool to analyze and display data is gathering momentum in the mining industry. Of particular interest is the capability of GIS to link spatial features and tabular data. The application of GIS in underground mining discussed here concentrates on four technical areas. They are land ownership and mineral claims, exploration management, production and mine safety. The benefits of consolidating and archiving claim data are important to a land and exploration manager, as is the ability to georeference property maps defined in local coordinates into state coordinate system. In the production area, GIS facilitates the optimal siting and querying of service installations relative to production centers, to meet the production parameters. In mine safety, the siting of refuge chambers relative to production centers is optimized. And areas of potential safety concern are identified using GIS's proximity analysis. The same applies to finding the shortest route to emergency exits and the preparation of maps to facilitate the prompt evacuation of mine personnel.
The use of mixed integer programming is a modelling approach well suited to formulate the mine scheduling optimisation problem for both open pit and underground mining. The resolution applied for discretising the problem, however, has a direct effect on both the level of selectivity that can be applied to improve profitability, as well as the computational feasibility. The proposed model allows for a balance in reducing the resolution used in discretising the underground mine scheduling problem, while maintaining enough detail that will allow the generation of mine production schedules that improve profitability through selective mining. As a secondary contribution, an improved formulation set within a resource production/consumption framework is presented, which can potentially simplify notation used in formulating underground mine scheduling optimisation problems.
This book addresses the concepts and principles of corporate investment decision making with relation to mine property valuation.
Quantification, assessment and management of orebody uncertainty is critical to strategic mine planning. A method consisting
of a series of steps for uncertainty quantification and risk assessment in pit design optimization is outlined here. Multiple
simulated scenarios of an orebody’s grade distribution are processed through an established pit optimization approach to produce
a distribution of possible outcomes in terms of key project indicators. These indicators are then assessed to support mine
planning decisions.
KeywordsOpen pit mine design–grade risk analysis–upside potential–downside risk
Redistricting is the process of dividing a geographic area consisting of spatial units—often represented as spatial polygons—into smaller districts that satisfy some properties. It can therefore be formulated as a set partitioning problem where the objective is to cluster the set of spatial polygons into groups such that a value function is maximized [1]. Widely used algorithms developed for point-based datasets are not readily applicable because polygons introduce the concepts of spatial contiguity and other topological properties that cannot be captured by representing polygons as points. Furthermore, when clustering polygons, constraints such as spatial contiguity and unit distributedness should be strategically addressed. Towards this, we have developed the Constrained Polygonal Spatial Clustering (CPSC) algorithm based on the A* search algorithm that integrates cluster-level and instance-level constraints as heuristic functions. Using these heuristics, CPSC identifies the initial seeds, determines the best cluster to grow, and selects the best polygon to be added to the best cluster. We have devised two extensions of CPSC—CPSC* and CPSC*-PS—for problems where constraints can be soft or relaxed. Finally, we compare our algorithm with graph partitioning, simulated annealing, and genetic algorithm-based approaches in two applications — congressional redistricting and school districting.
This paper examines Pareto optimality of solutions to multi-objective problems scalarized in the min-norm, compromise programming, generalized goal programming, or unrestricted min-max formulations. Issues addressed include, among others, uniqueness in solution or objective space, penalization for over-achievement of goals, min-max reformulation of goal programming, inferiority in Tchebycheff-norm minimization, strength and weakness of weighted-bound optimization, “quasi-satisficing” decision-making, just attaining or even over-passing the goals, trading off by modifying weights or goals, non-convex Pareto frontier. New general necessary and sufficient conditions for both Pareto optimality and weak Pareto optimality are presented. Various formulations are compared in theoretical performance with respect to the goal-point location. Ideas for advanced goal programming and interactive decision-making are introduced.
Large capital intensive projects, such as those in the mineral resource industry, are often associated with diverse sources of both endogenous and exogenous risks and uncertainties. These risks can greatly influence the project profitability. Having the ability to plan for these uncertainties is increasingly recognized as critical to long-term mining project success. In the mining industry in particular, the relationships between input variables that are controllable, and those that are not, and the physical and economic outcomes are complex and often nonlinear. The value of managerial flexibility is assessed using data on prices, costs, discount rates, grades, ore extraction, and metal output. Monte Carlo simulation of the mean reversion process is used to forecast revenue data based on an initial metal price, by using annualized volatility. Monte Carlo simulation of the Geometric Brownian Motion is used to forecast operating costs. To quantify the uncertainty in the parameters within a project such as capital investment, ore grade, and mill recovery, we used triangular, uniform, and normal statistical distribution, respectively. To decrease uncertainty related to selection of the appropriate discount rate, we have applied the concept of fuzzy sets theory. The result is a Net Present Value (NPV) based on the cash flows generated by the simulation over the timeframe of the project. When using fuzzy numbers, the fuzzy NPV itself is the payoff distribution from the project. The model explains investment behavior satisfactorily, both from a statistical and from an economic point of view.
In this paper we present and study a class of graph partitioning algorithms that reduce the size of the graph by collapsing vertices and edges, find a k-way partitioning of the smaller graph, and then uncoarsen and refine it to construct a k-way partitioning for the original graph. These algorithms compute a k-way partitioning of a graph G = (V, E) in O(|E |) time which is faster by a factor of O(log k) than previously proposed multilevel recursive bisection algorithms. A key contribution of our work is in finding a high quality and computationally inexpensive refinement algorithm that can improve upon an initial k-way partitioning. We also study the effectiveness of the overall scheme for a variety of coarsening schemes. We present experimental results on a large number of graphs arising in various domains including finite element methods, linear programming, VLSI, and transportation. Our experiments show that this new scheme produces partitions that are of comparable or better q...
Provides treatment of the applications of mining engineering while reinforcing material with analyses of special topics as well as numerical examples and problems. Initial chapters are devoted to fundamentals, explaining the four stages of mining - prospecting, exploration, development, exploitation - and the unit operations of mining. The text continues with coverage of surface mining and underground mining.
In this article we compare three models of the stochastic behavior of commodity prices that take into account mean reversion, in terms of their ability to price existing futures contracts, and their implication with respect to the valuation of other financial and real assets. The first model is a simple one‐factor model in which the logarithm of the spot price of the commodity is assumed to follow a mean reverting process. The second model takes into account a second stochastic factor, the convenience yield of the commodity, which is assumed to follow a mean reverting process. Finally, the third model also includes stochastic interest rates. The Kalman filter methodology is used to estimate the parameters of the three models for two commercial commodities, copper and oil, and one precious metal, gold. The analysis reveals strong mean reversion in the commercial commodity prices. Using the estimated parameters, we analyze the implications of the models for the term structure of futures prices and volatilities beyond the observed contracts, and for hedging contracts for future delivery. Finally, we analyze the implications of the models for capital budgeting decisions.
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