Optimizing open-pit design for ore and ferronickel sales schemes: Economic and environmental considerations from a nickel laterite mine in Southeast Sulawesi, Indonesia

Introduction: The optimization of the mine boundary is crucial for maximizing profitability in open-pit mining operations. This study focuses on determining the optimal mine boundary for PT Makmur Lestari Primatama (MLP) in Southeast Sulawesi, Indonesia, using a Discounted Cash Flow (DCF) analysis to maximize the Net Present Value (NPV). The analysis incorporates the economic factors affecting mining, such as ore tonnage, nickel grade, market prices, and mining costs. Objectives: The primary objective of this study is to determine the most economically viable pit design for MLP’s nickel laterite mining project by utilizing the Lerch-Grossmann pit optimization algorithm. Additionally, the study aims to assess the sensitivity of the project’s viability to fluctuations in ore prices and mining costs through a detailed sensitivity analysis. Methods: This research employs the Lerch-Grossmann pit optimization algorithm to generate multiple pit designs and calculate their respective NPVs. An economic model incorporates key parameters, including ore tonnage, nickel grade, market prices, and mining costs. A spreadsheet-based NPV model is used to conduct sensitivity analysis, assessing the impact of varying ore prices and mining costs on the project’s financial performance. Results: The results demonstrate that pit OPT_07 remains economically viable, even under fluctuating market conditions, with variations of up to 30% in ore prices and mining costs. This analysis highlights the robustness of the selected pit design in managing market uncertainties, emphasizing its economic sustainability in the face of external financial fluctuations. Conclusions: The NPV-based optimization approach significantly contributes to enhancing the economic sustainability of nickel laterite mining. The study introduces a novel method to optimize mining operations by combining the Lerch-Grossmann algorithm with NPV-based economic modeling and sensitivity analysis. This approach not only maximizes profitability but also assists in managing financial risks associated with market fluctuations. The methodology can be applied to other nickel laterite mines, offering valuable insights for strategic decision-making in mine planning and enhancing overall profitability and risk management.

PT Ceria Nugraha Indotama is planning to open a new mining site in Lapaopao Block area. In order to determine the most profitable pit design on this new area, pit limit optimization is required. Pit limit is optimized by considering economic parameters, geotechnical parameters, and production target. The optimal pit limit generates the highest Net Present Value (NPV) which is affected by economic parameters. The objective of this research is to analyse the NPV sensitivity of pit due to the changes of economic parameters namely mining cost, processing cost, and selling cost by using sensitivity analysis. Data used in this research were drill hole, ore and waste density, slope geometry, production target. The planned production target, ore price, recovery factor, dilution factor, mining cost, processing cost, and selling cost. The analysis results showed when mining costs increased by 20%, NPV decreased from $2,407,032 to $1,683,430 and when mining costs decreased by 20%, NPV increased from $2,407,032 to $3,130,635. When processing cost increased by 20%, NPV decreased from $2,407,032 to $2,133,523 and when processing cost decreased by 20%, NPV increased from $2,407,032 to $2,680,542. When selling cost increased by 20%, NPV decreased from $2,407,032 to $1,995,755 and when selling cost decreased by 20%, NPV increased from $2,407,032 to $2,818,310. It indicated that the economic parameters significantly affect the NPV and most sensitive to the mining cost parameter changes.

This study investigates the influence of bedrock on nickel (Ni) content in laterite deposits in the Lameruru Region, Langgikima District, North Konawe Regency, Southeast Sulawesi Province, Indonesia. The research area is dominated by ultramafic rocks, which are potential sources of laterite nickel. The study employs literature review, field surveys for lithological data collection and representative sampling, petrographic analysis to determine mineral composition and rock types, and geochemical analysis using X-Ray Fluorescence (XRF) to determine nickel content in the rocks. The results show that the bedrock in the study area consists of harzburgite and olivine pyroxenite. Harzburgite, occupying approximately 72.72% of the area, is composed of 54-78% olivine, 15-45% orthopyroxene, 6% clinopyroxene, and accessory minerals, while olivine pyroxenite, occupying about 27.28% of the area, is composed of 27% olivine, 49% orthopyroxene, 17% serpentine, and 7% spinel. Geochemical analysis reveals that harzburgite has higher Ni content (0.10-0.16%) compared to olivine pyroxenite (0.03-0.10%). The distribution of Ni in the bedrock and saprolite is directly proportional, with higher Ni content in harzburgite in both zones. The study concludes that the type of bedrock greatly influences the nickel content in laterite deposits, with harzburgite having a higher Ni content due to its higher percentage of olivine minerals, which are the main hosts of nickel in ultramafic rocks.

This research was conducted using two sample drying methods: the oven method and the roasting method. Elemental analysis was carried out using the X-Ray Fluorescence (XRF) Epsilon 4 instrument. The study investigated the influence of impurity metals on nickel (Ni) content, particularly the presence of silica (SiO₂) and magnesium oxide (MgO). The results showed that silica (SiO₂) plays a significant role in affecting nickel content. The higher the silica content, the lower the detected nickel content. This is supported by negative correlation coefficients of -0.9725 for the oven method and -0.9090 for the roasting method. The coefficients of determination were 94.58% and 82.62%, respectively, indicating a strong relationship between silica and nickel content. In addition, the relationship between MgO and Ni also showed a negative trend. An increase in MgO content was followed by a decrease in Ni content. The correlation coefficients were -0.9678 for the oven method and -0.9651 for the roasting method, with coefficients of determination of 93.66% and 93.13%. These results confirm a very strong influence of MgO on Ni content. A t-test was conducted to determine whether there was a significant difference between the two drying methods in nickel content measurement. The calculated t-value was 0.013, while the critical t-value was 2.18. Since the calculated value is lower, the null hypothesis (H₀) is accepted, indicating no significant difference between the oven and roasting methods

Accurate cost estimation is a critical component of mining project evaluation to determine whether the proposed project is clearly feasible, doubtfully feasible or clearly uneconomic. At the beginning of the process, it is very difficult for project evaluators to find reliable relevant data that can be used for all phases of the cost estimation. This paper provides an overview of published work on mining cost estimation methods, data sources and available software. It also introduces the concept of indices for different components of mining projects, discusses their historical movements, and then it compares mining cost indices in the USA, Canada and Australia. It also introduces a new econometric model for estimating the operating cost and capital cost of a coal mining project. The total operating cost and capital cost of mining are calculated by using the proposed econometric model and verified by comparing the outputs with CostMine data and Sherpa software outputs for a surface coal mine. The model shows that the capital cost and production rate have a negative effect on the operating cost, while the deposit average thickness and stripping ratio have a positive effect.

This paper presents an approach for modeling space solar power system architectures, integrating both economic and technical parameters for all space and ground-based system elements. Key elements of the approach are an integrated architecture assessment model that is used to conduct architectural analysis, a data dictionary that defines all technical and economic parameters and detailed analyses that are conducted external to the integrated model for specific architectural elements. The overall architecture of the space solar power system is composed of eight specific elements including both ground and space segments. Key parameters are identified for each element including variables that are input by the user as well as dependent variables that are calculated by the model. For each element, parameters are grouped into four categories including engineering and operations related technical parameters, and recurring and nonrecurring cost parameters. The integrated model allows the user to gain insights into the viability of the entire space solar power system through outputs such as internal rate of return, net present value, average duty cycle for use of on-orbit assets, and total nonrecurring investment cost. The user can also conduct sensitivity analyses to understand those technologies that have the greatest impact, the markets that provide the greatest opportunity, and the systems which offer the greatest potential.

In this work, the coupled operation of a wind park and a hydrogen power-to-gas (P2G) technology is addressed in order to improve dispatchability and profitability of the wind resource. Among many available storage technologies currently under development, the P2G was chosen because of its large storage capacity and fast response. In particular, oppositely to traditional storage approaches, the solution that considers the direct injection of hydrogen in the natural gas grid infrastructure could reduce investment costs and improve the renewable fraction of fuels. Economic optimal operation and installed P2G power are calculated solving a mixed-integer linear programming (MILP) problem. Performances of the main component (electrolysis unit) are modeled, including additional costs for start-up and partial load operation losses. Technical limits on both electric and natural gas grids are also included. Some assumptions on the economic rules governing the electric grid unbalance are made, according to possible evolutions of the regulation framework focused on EU and Italian system. The simulation is performed on hourly basis, assuming realistic forecasted and real power production profiles from an actual mid-size (30 MW) wind power plant, together with prices of electricity markets and gas production. A sensitivity analysis is also performed varying both economic and technical parameters. Whereas in some scenarios the technology is profitable with the current investment costs, a reduction of electrolyzer costs down to expected mid-term targets would lead to a stronger competitiveness in each scenario. The study aims at identifying the influence of main technical and economic parameters on the effectiveness of the power-to-gas technology. Results show how the proposed solution allows better exploiting the wind resource, although the net electricity production can decrease due to the production of hydrogen, suggesting the possibility to substantially oversize the wind park in order to cover the same electric load.

In this study technical diagnostic tests and economical lifetime assessment of transformers are investigated to evaluate the overall health condition of working transformers. Two artificial intelligence models including artificial neural network and adaptive neuro‐fuzzy inference system models are presented to determine the health index for transformers. The technical and economical parameters are used as input parameters to develop the models. Technical parameters are extracted from oil characteristics and dissolved gas analysis of different transformers. Economical parameters are constructed with transformer capital investments, maintenance and operating costs. The models are developed using 226 experimental field datasets of transformers technical and economical parameters. The models are trained using 80% of the experimental datasets. The remaining 20% is used to evaluate the performance and applicability of the models. The results prove that the models can be used to determine the health condition of transformers with high accuracy.

Equipment selection is one of the most important factors in open pit design and production planning. Equipment selection also affects economic considerations in open-pit design as a function of plan location and depth. Furthermore, equipment selection is a complex multiperson, multi-criteria decision problem. The group decision-making process can be improved by a systematic and logical approach to assess priorities based on the inputs of several specialists from different functional areas within the mine company. The analytic hierarchy process (AHP) can be very useful in involving several decision-makers with different conflicting objectives to arrive at a consensus decision. In this paper, the selection of a loading-hauling system using an AHP-based model was evaluated for coal production in an open pit coal mine located Orhaneli, in western Turkey. The use of the proposed model indicates that it can be applied to improve group decision making in selecting equipment that satisfies optimal specifications. Also, it is found that the decision process is systematic and that using the proposed AHP model can reduce the time taken to select optimal equipment.

A comparative analysis on thermal runaway behavior of Li (NixCoyMnz) O2 battery with different nickel contents at cell and module level

Developing a smart operating system for fairly distribution of irrigation water, based on social, economic, and environmental considerations

Forecasting the magnitude of sustainable biofeedstock supplies is challenging because of 1) myriad potential feedstock types and their management; 2) the need to account for the spatial variation of both the supplies and their environmental and economic consequences; and 3) the inherent challenges of optimizing across economic and environmental considerations. Over the last two decades, US biomass forecasts have become increasingly complex and sensitive to environmental and economic considerations; however, more model development and research is needed. In particular, the landscape and regional tradeoffs of differing biofeedstock supplies need to be addressed, especially with regard to water quality concerns and wildlife/biodiversity. Feedstock assessments need to be done in the context of the direction of land‐use change induced by biofeedstock production; they need to take into consideration both the starting environmental and economic conditions before the land was used to supply biofeedstocks and the probable future conditions that would result in the absence of biofeedstock production. To evaluate sustainability, process‐oriented models need to be coupled or used to inform sector models and more work needs to be done on developing environmental metrics that are useful for evaluating economic and environmental tradeoffs. These challenges are exciting and worthwhile as they will enable the bioenergy industry to capture the environmental and social benefits of biofeedstock production and reduce risks. Published in 2007 by John Wiley and Sons, Ltd.

Resource estimation plays a significant role in determining the quantity and quality of a deposit. In nickel laterite mining, estimation is needed to be able to calculate resources before the mining process takes place. This research was conducted to determine the nickel laterite resources at PT Genba Multi Mineral. In this research, the determination of nickel laterite resource estimation using the Inverse Distance Weighting (IDW) method. Based on the estimation of nickel laterite resources using the IDW method. The estimation of nickel laterite resources was carried out in the saprolite zone with the Cut of Grade (COG) used, namely Ni content ≥ 1.6% with a density of 1.5 tons/m3 obtained a resource tonnage of 233,738 tons with an average Ni content of 1.81%.

The criteria function concept consists of transforming the criteria function (CF) in a quality-economical matrix math MQE. The levels of prescribing the criteria function was obtained by using a composition algorithm for three vectors: T̄ vector – technical parameters’ vector (ti); Ē vector – economical parameters’ vector (ej) and P̄ vector – weight vector (p1). For each product or service, the area of the circle represents the value of its sales. The BCG Matrix thus offers a very useful map of the organization’s service strengths and weaknesses, at least in terms of current profitability, as well as the likely cash flows.

Problem Statement: This paper shows an original method for aluminum’ making optimization. This method is based on the analysis of the functional and technological performances of specific furnaces.Research Questions: The main research question of our article is: what are the best research methods for aluminum industry optimization?Purpose of the Study: The main purpose of the Study is optimization of aluminum industry.Research Methods: The main research methods are: The principle of analogy – consists in observing and analysing competently the modelated reality, using both analogy with other fields of research and logical homology, the modelated subject definition, the effiency criterias definition, making the options, choices evaluating, choosing the final solution.Findings: The main findings of our study are: mathematical modeling of the aluminum’s technological processes for the optimization of the functional and technological performances of this complex unit is based on many specific principles, the modeling system’s central element of the aluminum’ technological processes conceived consists of the system’s criteria function.Conclusions: The mathematical model of prescribing the criteria function concept consists of transforming the Criteria Function (CF) in Quality-Economical Matrices (MQE). The levels of prescribing the criteria function can be obtained by using a composition algorithm for three vectors: T vector – technical parameters’ vector (ti) E vector – economical parameters’ vector (ej) P vector – weight vector (p1).