Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach

Guo, Haoxu; Nguyen, Hoang; Vu, Diep-Anh; Bui, Xuan‐Nam

doi:10.1016/j.resourpol.2019.101474

Cited by 47 publications

(17 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…AI refers to a branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence [55]. AI techniques have been increasing steadily in many engineering fields, including image processing [56], mineral exploration [57], and mine planning [58,59]. Simeone [60] believes that the widespread use of data-driven AI methods is motivated by the successes of ML-based pattern recognition tools.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

2021

View full text Add to dashboard Cite

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

show abstract

Section: Machine Learning Modelsmentioning

confidence: 99%

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

2021

View full text Add to dashboard Cite

show abstract

“…Despite using dynamic sequencing of the geologically optimum push-backs, this research has not investigated the effectiveness of the various environmental and economic factors in the proposed model. Guo et al [21] predicted the cost of mining capital for open-pit mining projects with an artificial neural network approach. e study used Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) estimates of open-pit mining costs.…”

Section: Analysis Of the Environmental Costs In Mining Activitiesmentioning

confidence: 99%

Analysis of Environmental Costs’ Effect in Green Mining Strategy Using a System Dynamics Approach: A Case Study

Jafarpour

Khatami

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Nowadays, one of the most significant problems in mining activities is the significance of analyzing environmental issues along with mining, concentration, and mineral processing operations to achieve the goals of sustainable development. Nevertheless, mine owners refuse to include environmental costs (EC) and consider them unprofitable. Due to the ever-increasing importance of environmental and social topics in recent years, there is a vital need for assessing the EC and its impact on total mining costs and implementing green strategies by the mining managers and engineers. The current study tries to model the mining cost structure by considering the causal relationships between different factors affecting open-pit mining costs to highlight the EC’s role. Furthermore, this research evaluates the effectiveness of implementing each possible mining green strategy in a large-scale copper mine using the System Dynamics (SD) approach. In this regard, seven scenarios and a combination of different environmental strategies, including mine reclamation, an environmental strategy for a condensation and processing plant, and environmental mining operations, have been considered for the SD-based economic analysis. The simultaneous use of the green mining strategies for the concentration and processing plant (Scenario 4) shows a high impact on cost reduction in the mining operation.

show abstract

“…Methods for determining the parameters and indicators of the development system are constantly being improved, as evidenced by the data of papers [13][14][15][16][17][18]. The selected development subsystem in specific mining-geological and mining-technical conditions with the best parameters and environmentally friendly development of the deposit with the complete extraction of all reserves from the subsoil, useful components from mineral raw materials.…”

Section: A System Of the Minerals Opencast Development Is A Complex Omentioning

confidence: 99%

Justification of Systems of Open Cast Development of Useful Fossils and Their New Classification

Rakishev¹

2020

THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN

View full text Add to dashboard Cite

Б а с р е д а к т о р ы х.ғ.д., проф., ҚР ҰҒА академигі М.Ж. Жұрынов Р е д а к ц и я а л қ а с ы: Абиев Р.Ш. проф. (Ресей) Абишев М.Е. проф., корр.-мүшесі (Қазақстан) Аврамов К.В. проф. (Украина) Аппель Юрген проф. (Германия) Баймуқанов Д.А. проф., корр.-мүшесі (Қазақстан) Байтулин И.О. проф., академик (Қазақстан) Банас Иозеф проф. (Польша) Берсимбаев Р.И. проф., академик (Қазақстан) Велесько С. проф. (Германия) Велихов Е.П. проф., РҒА академигі (Ресей) Гашимзаде Ф. проф., академик (Әзірбайжан) Гончарук В.В. проф., академик (Украина) Давлетов А.Е. проф., корр.-мүшесі (Қазақстан) Джрбашян Р.Т. проф., академик (Армения) Қалимолдаев М.Н. проф., академик (Қазақстан), бас ред. орынбасары Лаверов Н.П. проф., академик РАН (Россия) Лупашку Ф. проф., корр.-мүшесі (Молдова) Мохд Хасан Селамат проф. (Малайзия) Мырхалықов Ж.У. проф., академик (Қазақстан) Новак Изабелла проф. (Польша) Огарь Н.П. проф., корр.-мүшесі (Қазақстан) Полещук О.Х. проф. (Ресей) Поняев А.И. проф. (Ресей) Сагиян А.С. проф., академик (Армения) Сатубалдин С.С. проф., академик (Қазақстан) Таткеева Г.Г. проф., корр.-мүшесі (Қазақстан) Умбетаев И. проф., академик (Қазақстан) Хрипунов Г.С. проф. (Украина) Юлдашбаев Ю.А. проф., РҒА академигі (Ресей) Якубова М.М. проф., академик (Тәжікстан) «Қазақстан Республикасы Ұлттық ғылым академиясының Хабаршысы».

show abstract

Forecasting mining capital cost for open-pit mining projects based on artificial neural network approach

Cited by 47 publications

References 38 publications

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Analysis of Environmental Costs’ Effect in Green Mining Strategy Using a System Dynamics Approach: A Case Study

Justification of Systems of Open Cast Development of Useful Fossils and Their New Classification

Contact Info

Product

Resources

About