Scenario-Based Economic Assessment and Local Ranking of Orebody Mining Alternatives in Caving Zones of the Zhezkazgan Deposit

The recovery of residual and previously unmined ore reserves near caving and active-displacement zones is complicated by increased ore loss, dilution, development requirements, support demand, and economic uncertainty. This study presents an exploratory economic–mathematical framework for scenario-based assessment and local ranking of mining alternatives at the Annenskaya Mine of the East Zhezkazgan deposit. The analysis uses 54 modelled scenarios representing distinct combinations of mine-specific geological, technological, and cost parameters rather than independently observed production blocks. The framework integrates material-and-value balance, a quadratic penalty-based scenario model, and a standardised second-order polynomial fitted using Lasso regularisation. The variables include orebody dip, thickness as a stratification factor, ore loss, dilution, displacement-zone conditions, and principal development, extraction, and support costs. Five-fold cross-validation selected λ = 10. The model achieved R2 = 0.41 on the training set, whereas cross-validation yielded R2cv = −0.17, RMSE = USD 553,800, and MAE = USD 461,200. These results restrict the model to exploratory sensitivity analysis and local comparison. The positive-profit domain was widest at dip angles of 30–45° and narrowed at 50–51°. Hessian eigenvalues confirmed a saddle-shaped response surface without an unconstrained global maximum. Broader application requires recalibration, stronger baselines, continuous geomechanical indicators, and independent operational verification.

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Journal
Sci
Published
2026-09-04
DOI
https://doi.org/10.3390/sci8090242
Primary Topic
Mining Techniques and Economics
Type
article
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article

Scenario-Based Economic Assessment and Local Ranking of Orebody Mining Alternatives in Caving Zones of the Zhezkazgan Deposit

Raissa Zhanakova, Abdikarim Zeinullin, Dosanbay Bekbergenov, Д.М. Шабаз et al.
Sci
Mining Techniques and Economics
article

Scenario-Based Economic Assessment and Local Ranking of Orebody Mining Alternatives in Caving Zones of the Zhezkazgan Deposit

Raissa Zhanakova, Abdikarim Zeinullin, Dosanbay Bekbergenov, Д.М. Шабаз, Igor Savich
article en

Abstract

The recovery of residual and previously unmined ore reserves near caving and active-displacement zones is complicated by increased ore loss, dilution, development requirements, support demand, and economic uncertainty. This study presents an exploratory economic–mathematical framework for scenario-based assessment and local ranking of mining alternatives at the Annenskaya Mine of the East Zhezkazgan deposit. The analysis uses 54 modelled scenarios representing distinct combinations of mine-specific geological, technological, and cost parameters rather than independently observed production blocks. The framework integrates material-and-value balance, a quadratic penalty-based scenario model, and a standardised second-order polynomial fitted using Lasso regularisation. The variables include orebody dip, thickness as a stratification factor, ore loss, dilution, displacement-zone conditions, and principal development, extraction, and support costs. Five-fold cross-validation selected λ = 10. The model achieved R2 = 0.41 on the training set, whereas cross-validation yielded R2cv = −0.17, RMSE = USD 553,800, and MAE = USD 461,200. These results restrict the model to exploratory sensitivity analysis and local comparison. The positive-profit domain was widest at dip angles of 30–45° and narrowed at 50–51°. Hessian eigenvalues confirmed a saddle-shaped response surface without an unconstrained global maximum. Broader application requires recalibration, stronger baselines, continuous geomechanical indicators, and independent operational verification.

SciVol. 8(9)
Satbayev University (KZ), National Nuclear Center of the Republic of Kazakhstan (KZ), Mining Institute (RU), Kazakh Technology and Business University named after K.Kulazhanov (KZ)
Openalex Percentile: Top 14%
Mining Techniques and Economics
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