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Record W2336702781 · doi:10.14288/1.0101783

Financial optimization of mining plant size

2011· article· en· W2336702781 on OpenAlexaboutno aff
Douglas Grant McIntosh

Bibliographic record

VenuecIRcle (University of British Columbia) · 2011
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFinanceEconomics

Abstract

fetched live from OpenAlex

The hypothesis of this study is that the optimal plant size for a mining venture is dependent upon both uncontrollable and controllable variables. Examples of variables which are uncontrollable to the firm are characteristics of the orebody, capital and product markets, projected price levels, and tax structure. Controllable variables in determining plant size are rate of recovery, level of recovery, capital costs, and operating costs. The primary purpose of this study is to delineate the effects of these variables, both singly and jointly, upon plant size and to present a model which will interrelate the variables with that scale of plant which will maximize the value of the firm. A secondary purpose of the study is to compare the impact of both Canadian and United States tax laws upon the profitability of a given operation, and upon the optimal grade-capacity combination to be employed for a given orebody. The difference of the impact of Canadian and United States tax laws with respect to the conservation of resources is also considered. After each of the controllable and uncontrollable variables is defined and analysed, a detailed analysis is made of various methods of mine valuation, with the objective being to identify the valuation method which most closely relates to the value of the firm. Then a model is constructed which will give the mine-life annual cash flows for a given orebody under various concentrator-capacity--cut-off-grade combinations. These cash flows are then converted to internal rates of return and benefit-to-cost ratios, which are contoured for various cut-off grades and concentrator capacities under various metal prices. The model assumes an orebody with a tonnage of 40e ⁽²ˉ ⁵x⁾ million tons, where x is the cut-off grade, in percent copper. Contour plots of benefit-to-cost ratio and internal rate of return were constructed for cut-off grades ranging from 0% to 1% copper, and for concentrator capacities ranging from 5000 tons-per-day to 50,000 tons-per-day, at net smelter returns of 40¢ per lb. to per 46¢ per lb. of contained copper. The tax system under which the highest profits are attained is Canadian tax laws with pre-1968 British Columbia taxes. The operation is least profitable under United States tax laws. However, optimal plant size is least sensitive to changes in metal price under United States tax laws, and most sensitive to price changes under Canadian tax laws with pre-1968 British Columbia taxes. Similarly, optimal cut-off grade is most insensitive to changes in metal price under Canadian tax laws with pre-1968 British Columbia taxes, and most sensitive to product price changes under United States tax laws. Therefore, it can be shown that under United States tax laws, the response of an operation to changing product prices would be to change the optimal cut-off grade, while in Canada, particularly under pre-1968 British Columbia taxes, the response would be to change the optimal operating capacity. Therefore, American tax laws provide the greatest flexibility for responding to changes in product price. The tax system which produces the greatest degree of conservation of resources, as is reflected by completeness of ultimate extraction, is the American case. The lowest extraction level would result under Canadian taxes, with pre-1968 British Columbia/taxes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.136
Teacher spread0.126 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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