MétaCan
Menu
Back to cohort
Record W4408485948 · doi:10.5194/egusphere-egu25-17341

Techno-Economic Assessment of Geothermal Energy Resource in the Jharia Coal Field of India

2025· preprint· en· W4408485948 on OpenAlexaff
Anupal Jyoti Dutta, Chandni Mishra, Debashis Konwar, Sandeep D. Kulkarni

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsUniversité de Saint-Boniface
Fundersnot available
KeywordsGeothermal gradientCoalResource (disambiguation)Coal fieldNatural resource economicsGeothermal energyField (mathematics)Environmental scienceEconomicsEngineeringGeologyWaste managementCoal miningComputer scienceGeophysicsMathematics

Abstract

fetched live from OpenAlex

The Jharia Coalfield (JCF), the magnificent pocket of coking coal in the southern part of the Dhanbad district of Jharkhand India, has always remained at the peak of attention for the technological challenges that occurred during the mining of coal. The JCF is also the most critically highlighted coalfield as it is the only known depository of prime coking coal in India which is infamous for its extensive coal fires ignited mainly due to its dynamic spontaneous combustion nature. Earlier studies reported significant anomalous temperature variations in the range of 160-200⁰C along subsurface cracks and vents; also the geothermal gradient is locally high in  the basin to be around 40-45⁰C/km. The implementation of geothermal heat extraction technologies to utilise the wasted heat underneath would require a comprehensive understanding and estimation of the techno-economics of various operational and maintenance costs. The economic assessment for a 5 MW geothermal plant revealed an initial investment cost of 12.02 (MM$) and 8.05 (MM$) and NPV to vary between 27.08 (MM$) to 31.04 (MM$)  respectively for the source temperature of 100⁰C and 150⁰C in the burning JCF basin.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.695
Threshold uncertainty score0.532

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 science0.0010.000
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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicMining Techniques and EconomicsFrench-language works237,207