Optimum Machine Learning on Gas Extraction and Production for Adaptive Negative Control

Cheng Cheng; Xiaoyu Cheng; Lu Xiao; Xingying Ma

doi:10.32604/fhmt.2025.065719

January 1, 2025Frontiers in Heat and Mass TransferOpen Access

Optimum Machine Learning on Gas Extraction and Production for Adaptive Negative Control

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Key Points

To develop an optimized machine learning framework for accurately predicting gas extraction performance and counteracting production decline in mining operations.
Constructed a predictive machine learning pipeline to forecast pure gas extraction volume.
Modeled adaptive negative control mechanisms to stabilize extraction rates during operational declines.
Targeted machine learning enables proactive tracking and prediction of pure gas extraction trends.
Adaptive control mechanisms reduce the rate of volume decline to support steady gas utilization in mines.

Implication

Computational study demonstrates adaptive machine learning models for predicting gas extraction volume in mines, suggesting enhanced utilization efficiency.

Abstract

To overcome the challenges associated with predicting gas extraction performance and mitigating the gradual decline in extraction volume, which adversely impacts gas utilization efficiency in mines, a gas extraction pure volu... | Find, read and cite all the research you need on Tech Science Press

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Cite This Study

Cheng et al. (2025) studied this question.

synapsesocial.com/papers/6aa44bc0f7c0f836eaf20684 https://doi.org/10.32604/fhmt.2025.065719

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Optimum Machine Learning on Gas Extraction and Production for Adaptive Negative Control

Key Points

Implication

Abstract

Discussion

Cite This Study

Optimum Machine Learning on Gas Extraction and Production for Adaptive Negative Control

Key Points

Implication

Abstract

Discussion

Cite This Study

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