Research on a Deeply Integrated Model for Structural Optimization in Coal Spontaneous Combustion Temperature Prediction

Summary

Here, we describe a protocol for predicting the temperature of coal spontaneous combustion using an SSA-optimized CNN-LSTM-Attention framework that automatically optimizes the network structure and parameters, thereby improving accuracy, adaptability, and generalization across heterogeneous datasets and varied mining conditions.

Abstract

Here, we propose a protocol for coal spontaneous combustion temperature prediction based on a Sparrow Search Algorithm (SSA)-optimized convolutional neural networks (CNN)-long short-term memory (LSTM)-Attention framework. This protocol addresses the limitations of fixed network architectures, restricted generalization, and poor transferability commonly encountered in conventional methods. The framework extracts spatial features using CNN and captures temporal dependencies with LSTM networks, while the attention mechanism highlights critical temperature phases and salient features. The SSA jointly optimizes network depth and hyperparameters, enabling dynamic adaptation to varying data complexities across different mining sites and experimental conditions. The protocol consists of data acquisition, feature preprocessing, model construction, parameter optimization, and validation steps. Experimental results demonstrate that the proposed model achieves significantly higher predictive accuracy on homogeneous datasets and maintains robust generalization performance across heterogeneous datasets, making it well-suited for real-time coal mine temperature monitoring and early-warning systems.

Introduction

Coal still plays a dominant role in China's energy structure. However, during storage, transportation, and mining, spontaneous heating can occur, leading to spontaneous combustion. This often causes mine fires, seriously threatening mine safety and workers' lives^1,2,3,4. Hence, accurate prediction of coal mine fire risks and their temperature variations is essential for early warning and disaster mitigation. The prediction methods for coal spontaneous combustion temperature have evolved from early empirical formulas to analysis appro....

Protocol

1.Coal spontaneous combustion data collection

1. Assemble the air-type coal programmed heating oxidation system, including the gas delivery system, temperature control unit, and gas analysis instruments. Ensure that all connections are secure, temperature control settings are accurate, and the gas chromatograph and other analytical devices are fully calibrated.
2. Weigh 1000 g of mixed coal sample (keep the original lump form), thoroughly homogenize the sample using the quartering method, and evenly place the coal blocks inside the heating chamber. Maintain consistent thickness and uniform distribution to ensure even heating.
3. Start the air pu....

Results

Model stability was validated using four independent datasets, demonstrating consistent predictive performance across different geological conditions. This section presents representative results and performance evaluations of the coal spontaneous combustion experiments and the proposed SSA-CNN-LSTM-Attention model. First, the variations in multiple gas indicators collected during the programmed heating oxidation experiments are analyzed to reveal the dynamic patterns of gas concentration.......

Discussion

Use intact coal blocks; perform only surface cleaning after sampling and double-seal immediately to avoid prolonged exposure. Keep the gas line leak-tight with constant-flow control (MFC), execute the programmed heating exactly as specified in the protocol, and calibrate the GC with certified standards. Acquire temperature and gas signals at fixed intervals and synchronize timestamps (see protocol). From a computational standpoint, fix and record the environment (OS, Python, deep-learning framework, CUDA, etc.), set rand.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
6-port, 2-position gas sampling valve w/ universal actuator	VICI Valco	EUDA-2C6UWT	1/16" fittings; 0.75 mm ports; RS-232; 2" standoff
Alumina sample boat	MTI Corporation	EQ-CA-L50W40H20	>99.5% Al2O3; 50×40×20 mm
Chromatography software	Agilent Technologies	OpenLab CDS	Data acquisition/processing
Coal samples (field-collected)	In-house/field-collected	N/A	Source detailed in Methods
CUDA / cuDNN	NVIDIA	https://developer.nvidia.com/cuda-zone	CUDA 11.x; matching cuDNN
DAQ mainframe	Keysight	DAQ970A	6½-digit DMM; USB/LAN
Desiccant (indicating)	W.A. Hammond Drierite	23001	Calcium sulfate; 8 mesh; 1 lb
Diaphragm air pump	KNF	https://www.knf.com	Continuous air supply; adjustable flow
FR lab coat	Bulwark	KEL2 (series)	NFPA 2112 compliant
Gas chromatograph	Agilent Technologies	G3540A (8890 GC System)	GC system; EPC; up to 2 inlets / 4 detectors
Heat-resistant gloves	Ansell	43-113	Intermittent up to ~350 °C
High-purity air	Air Liquide / Airgas	https://www.airgas.com/solutions/specialty-gases/pure-gases/alphagaz	≥99.99% purity
IDE	JetBrains / Microsoft	https://www.jetbrains.com/pycharm/ ; https://code.visualstudio.com/download	PyCharm / VS Code
Inline filters	Swagelok	https://www.swagelok.com/downloads/webcatalogs/en/ms-01-92.pdf	Sintered SS elements 0.5–15 µm
K-type thermocouples	OMEGA	https://www.omega.com	Type K (NiCr–NiSi)
Mass flow controller (0–200 sccm)	Alicat Scientific	MC-200SCCM-D	MC-series; ±(0.8% rdg + 0.2% FS)
Multi-component calibration gases	Messer	https://specialtygases.messergroup.com/standard-gas-mixtures	Custom concentration; certificate
Operating system	Microsoft	https://www.microsoft.com/en-us/software-download/windows11	Windows 11
Operating system	Canonical	https://ubuntu.com/download/desktop	Ubuntu LTS (22.04/24.04)
PTFE/PFA tubing	Swagelok	https://products.swagelok.com/en/all-products/hoses-flexible-tubing/ptfe-pfa-core-hose/c/716?clp=true	Chemically resistant; 1/16–1/4 in OD
Python	Python Software Foundation	https://www.python.org/downloads/	Version 3.8
Quartz sample boat	MTI Corporation	EQ-QB-1017 (example size)	~1200 °C working temp
Safety goggles	3M	93506P1-DC (example)	Chemical splash; anti-fog options
Stainless-steel seamless tubing	Swagelok	https://www.swagelok.com/downloads/webcatalogs/en/ms-01-181.pdf	316/316L; 1/16–1/4 in OD
Stainless-steel tube fittings & ferrules	Swagelok	https://products.swagelok.com/en/all-products/fittings/tube-fittings-adapters/c/154?clp=true	316/316L; double-ferrule
Temperature controller	Eurotherm	3216	Single-loop PID; programmable ramps/alarms
TensorFlow	Google	https://www.tensorflow.org	Version 2.6
USB thermocouple module	NI (National Instruments)	781314-01 (USB-TC01)	K/J/T; logging software
Variable area flowmeter (Visi-Float)	Dwyer Instruments	VFA-2-EC-SS (0.2–2 SCFH Air)	Low-flow range; direct reading
Workstation GPU	NVIDIA	900-1G136-2530-000 (Founders Edition)	GeForce RTX 4090, 24 GB GDDR6X (FE)