Prediction Model for Coal Spontaneous Combustion Based on SA-SVM.
Ontology highlight
ABSTRACT:
SUBMITTER: Deng J
PROVIDER: S-EPMC8153949 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The "three stages" division of coal spontaneous combustion is fuzzy and lacks adequate risk and warning levels corresponding to its divisions; additionally, the targeted prevention measures for each stage have not been described. To address the shortcomings of the "three stages" division, the "five stages" division was proposed to more clearly analyze the stage changes of the spontaneous combustion of coal. The "five stages" method divides the process of the spontaneous combustion of coal into five stages, including: the latent stage, heat accumulating stage, evaporation stage, active stage, and hypoxic stage. The critical point of each stage was determined using adiabatic oxidation experiments and programmed heat experiments. As the critical point of the latent stage, the temperature of zero activation energy is approximately 55-70°C. In the heat accumulating stage, the critical point is the temperature (approximately 90°C) where the external moisture of coal evaporates violently while the internal moisture of coal has not yet fully evaporated. During the evaporation stage, the temperature (approximately 105°C) where the internal moisture has evaporated completely represents the end of this stage and the start of the active stage (105-170°C). When the oxygen concentration drops to 5%, the spontaneous combustion of coal enters the hypoxic stage. Thus, an oxygen concentration of 5% represents the critical point of the start of the hypoxic stage (above 170°C). After the analysis of each stage, risk and warning levels were determined. Considering the major prevention measures of the spontaneous combustion of coal, a staged warning and disposal table was created.
| S-EPMC6107200 | biostudies-literature
Project description:Investigating the division of coal spontaneous combustion stages and the selection of indicator gases is significant to the safe production of coal mines. In this study, the characteristic temperature of coal spontaneous combustion, the generation law of indicator gases, the combustion process, and the division of the combustion stages of coal samples taken from Hongqingliang (HQL) and Dayan (DY) mines were investigated using thermogravimetric analysis experiment, indicator gas detection experiment, and coal oxidation spontaneous combustion experiment. The results of the thermogravimetric analysis experiment showed that the pyrolysis temperatures of the HQL and DY coals were 115.76°C and 131.80°C, and the ignition temperatures were 337.74°C and 360.18°C, respectively. The indicator gas detection results showed that the first-appearance temperature of C2H4 was 85°C for the HQL and DY coals, whereas the first-appearance temperature of C2H6 varied: 115°C for the HQL coal and 130°C for the DY coal. The first-appearance temperatures of C2H2 were 180°C and 195°C for the HQL and DY coals, respectively. The experiments on coal oxidation spontaneous combustion showed that the spontaneous combustion period of the HQL and DY coals were 35.45 and 42.3 days, respectively. The heating process during combustion could be divided into four stages: a latent period of spontaneous combustion, a slow spontaneous heating period, an accelerated spontaneous heating period, and a period of combustion. The critical temperature of each stage showed a good correlation with the incipient temperature of the indicator gases, namely C2H2, C2H4, and C2H6, and the appearance of the above gases can be used to characterize the degree of spontaneous combustion of coal.
| S-EPMC9045653 | biostudies-literature
Project description:The feasibility and accuracy of the risk prediction of gas extraction borehole spontaneous combustion is improved to avoid the occurrence of spontaneous combustion in the gas extraction borehole. A gas extraction borehole spontaneous combustion risk prediction model (PSO-BPNN model) coupling the PSO algorithm with BP neural network is established through improving the connection weight and threshold values of BP neural network by the particle swarm optimization (PSO) algorithm. The prediction results of the PSO-BPNN model are compared and analyzed with that of the BP neural network model (BPNN model), GA-BPNN model, SSA-BPNN model and MPA-BPNN model. The results showed as follows: the average relative error of the PSO-BPNN model was 4.38%; the average absolute error was 0.0678; the root mean square error was 0.0934; and the determination coefficient was 0.9874. Compared with the BPNN model, the average relative error, average absolute error and root mean square error decreased by 9.35%, 0.1707 and 0.2056 respectively; and the determination coefficient increased by 0.1169. Compared with the GA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 3.19%, 0.0602 and 0.0821 respectively; and the determination coefficient increased by 0.0320. Compared with the SSA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 5.70%, 0.0820 and 0.1100 respectively; and the determination coefficient increased by 0.0474. Compared with the MPA-BPNN model, the average relative error, average absolute error and root mean square error decreased by 3.50%, 0.0861 and 0.1125 respectively; and the determination coefficient increased by 0.0488, proving that the PSO-BPNN model is more accurate than the BPNN model, GA-BPNN model, SSA-BPNN model and MPA-BPNN model as for prediction. When the PSO-BPNN model was applied to three extraction boreholes A, B, and C in a coal mine of Shanxi, the prediction results were better than the BPNN model, GA-BPNN model, SSA-BPNN model and MPA-BPNN model, proving the accuracy and stability of the PSO-BPNN model in predicting risk of borehole spontaneous combustion in other mine.
| S-EPMC10762070 | biostudies-literature
Project description:The relationship between the properties of coal and its tendency to spontaneous combustion is critical for the environment, safety concerns, and economy. In this study, to eliminate the complex influence of moisture; the samples having similar moisture content were selected from Shanxi and Henan provinces. The chemical properties, physical properties, and tendency of coal samples to spontaneous combustion were characterized based on the conventional analysis, mercury intrusion porosimetry, fractal dimensions, and crossing point temperature (CPT). The results confirmed that the coal rank, volatile matter, oxygen contents, and fixed carbon content had a good linear relationship with the CPT. The relationship between the ash content and CPT presented a "U-shaped" non-linear correlation. For the pore size distribution, the total pore volume also possessed a linear positive correlation with the CPT. The fractal curves could be distinctly divided into two stages: low-pressure (<20?MPa) and high-pressure (>20?MPa), from which the fractal dimensions were obtained using the Sponge and Sierpinski models. The relationship between the fractal dimensions (Ds1, Ds2, and Dg1) and CPT could be divided into two distinct stages: a decrease in the CPT with increasing fractal dimensions (2.6-2.85), and then an in increase in the CPT. CPT decreased with increasing parameters of D1, D2, H, and D10, and it gradually increased with increasing D-10-D10, D-10-D0, and D0-D10. The above characteristics are important to comprehensively and systematically reveal the mechanism of spontaneous combustion.
| S-EPMC7189386 | biostudies-literature
Project description:The accurate prediction of coal spontaneous combustion (CSC) in the goaf areas of coal mines is a vital aspect of the migration from passive to active fire prevention and control. However, CSC is highly complicated and existing technologies cannot accurately monitor coal temperatures over wide expanses. Thus, it may be beneficial to assess CSC based on various index gases produced by the reactions of coal. In the present study, the CSC process was simulated by temperature-programmed experiments, and the relationships between index gas concentrations with the coal temperature were determined using logistic fitting functions. CSC was divided into seven stages, and a coal seam spontaneous ignition early warning system involving six criteria was established. Field trials demonstrated that this system is a viable approach to predicting coal seam fires and meets the requirements for the active prevention and control of coal combustion. This work establishes an early warning system based on specific theoretical guidelines that permits the identification of CSC and the implementation of active fire prevention and extinguishing measures.
| S-EPMC9947963 | biostudies-literature
Project description:Spontaneous combustion of coal is one of the major hazards threatening production safety during longwall mining. Mining-induced voids, which provide passages for air leakage, are the key factor triggering spontaneous combustion of coal in longwall goafs. In this study, a comprehensive method, which combined pressure balance, grouting injection, and filling fissures, was proposed to prevent spontaneous combustion of coal in longwall goafs with complex air leakage. Field engineering practice was carried out in Sitai Coal Mine in China. The results demonstrated that with the application of the proposed method, in the working face, the concentration of CO was decreased from 31ppm to 0 and the air leakage quantity was decreased from 261 to below 80 m3min-1. The gas samples analysis from the gob areas also indicated that concentrations of O2 and CO were successively decreased, indicating that the risk of spontaneous combustion of coal in goafs was eliminated. The above mentioned analysis indicates that, the method proposed in this study is useful and efficient. Successful application of this technology could provide reference for the treatment of other coal mines.
| S-EPMC6396930 | biostudies-literature
Project description:Due to the problems related to the numerous factors affecting the spontaneous combustion of goaf coal, such as sudden, uncertain, and dynamic changes, and the fact that the weight of the indexes in the prediction model of the spontaneous combustion risk is difficult to determine, an improved Criteria Importance Through Inter-criteria Correlation (CRITIC) modified Technique for Order of Preference by Similarity to Ideal Solution G2-(TOPSIS) dynamic prediction model of goaf spontaneous combustion was developed. An optimal decision-making model was established by introducing the Euclidean distance function, and the function-driven type G2 weighting method was modified using the differential-driven type weighting method of the CRITIC. In addition, the comprehensive weights of each index were obtained. An update factor was introduced to obtain the dynamic weight, and the primary-secondary relationship of the risk factors affecting the spontaneous combustion of goaf was evaluated. Based on the G2 weighting method, which approximates the driving function principle of the ideal solution ranking method (TOPSIS), a G2-TOPSIS goaf spontaneous combustion risk assessment model was established. The degree of closeness was analyzed and the risk grade of the goaf spontaneous combustion was finally predicted. The sub-model was applied to the goaf of working face 1303 in the Jinniu Coal Mine. It was concluded that the air leakage duration was the dominant factor inducing the risk of the spontaneous combustion of the goaf. The risk grade of spontaneous combustion of the goaf is Class III, and the predicted results are consistent with the actual situation. The improved CRITIC-G2-TOPSIS dynamic model was demonstrated to be scientific in predicting the goaf spontaneous combustion risk, and these research results have important popularization and application value.
| S-EPMC8550420 | biostudies-literature
Project description:Air leakage from surface mining-induced fissures can easily cause spontaneous combustion of residual coal in the goaf, which threatens the safe production of the underground working face. In order to study the air leakage law of the goaf under the surface air leakage and the prevention and control technology of spontaneous combustion of residual coal. Based on engineering data from the 6104 working face of the Chuancao Gedan coal mine, this study uses a combination of theoretical analyses, numerical simulations, and field observations to study the dynamic distribution characteristics of the air leakage velocity of surface mining-induced fissures in shallow coal seams, the distribution characteristics of relative pressure, the air leakage velocity, the air leakage flow field, the distribution ranges for the "three zones" of spontaneous combustion in the goaf, and a reasonable range for the pressurized ventilation of the working face. The results show that there is a quadratic relationship between the air leakage speed from the surface mining-induced fissures in shallow coal seams and the distance from the working face. The air leakage speed decreases as the distance from the working face increases, and the air leakage speed in the middle of the working face is slower than the air leakage on either side of the goaf. The pressure difference between the goaf and the surface mining-induced fissures is the root cause of air leakage into the goaf, and a change in the pressure difference has a significant impact on the air leakage flow field and the distributions of the "three zones" of spontaneous combustion in the goaf. When the pressure difference between the ground surface and the working face is maintained within the range of 200~-200 Pa, air leakage is effectively reduced, and the spontaneous combustion of residual coal is inhibited. The research results reveal the air leakage mechanism in the goaf of shallow coal seams and provide a reference for the prevention and control of spontaneous combustion of residual coal in the goaf.
| S-EPMC9232134 | biostudies-literature
Project description:BACKGROUND: Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM) algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain. RESULTS: A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i) the tetrapeptide cleavage sites, (ii) the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii) the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available) were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants. CONCLUSION: This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.
| S-EPMC1764470 | biostudies-literature
Project description:BackgroundFunctional annotation of rapidly amassing nucleotide and protein sequences presents a challenging task for modern bioinformatics. This is particularly true for protein families sharing extremely low sequence identity, as for lipocalins, a family of proteins with varied functions and great diversity at the sequence level, yet conserved structures.ResultsIn the present study we propose a SVM based method for identification of lipocalin protein sequences. The SVM models were trained with the input features generated using amino acid, dipeptide and secondary structure compositions as well as PSSM profiles. The model derived using both PSSM and secondary structure emerged as the best model in the study. Apart from achieving a high prediction accuracy (>90% in leave-one-out), lipocalinpred correctly differentiates closely related fatty acid-binding proteins and triabins as non-lipocalins.ConclusionThe method offers a promising approach as a lipocalin prediction tool, complementing PROSITE, Pfam and homology modelling methods.
| S-EPMC2813246 | biostudies-literature