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Gas outburst poses a huge threat to the safe production of coal mines. Therefore, the prediction of gas outburst has always been a hot topic for researchers. In recent years, the use of artificial intelligence algorithms for gas outburst prediction has made progress, such as using BP neural network, GA algorithm, and SVM algorithm. Despite these progresses, predicting the gas outburst more accurately and efficiently still remains a great challenge. In this work, an algorithm based on grey relational analysis and SVM using adaptive particle swarm optimization (APSO-SVM) for gas outburst prediction is proposed. Grey relational analysis was used to extract the four most relevant ones from nine gas outburst prediction parameters (geological structure zone distance, coal seam gas content, gas release initial velocity, gas desorption index-K1, drill cuttings volume, coal seam depth, coal seam thickness, coal destruction type, and coal firmness coefficient) to give the needed parameters for SVM model. Higher prediction accuracy was then obtained with the selected parameters composed by coal seam gas content, gas release initial velocity, gas desorption index-K1, and drill cuttings volume. Moreover, adaptive particle swarm optimization (APSO) was used to optimize the penalty factor and kernel parameters of the support vector machine to improve the global search ability and avoid the occurrence of the local optimal solutions. The APSO-SVM model was applied to the prediction of gas outburst in 31004 tunneling face of Xinyuan Coal Mine, Yangquan City, Shanxi Province, China. We further introduced the criteria of accuracy, precision, recall, and F 2 -score to evaluate the prediction results of different models. The results show that, in the gas outburst prediction, the accuracy of the APSO-SVM model is 98.38%, the precision and recall are both 100%, and F 2 -score is 1. Comparative studies confirm that APSO-SVM displayed better performance than SVM and PSO-SVM models for the applied grey relational analysis assisted gas outburst prediction. These obtained results indicate the validity of APSO-SVM model for gas outburst prediction. [ABSTRACT FROM AUTHOR]

The accurate and stable prediction of electricity consumption is essential for intelligent power systems in rapidly developing countries. Grey prediction model is one of choices for prediction under the condition of limited historical data. Nonetheless, it seems rather sceptical using single-variable grey prediction model to predict the dynamics of a complex system. This paper presents a novel multivariable grey prediction model based on first-order linear difference equation for long-term electricity consumption prediction. The proposed model solves the problem of parameter estimation and variable prediction deriving from different approaches through rewriting the whitenization equation of multivariable grey model (MGM(1, m)). To validate the effectiveness of the proposed hybrid model, the electricity consumption is estimated and predicted over the data from Shanxi province and Beijing city in China from 1999 to 2018. The results show that the hybrid model provides a better estimation and prediction performance compared with other prediction model for predicting electricity consumption. [ABSTRACT FROM AUTHOR]

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Chinese dwarf cherry (Ouli) is a native shrub in northwest China with high ecological value, but the nutritive potential remains to be explored. In this study, the total content of polyphenols, flavonoids, proanthocyanidins, and anthocyanins of Ouli from 30 germplasms were measured, and the antioxidant activities were evaluated by FRAP and ABTS assays. The polyphenol profile was determined by ultra-high-performance liquid chromatography-high resolution mass spectrometry. Grey relational analysis and partial least squares were utilized to build chemometrics models for selecting antioxidant compounds. The contents of total flavonoids, proanthocyanidins, phenols and total anthocyanidins of 30 germplasms of Ouli were 119.92–668.76 mg/100 g fresh weight (FW), 90.81–555.44 mg/100 g FW, 148.42–390.43 mg/100 g FW, 0.02–17.26 mg/100 g FW, respectively. A total of 61 compounds were identified, including 40 flavonoids (26 flavonols, 4 anthocyanins, 6 dihydroflavonols, 2 dihydrochalcones, and 2 flavones), 12 proanthocyanidins, 6 phenolic acids, and 3 types of other class compounds (one stilbene and two phenols). The proanthocyanidin compounds in Ouli contributes most to the total antioxidant capacity with the antioxidant rank that procyanidin trimer > tetramer > dimer ≈ monomer. Catechin and procyanidin B1 were the most abundant compounds and were 18.45–162.50 μg/g FW and 30.52–149.42 μg/g FW, respectively. Shanxi (SX) 2 and SX10 could be considered as potential germplasms, and proanthocyanidins could be chosen as a characteristic nutrient evaluation index of Ouli. This study not only takes a key step to lay the groundwork for further research of Ouli but also provides a reference for the discovery of the functional ingredients of other foods. [Display omitted] [ABSTRACT FROM AUTHOR]

As well-known, not all abandoned mines have abundant AGM resources. Also not all abandoned mines with abundant AGM resources are suitable for surface gas development. It is a very timely and valuable work on how to scientifically and quickly optimize the target abandoned mines suitable for surface development from many abandoned mines. This research includes three parts, e.g., the development suitability evaluation of the trap in the stope, the optimization of target abandoned mine and the field application. In the first part, the evaluation system and method of trap development suitability are established. AGM resources, storage conditions, the accumulated water and the developing economy are selected as the development suitability evaluation indexes. Each evaluation index is divided into three levels, and the method to determine the grading standard value of each evaluation index is proposed. The multi-factor comprehensive evaluation method of grey relational analysis is used in the evaluation and divided the trap into three types: suitable trap, less suitable trap and unsuitable trap according to the degree of development suitability. In the second part, the optimization method of the target abandoned mine is proposed based on the theory and development suitability of the gas trap. Firstly, screen out abandoned mines with obvious safety risks or poor economic benefit prospect of gas development. Secondly, determine the number of gas traps and suitable traps in each candidate. Thirdly, the candidate abandoned mine with the largest total gas resources in suitable traps is selected as the most favorable abandoned mine. In the third part, taking a medium-sized coal mine in the southeast of Shanxi Province as a field test mine, the gas trap determination, resource calculation and development evaluation of 17 gobs were carried out. The evaluation results show that none of them are suitable trap. The results of surface gas development in the gobs of the mine confirmed the evaluation results. • Not all closed gobs can be form traps, and become recoverable gas reservoirs. • Established the evaluation system for development suitability of gas traps. • The optimization method of target abandoned mine is established. • The theory of gas trap and its evaluation have been applied in the field. [ABSTRACT FROM AUTHOR]