Geological studies are very important at different steps of mining activities. Different uncertain geological criteria and factors show a significant impact during the underground mining operations and mineral extraction process. The current study reports on the evaluation and classification of coal seams for methane drainage-ability (MDA) through a fuzzy hybrid approach. This problem was investigated due to the importance of uncertain geological factors in the process of MDA and the necessity to evaluate safety operations in underground coal mines. The important criteria involved are depth, thickness, and uniformity of coal seam, joints and cleats conditions, roof quality, coal seam gas content, underground water condition, and permeability of coal seam. The two-stage fuzzy classification approach was used to analyze the effectiveness of the uncertain geological criteria. Also, fuzzy cognitive map (FCM) method was used to calculate weights for geological criteria. The used FCM method is based on the Hebbian algorithm and metaheuristic methods. The Hebbian learning algorithm is made from a hybrid learning algorithm of nonlinear heuristics and differential evolution. In addition, fuzzy intervals of criteria were calculated based on technical reports and other scientific studies. Then, the rank of each coal seam calculates by fuzzy T-norms. The proposed system was employed to classify the coal seams for MDA in Parvadeh coalfield, Iran. The results showed that the C1, C2, B2, B1, and D coal seams were classified in the “very good,” “good,” “moderate,” “poor,” and “very poor” categories, respectively. The proposed fuzzy hybrid approach provides a new logical tool in the selection of coal seam for methane drainage operation and can reduce the risk of methane drainage projects in underground coal mines.
展开▼
