ABSTRACT
With increasing concerns about climate change and resource-environmental limitations, the green development of the mining industry has become mainstream and gained much support. Driven by the concept of sustainable and green development, China has made the advancement of green mine construction a crucial part of establishing an eco-society and has put forward the overall goal of green mines. An important future strategy is to evaluate a large number of mines. However, developing scientific, reliable, and comprehensive index systems and evaluation methods is extremely difficult because of the objective complexity of green mine evaluation and the fuzziness of some indicators. The kernel method and intuitionistic fuzzy set can effectively handle these problems. This study proposed a comprehensive evaluation index system and a hybrid evaluation method based on the kernel distance measure and intuitionistic fuzzy TOPSIS method. The index system contains 22 indicators considering six aspects: mining area environment, resource development mode, comprehensive utilization of resources, energy saving and emission reduction, technical innovation, and corporation management. The hybrid evaluation method was applied to the practical assessment of ten green mines in Panxi, China. Comparative analyses were carried out to demonstrate its applicability and sensitivity. The results verify that the hybrid method can fully depict the construction achievements of green mines in all aspects with strong reliability and stability. This approach is a valuable reference for evaluators and decision-makers in government departments.
Subject(s)
Fuzzy Logic , Reproducibility of Results , ChinaABSTRACT
This paper proposes a fluid classifier for a tight reservoir using a quantum neural network (QNN). It is difficult to identify the fluid in tight reservoirs, and the manual interpretation of logging data, which is an important means to identify the fluid properties, has the disadvantages of a low recognition rate and non-intelligence, and an intelligent algorithm can better identify the fluid. For tight reservoirs, the logging response characteristics of different fluid properties and the sensitivity and relevance of well log parameter and rock physics parameters to fluid identification are analyzed, and different sets of input parameters for fluid identification are constructed. On the basis of quantum neural networks, a new method for combining sample quantum state descriptions, sensitivity analysis of input parameters, and wavelet activation functions for optimization is proposed. The results of identifying the dry layer, gas layer, and gas-water co-layer in the tight reservoir in the Sichuan Basin of China show that different input parameters and activation functions affect recognition performance. The proposed quantum neural network based on hybrid parameters and a wavelet activation function has higher fluid identification accuracy than the original quantum neural network model, indicating that this method is effective and warrants promotion and application.