The calorific value of coal is the basic technical basis for calculating parameters such as boiler heat balance, thermal efficiency, and boiler output. The calorific value of coal has different meanings, such as the calorific value of the cartridge, the high calorific value of coal, and the low calorific value of coal to generate heat at a high level of constant humidity and no ash. This paper focuses on the analysis of the structure and algorithm characteristics of artificial neural network and RBF neural network. On this basis, the predictive modelling of the received low-level calorific value is carried out. Through the test summary, the predictiveness of the neural network is better than the empirical formula. For the prediction problem with small sample size, the RBF network has better prediction performance. In addition, the quality of the sample, including its quantity and comprehensiveness, has an important impact on the predictive performance and generalization ability of the model.

Coalbed methane development represents a complex system engineering operation that involves complex technology, many links, long cycles, and various risks. If risks are not controlled in a timely and effective manner, project operators may easily cause different levels of casualties, resource waste and property loss. To evaluate the risk status of coalbed methane development projects, this paper constructs a coalbed methane development risk assessment index system that consists of six first grade indexes and 45 second grade indexes. The weight of each index is calculated based on the structure entropy weight method. Then, a theoretical model for risk assessments of coalbed methane development is established based on the matter-element extension method. Finally, the model is applied to analyze a coalbed methane development project in the southern Qinshui Basin of China. The results show that the overall risk level of the coalbed methane development project is Grade II, indicating that the overall risk of the project is small, but the local risk of the project needs to be rectified in time. The assessment results are consistent with the actual operation of the project, indicating that the established risk assessment model has good applicability and effectiveness.

Coalbed Methane development is a complex system project with complex technology, many links and long cycle. It is highly susceptible to risks such as legal, technical and management during the development process. In order to scientifically measure the overall risk of CBM development projects, reduce resource waste and property losses caused by risk management failure. This paper constructs a risk assessment index system for CBM development projects consisting of six first-level indicators and 28 second-level indicators. The weight of each indicator is calculated by the structural entropy weight method. Secondly, based on the grey clustering analysis method, the theoretical model of risk assessment for coalbed methane development projects is established. Finally, an empirical study on a coalbed methane development project in Qinshui County, Shanxi Province was carried out using the evaluation system. After calculation, the overall risk level of the project is Grade II. The evaluation results are consistent with the actual situation of the enterprise, indicating that the established evaluation system has certain effectiveness and feasibility.