This paper describes an experiment of underground coal gasification in experimental equipment. The experiment was done within the range of the project APVV-0582-06, in May 2010. During the period of 63 h there was gasified amount of 214 kg of coal in experimental gasifier with average rate of 3.4 kg/h. The air, was the primary gasification agent in the experiment and its total volume was 661 Nm3. Oxygen was used only in a short period of the experiment. The produced gas reached an average calorific value of 3.27 MJ/Nm3. The calorific value was slightly higher (4.13 MJ/Nm3) when was using oxygen as gasification agent. This article talks not only about the analysis of the achieved results from UCG but also used experimental gasifier, input supply system of a gasification agent, and monitoring system

The basic oxygen steelmaking process (BOS) faces the issue of the absence of information about the melt temperature and the carbon concentration in the melt. Although deterministic models for predicting steelmaking process variables are being developed in metallurgical research, machine-learning models can model the nonlinearities of process variables and provide a good estimate of the target process variables. In this paper, five machine learning methods were applied to predict the temperature and carbon concentration in the melt at the endpoint of BOS. Multivariate adaptive regression splines (MARS), support-vector regression (SVR), neural network (NN), k-nearest neighbors (k-NN), and random forest (RF) methods were compared. Machine modeling was based on static and dynamic observations from many melts. In predicting from dynamic melting data, a method of pairing static and dynamic data to create a training set was proposed. In addition, this approach has been found to predict the dynamic behavior of temperature and carbon during melting. The results showed that the piecewise-cubic MARS model achieved the best prediction performance for temperature in testing on static and dynamic data. On the other hand, carbon predictions by machine models trained on joined static and dynamic data were more powerful. In the case of predictions from dynamic data, the best results were obtained by the k-NN-based model, i.e., carbon, and the piecewise-linear MARS model in the case of temperature. In contrast, the neural network recorded the lowest prediction performance in more tests.