Carbonate reservoirs are one of the most important fossil fuel sources, and the acidizing stimulation is a practical technique for improving the recovery of carbonate reservoirs. In this study, the improved two-scale continuum model, including the representative elementary volume (REV) scale model and the upscaling model, is used to study the acidizing process with an isolated fracture. Based on this model, a comprehensive discussion is presented to study the effect of the physical parameters of the isolated fracture on the acidizing results and dissolution images, including the isolated fracture geometry, location, and morphology. Results show that the isolated fracture system is still the target system for the acidizing stimulation. The isolated fracture provides a limited contribution to the core porosity. The permeability of the core sample with fracture can be obviously increased only when the fracture penetrates through the whole sample. The existence of the isolated fracture reduces the consumption of acid solution to achieve a breakthrough. The acidizing curve is sensitive to the change of the length, aperture, and position of the isolated fracture. The acidizing curve difference corresponding to different rotation angles has not changed significantly for clockwise rotation and anticlockwise rotation groups.

Coal combustion is considered to be the key source of nitrogen oxide (NOx) emissions in thermal power plants. Methods for effective reduction in these emissions are critically sought on the national and global levels. Such methods typically achieve this goal through accurate modeling and prediction. However, such modeling process is difficult because of the complexity of the NOx emission mechanisms and the influence of many factors. Furthermore, real-operation data of power plants tend to be centralized in some local areas because of working condition experiment so that no single model can deal with the complicated and changeable boiler production processes. In this paper, we address this problem and propose a model intelligent combinatorial algorithm (MICA). First, the actual production data are preprocessed by a wavelet denoising algorithm, and the model input variables are selected based on a random forest algorithm. Then, several models for NOx emission prediction are constructed by various data-driven algorithms. Finally, a C4.5 algorithm is applied to intelligently combine these models. The experimental results indicate that the proposed algorithm can construct an accurate prediction model for NOx emissions based on actual operating data. The mean absolute percentage errors are within 1%. Moreover, a correlation of 0.98 between predicted and measured values was obtained by applying the MICA model.

Burning of coal in power plants produces excessive nitrogen oxide (NOx) emissions, which endanger people’s health. Proven and effective methods are highly needed to reduce NOx emissions. This paper constructs an echo state network (ESN) model of the interaction between NOx emissions and the operational parameters in terms of real historical data. The grey wolf optimization (GWO) algorithm is employed to improve the ESN model accuracy. The operational parameters are subsequently optimized via the GWO algorithm to finally cut down the NOx emissions. The experimental results show that the ESN model of the NOx emissions is more accurate than both of the LSTM and ELM models. The simulation results show NOx emission reduction in three selected cases by 16.5%, 15.6%, and 10.2%, respectively.