Advanced Heterogeneous Fenton Treatment of Coalbed Methane-Produced Water Containing Fracturing Fluid

Deep Learning Model for Prediction of Diffusion in Defect Substances

Actual diffusion activity function is an important metric utilized to describe the diffusion activities of a vacancy defect substance. In this paper, we propose a deep learning three-dimensional convolutional CNN model (D3-CNN). A 3D convolution has its kernel slides in three dimensions as opposed to two dimensions with 2D convolutions. 3D convolution is more suitable for three-dimensional data. We also propose an amplification learning technique to predict the actual diffusion activity of a vacancy defect substance, which is impacted by the geometrical parameters of the defect substance and the vacancy distribution function. In this model, the geometric parameters of a three-dimensional constructed vacancy defect substance are generated. The 3D dataset is obtained by the atoms diffusion defect (ADD) simulation model. The geometric parameters of the 3D vacancy defect substance are computed by the proposed amplification technique. The 3D geometric parameters and the diffusion activity values are applied to a deep learning model for training. The actual diffusion activity values of a substance with a vacancy size ranging from size 0.52 mm to 0.61 mm are used for training, and the actual diffusion activity values of substance vacancy of size between 0.41 and 1.01 are classified by the three-dimensional network. The model can realize high speed and accuracy for the actual diffusion activity value. The mean relative absolute errors between the D3-CNN and the ADD models are 0.028–7.85% with a vacancy size of 0.41 to 0.81. For a usual sample with a vacancy of size equal to 0.6, the CPU computation load required by our model is 14.2 × 10−2 h, while the time required is 15.16 h for the ADD model. These results indicate that our proposed deep learning model has a strong learning capability and can function as an influential model to classify the diffusion activity of compound vacancy defect substances.

Numerical Simulation and Experimental Analysis of Dynamic Continuous Operation of Low-Concentration Coalbed-Methane-Mixing Device

The concentration of low-concentration coalbed methane extracted from underground coal mine fluctuates greatly, which does not meet the requirements of intake concentration of coalbed-methane utilization devices. Due to this fluctuation, the coalbed-methane-utilization device cannot maintain stable and safe operation. The gas-mixing device is mainly used in coalbed-methane-utilization systems, providing each with a stable feed gas source with qualified concentration. In order to solve the problems of unsatisfactory uniformity of gas mixing and the large resistance of the existing coalbed-methane-mixing device, the mathematical model of the internal flow of the gas-mixing device is established. The influence of the internal structure of the gas-mixing device on the change in the uniformity of gas mixing and resistance loss is studied by numerical simulation and experiment. When the flow is 7000 Nm3/h, 50,000 Nm3/h and 160,000 Nm3/h, respectively, the spiral structure combination of L-N-R, N-L-R and L-N-R is optimal (L, R, N indicate left rotation, right rotation and without setting screw, respectively). There are some defects in the processing technology of the experimental device, which make the simulation and experimental data different. The numerical simulation of the gas-mixing process inside the unit can provide technical means for reducing resistance and improving the uniformity of gas mixing, and provide a stable gas source and safety measures for the coalbed-methane-utilization unit.

Fenton's reagent-enhanced supercritical water oxidation of wastewater released from 3-hydroxypyridine production

A study on Fenton's reagent-enhanced supercritical water oxidation (SCFO) of wastewater released from 3-hydroxypyridine production was carried out in this paper. The effects of temperature, oxidant multiple, residence time, Fe2+ concentration, and pH on the degradation efficiency of wastewater were investigated. The Plackett-Burman test was designed to evaluate various factors, namely, temperature, oxidant multiple, and pH, which were found to significantly affect degradation efficiency. Response surface analysis was performed to optimize the parameter levels of the main influencing factors. The results indicated that the optimal conditions required for the oxidative degradation of wastewater in the SCFO systems were pH of 3, temperature of 473 °C, oxidant multiple of 7, Fe2+ concentration of 0.5 mg L-1, and residence time of 262.6 s (flow rate: 1.5 mL min-1). Under these conditions, the total organic carbon removal rate of the wastewater could reach 98.1%. The activation energy of the wastewater under SCFO conditions was 55.3 kJ mol-1, and the pre-exponential factor A was 52.8 s-1.