Coke and Chemistry最新文献

Preliminary data are gathered regarding the effectiveness of various reagents on the basicity of Kaa-Khem coal in laboratory flotation.

The following coal samples are compared: Kansk-Achinsk lignite from the Kaichasky-1 and Berezovsky mines; and long-flame (bituminous) coal from the Pervomaisky mine in the Kuznetsk Basin. Their composition and results of their technical analysis, elemental analysis, and petrography are presented. In all cases, the ash content is low (5.0–8.0%), as is the sulfur content (0.2–0.4%). In the mineral component of the lignite samples, the main elements are calcium, silicon, and sulfur; for the bituminous coal, they are silicon, aluminum, and iron. The samples are complex mixtures of macerals in the vitrinite, semivitrinite, inertinite, and liptinite groups. For all the coal samples, the reflectogram is free of discontinuities; the petrographic nonuniformity is minimal, with a predominance of the vitrinite group. The goal of the research is to select the optimal sample for the production of specialized activated coal additives in the thermal hydrocracking of heavy oil refining residues (tars). Such additives must meet requirements regarding the sulfur content, the content of the ash, its composition, and the porosity of the structure.

The aluminum content in coal wastes in the Kemerovo region of the Kuznetsk Basin is investigated. The possibility of using such waste to extract aluminum concentrate by acidic leaching using sulfuric acid and nitric acid is assessed. Complete aluminum extraction in one processing cycle is impossible, but around 96% extraction is possible by successive leaching. The content of aluminum oxide in the concentrate is 84.5%.

In the treatment of open-pit wastewater at mining enterprises, phenol is adsorbed on carbon sorbents, in equilibrium. The influence of minerals in the wastewater on this process is studied. At low phenol concentrations, inorganic compounds impede its adsorption. Iron and manganese ions have a particularly strong influence on phenol extraction. The basic adsorption parameters are calculated using the Freundlich and Dubinin–Astakhov equations.

Coal tar pitch is important as a binder in the production of construction materials and as the raw material in the production of pitch coke. It is widely used, under the name of electrode pitch, as a binder in the production of electrodes and anode mass. Various modifications improve the characteristics of electrode pitch. In the present work, the practicality of modification by low-temperature thermal oxidation (up to 300°C) is considered. Specifically, experiments are conducted on the thermal oxidation of Altai Koks electrode pitch. The apparatus employed consists of a reactor, a compressor, and a heating system. The softening point, fractional composition, and yield of the oxidation products are determined. Low-temperature thermal oxidation permits the production of higher-quality electrode pitch. The duration of low-temperature thermal oxidation and the maximum temperature corresponding to increase in the softening point of the pitch are established. This outcome of oxidation is of great interest. The experiments show that the content of the α₁ fraction does not increase in the low-temperature thermal oxidation of electrode pitch. The increase in softening point is associated with increase in the α₂ fraction during oxidation.

The physical processes in coke discharge from the furnace chambers of coke batteries are briefly considered. The basic operating problems of dust-free discharge units are identified, and recommendations are made for their elimination. Design requirements for dust-free coke discharge units in terms of economic efficiency and environmental safety are derived by analysis of operational experience. Some promising designs are considered.

The use of neural networks to improve the continuous pyrolysis of coal is discussed. In particular, attention focuses on the use of a two-layer fully connected neural network to control the process parameters and boost the efficiency of coal processing. The neural network is trained by using a backpropagation algorithm on the basis of experimental data, including data on the temperature, pressure, porosity, and product yield. The neural network permits more efficient fuel processing, reduces harmful emissions, and improves product quality. The network architecture and its training by means of real time data are described. The experiments indicate that coal pyrolysis consists of two competing processes: the destruction of the coal’s organic mass; and the condensation of carbon from the gas phase on the coke that forms. The results show that artificial intelligence has great potential for improving coal processing and creating more efficient and environmentally benign processing methods.