The organic components of heavy tar from Pavlodar Petrochemical Plant (Pavlodar, Kazakhstan) are investigated. Gas–liquid chromatography reveals the presence of 13 paraffinic hydrocarbons, each with between 9 and 21 carbon atoms. Most are low-molecular-weight paraffins (C9–C13), present in quantities of 2.88–7.12
The coke industry, traditionally regarded as capital- and energy-intensive, with challenging operating conditions, has undergone a fundamental technical transformation in the last decade. The present work reviews the current state of automated process control systems in the coke industry and emerging trends. The evolution of architectural solutions is considered: from three-level hierarchical systems to cyberphysical systems integrating digital twins, predictive analytics, and adaptive algorithms based on machine learning. Particular attention is paid to the concept of intelligent coking (the smart coke plant), including combinatorial modeling of coke properties, optimal thermal control, robotized diagnostics, digital resource stores, and smart coal flow dispatching. The results of introducing machine vision systems for monitoring gas liberation (at NLMK), algorithms for detecting anomalies (at EVRAZ ZSMK), smart coal blending and procurement management (at Risun Group, China, and Shagang Group), digital 3D stores based on laser scanning and high-precision positioning, and the introduction of systems for automatic assessment of coke quality are considered. The patent literature regarding coke plant automation is analyzed, and key patent holders are identified (SunCoke, Shagang, and Paul Wurth). Unsolved problems are noted: the lack of labeled industrial data, the low baseline level of automation at Russian enterprises, skill deficits, and the need to standardize communication protocols. The review confirms the importance of the Coke Plant 4.0 paradigm, on the basis of hybrid (physical–statistical) models and end-to-end quality tracking from the coal batch to the coke produced.
The possibility of predicting the indices Ro and Vdaf for Kuznetsk Basin caking coals on the basis of regression models and approximating their relationship on the basis of regression models for a sample of coal from a specific region of the mine is investigated. In two-stage monitoring, the first step is identification of reliable results from analysis of the classification indices in State Standard GOST 25543-2013 derived from prior research or in an existing database; then, on that basis, the second step is correct assessment of the coal’s rank composition by seam, product, and final blends. This approach provides information regarding the quality of the coal supplied for the production of concentrates and also the coal batch for the production of metallurgical coke. In addition, it ensures reliable storage of coal in terms of its rank components, so as to permit the formulation of batch with specified parameters and hence quality control of the coke.
The pore structure of composite sorbents based on lignite coke is analyzed by means of fractal theory. The fractal sample dimensions are calculated using the Frenkel–Halsey–Hill (FHH) model, for isotherms of low-temperature nitrogen adsorption at 77 K obtained using an ASAP-2020 system. According to the model, the dependence of ln V on ln [ln (P0/P)] should be a straight line of negative slope S, from which the fractal dimension D may be calculated. However, the correct D value will only be obtained if the appropriate section of the line is selected. In the present work, the first linear section in the range between a monolayer coating and a multilayer coating is selected, in the portion where no surface smoothing is seen. The minimum coating thickness n in the selected range of relative pressure is 1.0. The maximum n is close to 1.3 for all the samples. Analysis of the results shows that the fractal dimensions of the composite sorbents based on lignite coke are between 2.31 and 2.52. In the oxidation of lignite by nitric acid and modification by iron salts, the textural characteristics grow on account of the formation of new pores and decrease in fractal dimension as a result of smoothing of the existing pore surface. Altogether, these factors lead to the formation of a pore system that is more orderly in shape and size.
The sorptional properties of natural Zh coal samples selected at different intervals over the control borehole (including the reference pressure zone) are investigated. A special device is developed and produced for the laboratory experiments. Gas kinetic curves obtained in experiments on the cyclic saturation of samples with methane, in which the gas pressure is increased and then released, characterize the gas liberation from the saturated sample and reflect the desorption properties of the coal sample. On the basis of the gradients of the absorption and desorption rates, they are compared.
