The purpose of the work is demetallization and desulfurization of heavy oil residues using adsorbents. Optimal parameters of the demetallization and desulfurization processes of heavy vacuum fraction of Pavlodar Petrochemical Plant LLP in the presence of a zeolite adsorbent containing vanadium oxide xerogel were established: temperature 340 °C, pressure 1 atm., bulk feed rate 1 h⁻¹. As a result of the process the extraction degree of vanadium, nickel and iron were 90, 70 and 60%, respectively, and the sulfur content decreased from 1.97 to 1.36%. Composition of the chemical adsorbent for demetallization and desulfurization of heavy vacuum fraction is established, too. Content of vanadium, iron and sulfur increase in composition of the adsorbent after treatment of heavy vacuum fraction, which confirms its high adsorption capacity regarding to metals and sulfur.

Nowadays in the world, due to the constant desire for recycling, many countries are considering the use of recycled plastics on roads. Modification of bitumen for roads in Kazakhstan is considered one of the most suitable and popular approaches. This paper presents the results of research on the modification of bitumen by recycled plastics from waste materials. The paper describes the details of the use of plastic waste as bitumen modifiers, with a specific focus on recycled plastics and how they can potentially be used to enhance bitumen performance and the road durability. The main physical and mechanical characteristics of the modified bitumen were determined after routine tests, penetration and plasticity, softening temperature, brittleness temperature on Fraas and microscopic analysis. The morphology of the modified bitumen was studied using scanning electron microscopy. The results confirm that the modified bitumen complies with the requirements for polymer-bitumen binder of Kazakhstani standards and is suitable for the production of modified bitumen by its physical and chemical characteristics.

This review discusses recent research findings spanning the last two decades concerning ultrasonic technologies applicable to the oil, gas, and coal sectors. Various experiments conducted in laboratories have demonstrated the efficacy, cost-effectiveness, and environmental friendliness of ultrasound in recovering and processing oil, bitumen, coal, and oil shale. Ultrasound enhances formation permeability, coal gas permeability, and oil viscosity, particularly when delivered in short, powerful pulses at medium frequencies. Combining ultrasound with traditional recovery methods has shown promising results, boosting recovery efficiency by up to 100%. At the same time, ultrasonic treatment reduces the use of traditional reagents, thereby reducing environmental pollution. Moreover, ultrasound treatment shows potential in tasks such as separating oil–water emulsions, desulfurization, dewaxing oil, coal enrichment, and extracting valuable metals from metal-bearing shales through hydrometallurgical leaching. However, the widespread industrial implementation of ultrasonic technology necessitates further field and mathematical research.

A study on the effect of silica nanoparticles (SNPs) dispersion in bitumen is herein reported. First, the size of the nanoparticles was finely tuned by controlling the experimental conditions during their synthesis, obtaining spherical SNPs with diameter ranging from 95 up to 900 nm. Subsequently, SNPs were embedded with peripheral amine groups by using APTES (3-aminopropyltriethoxysilane) as functionalized agent (NH₂@SNP), and ultimately long alkyl chains were grafted by reacting the free amine with an alkylated aldehyde (C₁₄N@SNP). All SNPs (ca. 1 wt%.) were dispersed in bitumen to probe their effect on the rheological properties of bitumen. No significant change in the thermorheological properties of bitumen was observed upon varying the size of the SNPs. Slight improvement was observed when using NH₂@SNPs, while the best results were obtained by using C₁₄N@SNPs, showing the crucial role that hydrophobic substituents play in bitumen binders which leads to significant improvements.

Using the mixture of carbonized rice husk and shungite from the Kazakhstan Koksu deposit and the experimentally determined oil sorption capacity from contaminated soil with oil originating in the Karazhanbas oil field, a set of Artificial Neural Network (ANN) models were built for sorption predictions. The ANN architecture design, training, validation and testing methodology were performed, and the sorption capacity prediction was evaluated. The ANN models were successfully trained for capturing the sorption capacity dependence on time and on a carbonized rice husk and shungite mixture ratio for the 10% and 15% oil-contaminated soil. The best trained ANNs revealed a very good prediction capability for the testing data subset, demonstrated by the high coefficient of the determination values of R² = 0.998 and R² = 0.981 and the mean absolute percentage errors ranging from 1.60% to 3.16%. Furthermore, the ANN sorption models proved their interpolation ability and utility for predicting the sorption capacity for any time moments in the investigated time interval of 60 days and for new values of the shungite and rice husk mixture ratios. The ANN developed models open opportunities for planning new experiments, maximizing the sorption performance and for the design of dedicated equipment.