In open-pit mines, traditional truck dispatching systems typically make it hard to balance the demand for regular ore grade blending with the need for operating efficiency. Some people have suggested that intelligent, IoT-enabled systems could be a solution, but there is not always a thorough, controlled evaluation of how well they work compared to traditional baselines. This study fills in the gaps by creating a discrete-event simulation framework that lets you compare a traditional, proximity-based dispatching strategy with a new IoT-enabled method in a quantitative way. The suggested algorithm uses a closed-loop feedback system that works in real time to make judgments about how to blend ore dynamically, always bringing the stockpile back to the goal grade. We tested the two systems in the same way and looked at important performance indicators like grade consistency, manufacturing throughput, and equipment use. The simulation results show that the IoT-enabled system is much better since it can keep the target ore grade with great consistency and stability. These metallurgical improvements were accomplished with almost no effect on total production throughput (< 0.3% difference), and they made the workload of the excavator much more even, which means that operations are more sustainable. This study gives strong, quantitative proof that a smart, grade-aware dispatching system can improve both metallurgical quality and operational efficiency at the same time. The simulation framework shown here is a useful model for reducing risk and measuring the effects of IoT and AI technologies. This will help Mining 4.0 operations become more productive and sustainable.

This scientific work analyzes the research on data collection and transmission technologies for automatic control of high-frequency ozonators in water disinfection. The purpose of the study is to improve disinfection efficiency and optimize the system's operation by using IoT and artificial intelligence technologies in the control of ozonators. During the study, data collected by sensors were processed in real-time through an IoT system and analyzed using artificial intelligence. Results showed that the efficiency of ozonator control increased by 25%, and ozone consumption was reduced by 20%. The use of a PID controller allowed for a 15% reduction in the duration of the disinfection process. The application of sensor networks reduced system downtime by 10%, ensuring continuous operation. Service costs were reduced by up to 30%, improving economic efficiency. These results proved the effectiveness of integrating IoT and artificial intelligence technologies. Furthermore, this method offers an environmentally friendly solution. In conclusion, improving the automatic control systems of ozonators is a significant step towards enhancing the water disinfection process. © Published under licence by IOP Publishing Ltd.

Froth flotation remains one of the primary methods for mineral resource beneficiation. Despite its widespread use, the process’s overall efficiency depends heavily on accurate and timely control of its parameters, highlighting the need for improved automation technologies. Applied methods: This paper reviews current approaches to flotation process automation, focusing on smart technologies, real-time sensors, and predictive control systems. Particular attention is given to methods for pulp level regulation, airflow adjustment, reagent dosing, and pH monitoring. Additionally, the implementation of machine vision systems is considered as a complementary tool for flotation diagnostics and control. Main hypotheses and conclusions: The analysis of existing research underscores the limitations of traditional control strategies and supports the shift toward hybrid, adaptive, and model-free techniques. These emerging approaches provide greater flexibility and responsiveness to process fluctuations. The integration of advanced sensor technologies, visual data processing, and predictive modeling can notably enhance the stability of flotation operations and improve mineral recovery efficiency. Practical significance: The findings of this work offer a scientific foundation for developing innovative automation solutions tailored to flotation processes. These solutions are aimed at optimizing operational performance, reducing manual intervention, and lowering energy and reagent consumption. As such, the adoption of intelligent control systems presents a promising path toward more sustainable and cost-effective mineral processing in modern industry. © National Academy of Sciences of the Republic of Kazakhstan, 2025.

Hydrogen production from biomass has emerged as a promising renewable energy solution. However, significant challenges such as thermodynamic inefficiencies, high raw material costs, low hydrogen molar yields, and difficulties in using lignocellulosic feedstocks hinder its large-scale implementation. Conventional methods have not been able to effectively address these issues, which makes modern approaches, such as in silico strategies, essential. This review explores the role of computational models like genome-scale metabolic modeling, synthetic biology, and metabolic pathway reconstruction in overcoming these barriers. By utilizing vast genomic databases and advanced computational tools, researchers can optimize microbial systems, improve hydrogen yields, and design more efficient biohydrogen production processes. These in silico methods provide a pathway to enhance the efficiency of biomass processing and enable the development of scalable and sustainable hydrogen production technologies. The review highlights recent advancements and discusses the potential of in silico approaches to address key technological and economic limitations, paving the way for the future of biohydrogen energy.

We investigate the photochemical transformations caused by the continuous CO2 laser radiation (105−106 W/cm2) as well as by the pulsed CO2 laser irradiation with a pulse energy of 3–4 J, a pulse duration of 200 ns, and an effective diameter of the laser spot of 1 mm on the surface of zircon (ZrSiO4). We study the X-ray emission spectra and photoluminescence of the sample surface after the CO2 laser irradiation. We establish that the action of laser radiation in the infrared range on crystalline zircon leads to selective sublimation of Silicon (SimOn) complexes and find increase in the content of Zr atoms and a decrease of Si atoms on the surface of the irradiated samples. The analysis of X-ray emission spectra of samples after CO2 laser irradiation shows also that there is a redistribution of the intensities of the K and L components in the spectra of irradiated zircon. The processes of increase in the Zr content and decrease of the Si content at the irradiated samples and the redistribution of electrons over the K and L shells of the Zr atoms can be caused by the resonant breaking of the Si–O bonds induced by the CO2 laser action; this leads to the creation of long-lived Zirconium–Oxygen defect nanoclusters [Zr[·]+Zr]n+ and [Zr[·]+Si]n+ on the surface of zircon induced by laser irradiation, where symbol [·]+ means an Oxygen vacancy and n is a charge of nanoclusters. Arising of such nanoclusters is confirmed by the photoluminescence spectra of laser irradiated samples. These spectra demonstrate the intense line of complicated form at a region of 400 nm. The feature of Zirconium nanoclusters shows that, in the clusters, the Zirconium atoms stay in different electronic states. © Springer Science+Business Media, LLC, part of Springer Nature 2023.

The study of the composition and distribution of hydrocarbons in oil and dispersed organic matter represents a key tool for determining their genetic features. This paper presents the results of complex geochemical studies carried out on oil samples from the eastern part of the Caspian depression. The methods of adsorption and gas chromatography with mass spectrometry were used to study hydrocarbons of different oil fractions, which allowed to determine their biomarker and isotopic composition. The study also included analysis of the calculated reflectivity of vitrinite to assess the degree of catagenetic transformation of organic matter. The results obtained indicate the marine origin of the oil and distinguish its facies-genetic type, showing a predominantly marine and mixed genesis. Cross-correlation of isotope and biomarker data confirmed the genetic relationship between the oil and its potential sources. The calculated reflectivity of vitrinite allowed us to determine the degree of catagenesis of the petroleum parent rocks, indicating their early stage of catagenetic transformation. These results reflect the significance of geochemical methods of investigation for revealing the genesis of oil, its sources and the degree of catagenetic transformation, which is important for predicting the oil and gas content of sedimentary basins. © 2024, National Academy of Sciences of the Republic of Kazakhstan. All rights reserved.

Nitrogen emissions in the form of NOx with flue gases from thermal power plants are the most serious pollutants gener-ated during the combustion of coal. The calcium carbonate gas scrubbing process used today is expensive, generates a lot of waste, and leaves a significant amount of SO2 in the gas. Virtually no NO x removal. In this paper, we consider the behavior of nitrogen oxides during the purification of exhaust gases from thermal power plants with a carbonate melt of alkali metals. Based on the thermodynamic analysis of reactions between NO x and alkali metal carbonates, the possibility of reducing their concentrations in the exhaust gases is shown. It has been established that the process of NOx absorption in the temperature range of 573…823K is accompanied by the formation of stable potassium nitrite (KNO2) in the melt, as evidenced by the high negative values of the Gibbs energies of the reactions. The results of balance experiments fully confirm the established regularities. One can foresee that carbonate melt-based SO2 removal may become a practical and economical scrubbing method for sulfur-poor flue gases emitted by non-ferrous metal production plants, thereby contributing to the limiting of harmful sulfur and NOx emissions into the atmo-sphere. © 2022, Ore and Metals Publishing house. All rights reserved.

The article deals with the research of component composition and catalitic reactivity of metallurgical waste prod-ucts. The slags component composition was investigated by X-ray fluorescence analysis. The slag stuff has been modified with alkali (NaOH) and mineral acids (HNO3, H2 SO4, НCI and H3 PO4), and their catalytic reactivity in the catalytic decomposition of ethyl alcohol and hydrogen peroxide has been determined for the first time. The re-vealed catalytic reactivity of the slag staff for the decomposition of ethyl alcohol and hydrogen peroxide indicates the need for a more detailed research and development of an industrial non-ferrous waste treatment technology. © 2023, Faculty of Metallurgy. All rights reserved.

We investigated the potential of tailings generated from chrysotile asbestos fiber production as a source of iron, nonferrous metals, and gold. We proposed the use of granulometric separation and systematically examined different enrichment processes, namely magnetic separation, gravity concentration, and enrichment using a Knelson concentrator, to extract the valuable components. The characterization of the initial tailing samples revealed that it comprises primarily of serpentine, brucite, antigorite, hematite, vustite, sillimanite, and magnesium oxide. Using the suggested enrichment process, we isolated gold, chromite, and nickel-cobalt concentrates as valuable products in addition to magnetite. The new approach exhibited high separation efficiency for iron, nonferrous metals, and gold, allowing their satisfactory extraction. © 2022 by the authors.

In the context of sustainability, the concept of balanced development is crucial at both global and regional levels. This principle is equally significant for specific regions, natural-economic complexes, and local communities. Sustainable regional development necessitates a holistic approach to addressing economic, social, and environmental challenges, which are particularly pertinent at the regional scale. The sustainable development of nations is intrinsically linked to their integration into global processes; however, its resilience and stability are contingent upon balanced regional progress. The West Kazakhstan region exemplifies an economic powerhouse within the country and plays a pivotal role in national regional policy. This study introduces a conceptual model designed to evaluate sustainable development through the balanced interaction of various indicators. The results reveal a disparity between the financial and economic potential of different regions and their environmental challenges. These findings form the foundation for developing a new paradigm of sustainable development that emphasizes the integration of economic growth, social stability, and environmental security. The proposed model has the potential to be adapted in various regions of the world facing similar climatic, water, and social challenges. However, it is necessary to consider local characteristics, data availability, and institutional contexts.