Gasification Process Research Articles

Modeling the emitted carbon dioxide and monoxide gases in the gasification process using optimized hybrid machine learning models

Abstract The machine learning methods are hereby proposed to predict the amount of Carbon Monoxide (CO) and Carbon Dioxide (CO₂) emissions in a gasification process, which is one of the most important enabling technologies for carbon-containing materials, such as coal, biomass, and waste toward producing end products of worth, such as syngas, hydrogen, and synthetic fuels. In an attempt to support efforts for improving the emission prediction-a key criterion for enhancing efficiency and further, the environmental performance of gasification-two new advanced algorithms are being applied for the optimization of the model of a random forest: the Jellyfish Search Optimizer (JSO) and Sooty Tern Optimization Algorithm (STOA). The tuned RFJS (RF+JSO) was the best of these configurations, providing the least RMSE of 0.593 on test data and the highest R 2 on validation of 0.983, proving to be most effective for the prediction of emissions. This goes to attest that the model RFJS would be a strong tool in real-time-based carbon emissions reduction due to its effectiveness in dealing with major implications from environmental monitoring to regulation and further into sustainable energy production.

Prediction of syngas production in the gasification process of biomass employing adaptive neuro-fuzzy inference system along with meta-heuristic algorithms

Abstract Compared to fossil fuels, biomass fuels have minimal sulfur content, lower ash production, and significantly reduced emissions. The global need to reduce dependence on imported energy sources and preserve dwindling fossil fuel reserves underscores the importance of utilizing alternative energy resources. Biomass, with its abundant availability, presents a promising source for syngas production, even though the gasification procedure requires substantial energy due to its endothermic nature. Challenges related to the efficiency of biomass gasification and compliance with environmental standards have hindered economic viability. Much attention has been focused on predictive modeling of biomass gasification procedures to address these issues, necessitating robust frameworks capable of predicting parameters under varying operating conditions. This article introduces two hybrid frameworks, which are combined versions of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Rabbits Optimization (ARO) and Crystal Structure Algorithm (CSA), based on proximate biomass values to predict elemental compositions (N2 and H2). These intelligent hybrid frameworks, trained with 70 % of biomass data, were further validated and tested with the remaining 15 % portions of the database. The frameworks were assessed based on some known performance metrics, namely, root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R 2), RMSE-observations standard deviation ratio (RSR), and Nash-Sutcliffe Efficiency (NSE). Developed single and two hybrid frameworks compared and obtained outcomes revealed that both introduced optimizers efficiently promoted N2 and H2 estimation by ANFIS, especially CSA. R 2 values for ANCS were a maximum of 0.993 in both targets’ predictions. Also, minimum RMSE values of 1.007 and 1.470 related to N2 and H2 prediction emphasized the accuracy of ANCS, which is capable of being used in real-world applications.

Advanced Control Strategies for Cleaner Energy Conversion in Biomass Gasification

The escalating climate crisis necessitates urgent and decisive action to mitigate greenhouse gas emissions. Gasification stands out as a highly adaptable process for energy conversion, capable of handling a wide range of feedstocks, from coal to biomass. The process plays a significant role in improving sustainability by converting these feedstocks into synthesic gas (syngas), which can be used as a cleaner energy source or as a building block for producing various chemicals. The utilization of syngas obtained through gasification not only reduces the reliance on fossil fuels but also helps in reducing greenhouse gases (GHGs), thereby contributing to a more sustainable energy landscape. To maintain optimal operational conditions and ensure the quality and safety of the product, an effective control system is crucial in the gasification process. This paper presents a comparative analysis of three control strategies applied to a numerical model of rice husk gasification: classical control, fuzzy logic control, and dynamic matrix control. The analysis is based on a comprehensive model that includes the equations necessary to capture the dynamic behavior of the gasification process across its various stages. The goal is to identify the most effective control strategy, and the performance of each control strategy is evaluated based on the integral of the absolute value of the error (IAE). The results indicatethat fuzzy logic control consistently outperforms classical control techniques, demonstrating superior disturbance rejection, enhanced stability, and overall improved control accuracy. These findings highlight the importance of selecting an appropriate advanced control strategy to optimize sustainable gasification processes.

Neural operators for hydrodynamic modeling of underground gas storage facilities

Objectives. Much of the research in deep learning has focused on studying mappings between finite-dimensional spaces. While hydrodynamic processes of gas filtration in underground storage facilities can be described by partial differential equations (PDE), the requirement to study the mappings between functional spaces of infinite dimension distinguishes this problem from those solved using traditional mapping approaches. One of the most promising approaches involves the construction of neural operators, i.e., a generalization of neural networks to approximate mappings between functional spaces. The purpose of the work is to develop a neural operator to speed up calculations involved in hydrodynamic modeling of underground gas storages (UGS) to an acceptable degree of accuracy.Methods. In this work, a modified Fourier neural operator was built and trained for hydrodynamic modeling of gas filtration processes in underground gas storages.Results. The described method is shown to be capable of successful application to problems of three-dimensional gas filtration in a Cartesian coordinate system at objects with many wells. Despite the use of the fast Fourier transform algorithm in the architecture, the developed model is also effective for modeling objects having a nonuniform grid and complex geometry. As demonstrated not only on the test set, but also on artificially generated scenarios with significant changes made to the structure of the modeled object, the neural operator does not require a large training dataset size to achieve high accuracy of approximation of PDE solutions. A trained neural operator can simulate a given scenario in a fraction of a second, which is at least 106 times faster than a traditional numerical simulator.Conclusions. The constructed and trained neural operator demonstrated efficient hydrodynamic modeling of underground gas storages. The resulting algorithm reproduces adequate solutions even in the case of significant changes in the modeled object that had not occurred during the training process. The model can be recommended for use in planning and decision-making purposes regarding various aspects of UGS operation, such as optimal control of gas wells, pressure control, and management of gas reserves.

Modelling of Biomass Gasification Through Quasi-Equilibrium Process Simulation and Artificial Neural Networks

In the past two decades, advancements in thermochemical technologies have improved biomass gasification for distributed power generation, enhancing efficiency, scalability, and emission control. This study aims to optimize syngas production from biomass gasification by comparing two computational models: a quasi-equilibrium thermodynamic model implemented in Aspen Plus and an artificial neural network (ANN) model. Operating at 850 °C with varying steam-to-biomass (S/B) ratios, both models were validated against experimental data. Results show that hydrogen concentration in syngas increased from 19.96% to 43.28% as the S/B ratio rose from 0.25 to 0.5, while carbon monoxide concentration decreased from 24.6% to 19.1%, consistent with the water–gas shift reaction. The ANN model provided rapid predictions, showing a mean absolute error of 3% for hydrogen and 2% for carbon monoxide compared to experimental data, though it lacks thermodynamic constraints. Conversely, the Aspen Plus model ensures mass and energy balance compliance, achieving a cold gas efficiency of 95% at an S/B ratio of 0.5. A Multivariate Statistical Analysis (MVA) further clarified correlations between input and output variables, validating model reliability. This combined modelling approach reduces experimental costs, enhances gasification process control and offers practical insights for improving syngas yield and composition.

Life cycle assessment and thermodynamic performance of biomass-based combined cogeneration

Abstract This study presents a thermodynamic and life cycle analysis of a biomass-based integrated energy system, comprising a biomass gasification cycle, a steam Rankine cycle, and an ammonia fluid Organic Rankine cycle. The primary objective of the system is to efficiently convert olive husk, a sustainable biomass source, into electricity and heating. The gasification process generates syngas, which fuels a gas turbine, producing high-temperature exhaust that drives a Rankine cycle. The thermodynamic analysis indicates that the overall system achieves an energy efficiency of 20.85% and an exergy efficiency of 11.70%. The Rankine cycle exhibits an energy efficiency of 23.91%, while the Organic Rankine cycle demonstrates a higher exergy efficiency of 68.87%. Parametric studies reveal that increasing the combustion chamber temperature significantly enhances system efficiency and output, with energy efficiency rising linearly as the temperature increases from 800 °C to 1000 °C. Additionally, the analysis emphasizes the importance of the compressor's compression ratio on system performance. A life cycle assessment conducted using the ReCiPe methodology evaluates the environmental impact of the system throughout its lifecycle, revealing critical insights into its sustainability. This comprehensive evaluation highlights the potential of utilizing agricultural waste, such as olive husk, to produce renewable energy, thereby supporting environmental sustainability and waste management goals. The findings underscore the viability of the proposed system for enhancing energy production while minimizing environmental impacts, making it a promising solution for renewable energy generation in regions with abundant biomass resources.

Holistic characterization analysis of tar waste content from gasification process at Surakarta landfill

Surakarta is facing serious problems related to waste management at the Putri Cempo landfill. PT. Solo Citra Metro Plasma Power with the Surakarta city government built a waste power plant installation with gasification technology. Tar waste is a gasification liquid product that is dangerous if not managed. This study aimed to determine the heavy metal content and toxicity level of gasified tar waste using the atomic absorption spectrometry (AAS) and lethal dose-50 (LD<sub>50</sub>) acute toxicity test. The parameters of heavy metals tested are lead (Pb), copper (Cu), iron (Fe), zinc (Zn), and total chromium (Cr). In the LD<sub>50</sub> test, the animals were divided into 5 groups, namely 1 control group and 4 groups with doses of 0.50 mL, 1 mL, 2 mL, and 4 mL. The results showed that the tar contained 17.4 mg/L of iron, 3.5 mg/L of zinc, and <0.01 mg/L of lead, copper, and chromium. Acute toxicity tests did not cause death in the animals but still showed toxic symptoms, so the LD<sub>50</sub> value is declared pseudo. The content of some heavy metals in tar is within safe limits and the level of toxicity is relatively harmless.

MoS2 Coatings in unsynchronized, dry-running Screw Compressors: Experimental Insights on Operational Efficiency and Durability

Abstract The discourse on unsynchronized, oil-free screw compressors versus their conventional, oil-lubricated counterparts encapsulates a pivotal shift towards more effi-cient and environmentally friendly industrial machinery. By forgoing gears and lubricants, these innovative compressors streamline manufacturing and assembly, while also eliminating the risk of lubricant contamination in the process gas. In this configuration, the torque generated on the gate rotor by the gas pressure is directly conveyed to the main rotor through the interfacing surfaces of the rotors. In the development of compressor technology, a significant improvement is the application of a molybdenum disulfide (MoS2) coating on the primary rotor. This application utilizes the properties of molybdenum disulfide, which is effective in reducing friction and wear under various operational conditions. By applying this coating, there is an anticipated increase in the wear resistance of the rotor, effectively reducing frictional losses during its operation. This reduction in friction is expected to improve the mechanical efficiency of the compressor and extend its operational lifespan. The investigation of contact interactions utilizes a custom-designed test rig, through which effective parameters are analyzed to facilitate conclusions regarding losses and the durability of the coating. Additionally, the geometry within the contact area is tactilely measured, enabling the quantitative determination of wear. Various layer thicknesses, surface roughness levels, and loads are examined in this context.

From biogas to biomethane: Evaluating the role of microalgae in sustainable energy production – A review

Methane, predominantly derived from fossil resources, is a highly utilised energy source due to its high energy density. However, the rapid consumption of fossil fuels has precipitated anthropogenic climate change and resource depletion, compelling the necessity for a transition towards sustainable and climate-compatible energy sources. This review examines biogas upgrading technologies, encompassing physical, chemical, and biological methods, with a particular focus on photosynthetic CO2 fixation to enhance methane purity and meet biomethane purity standards. Biological processes, in particular those utilising microalgae, demonstrate considerable potential for CO2 sequestration and have the capacity to reduce emissions while simultaneously generating valuable byproducts. The review investigates key process parameters, including pH, liquid-to-gas (L/G) ratio, temperature, and gas retention time (GRT), along with the removal efficiency of CO2. The mass transfer of CO2 and the role of microorganisms are also examined. Furthermore, biological, and chemical/physical upgrading technologies are economically and ecologically compared.

Experimental and kinetic studies on steam gasification of oxidized carbon-rich fraction from coal gasification fine slag

Gasification is an important method to realize the resource utilization of coal gasification fine slag (CGFS). In this work, the carbon-rich fraction (CF) of CGFS was modified by oxidation, and the gasification reactivities, structures, and kinetics of samples were studied. The results demonstrate that compared to CF, the gasification characteristic temperatures of oxidized CF are lower, the weight loss rates are faster, and the gasification reactivities are significantly enhanced. These improvements are attributed to the structural evolutions of oxidized CF. Compared with CF, the specific surface area and pore volume of oxidized CF increase by at least 2.5 and 1.5 times, the proportions of oxygen-containing groups rise, and the carbon microstructure of different oxidized CF varies significantly. CF oxidized by steam exhibits the most developed porosity and the most disordered carbon microstructure, corresponding to the highest gasification reactivity of all the samples. The kinetics results showcase that the diffusion reaction model is suitable for describing the gasification process of samples, so porosity emerged as the primary factor determining the gasification reactivity. However, the influence of carbon microstructure and active groups on the gasification reactivity of oxidized CF is enhanced when the porosity meets the diffusion requirements of the gasification reaction.

Optimization of Process Design and Operating Parameters of H2S Removal Unit to Reduce Lean Amine Inlet Temperature of Amine Contactor at Upstream Oil and Gas Subsidiary SI

Upstream Oil and Gas Subsidiary SI is a natural gas processing company that operates an H2S removal unit to convert natural gas rich in CO2 and H2S into sweet gas. The main problem of this unit is the high temperature of lean amine entering the Amine Contactor. The purpose of this study is to determine the factors of high lean amine temperatures, evaluate the lean amine cooler, amine regenerator overhead cooler, and plate exchanger performance, and determine the optimal process design configuration and operating parameters. The method used is the simulation of the H2S removal unit with Aspen HYSYS, followed by a comparative analysis between simulation data and equipment design data. The independent variables of this study include Reboiler duty, reflux ratio, and heat transfer area in the Plate Exchanger, with the main dependent variable being lean amine temperature. The results showed that the high lean amine temperature was caused by a decrease in the performance of the Lean Amine cooler and amine regenerator overhead cooler, as seen from the UA and LMTD values of the simulation results which were smaller than the design. In contrast, the Plate Exchanger still functions well with a UA value greater than the design. Optimization was carried out by adjusting the process design and operating parameters of the H2S removal unit. The optimized design involves bypass reflux from the regenerator to the rich amine stream entering the rich/lean amine exchanger and increasing the heat transfer surface area of the plate exchanger to 62.17 m². The influential operating parameters are reboiler duty, liquid flow rate to the mixer, and plate exchanger heat transfer surface area. Optimal operating conditions were achieved at a Reboiler duty of 1,642 kW, a liquid flow rate of 1.4 m³/h, the reflux to Mixer ratio is 100%, and a heat transfer surface area of 62.17 m². In addition, it can be concluded that the optimization of process design and operating parameters successfully reduced the lean amine inlet temperature of the Amine Contactor.

Synthesis of fluorinated ether free aromatic backbone copolymers containing trifluoromethyl and dimethylamino groups for CO2 separation: Investigation of pure and mixed gas performance under dry and humid conditions

A novel series of linear, high molecular weight aromatic copolymers containing trifluoromethyl and dimethylamino groups was synthesized via facile super-acid catalyzed polyhydroxyalkylation and investigated as CO2 gas separation membrane materials. The molar ratio of the two ketone comonomers used in copolymerization, trifluoroacetophenone (TFAp) and N,N-dimethylamino trifluoroacetophenone (dMATFAp), was systematically varied to produce five copolymers with different dMATFAp contents ranging from 12 to 73 %. The influence of dMATFAp content on physicochemical and mechanical properties as well as pure gas and water vapor separation performance was studied. All synthesized copolymers displayed excellent film forming ability, high thermal stability and high Tg values (Tgs > 380 °C). Pure gas permeability measurements revealed that the increase of dMATFAp molar content from 12 % to 34 % resulted in improved gas permeability, while, on the contrary, further increase of dMATFAp content from 34 to 73 % led to substantial gas permeability decrease ascribed to FFV decrease. In particular, among copolymers, the one with dMATFAp content of 34 % presented the highest CO2 permeability value of 290 Barrer due to its highest CO2 diffusion and CO2 solubility coefficients associated with its higher FFV value. The CO2/CH4 performance of synthesized copolymers fell very close to 2008 upper bound limit and exceeded most of the super-acid catalyzed copolymer membranes reported in the literature and commercial membranes. CO2/CH4 and CO2/N2 selectivity values for the different synthesized copolymers were in the range of 29.9–40 and 22.7–44, respectively. The study of the effect of dMΑTFA on water permeability unveiled that the membrane with the highest dMΑTFAp molar content (73 %) showed the highest water permeability value (23,100 Barrer) as well. This was attributed to the increased concentration of N-methyl substituent of dMΑTFAp which can interact with water molecules thereby increasing solubility. Under process gas stream conditions using a mixture of 3 % H2O-48.5 % CO2-48.5 % CH4, both CH4 and CO2 permeability were significantly reduced (by 35 % and 47 %, respectively) due to the preferential sorption of water vapor over other gases present in the mixture, highlighting the negative effect of water vapor on gas permeability. Accordingly, the CO2/CH4 separation factor was slightly increased from 23.8 to 29. Finally, stability test of the copolymer (BP-TFAp)66-(BP-dMATFAp)34 at 40 °C under humid mixed gas conditions showed that CO2 and CH4 permeability remained unchanged for one month after an initial condition stabilization that is required implying that these copolymer structures are promising materials for CO2 separation.

Bioaugmentation strategy for mitigating pollutant gas emissions in food waste composting using fermented mixtures

The fermented mixtures at different composting stages exhibit varying physicochemical and microbiological properties, adding them to compost, which may show different biological enhancement abilities. This study analyzed the efficiency of fermented mixtures in mitigating pollutant gas emissions during composting, through the co-composting of food waste and sawdust, supplemented with 20 % fermented mixtures from different composting stages: thermophilic (TS), cooling (CS), and mature (MS). Results revealed that fermented mixtures affected the composting process and pollutant gas emissions by regulating bacterial communities. The TS treatment increased the abundance of high-temperature resistant organic matter degrading bacteria, thereby effectively promoting temperature and reaching the highest peak temperature (> 70 °C) in just 2 days. Among all the treatments, TS exhibited the most pronounced reduction in N2O emissions, inhibiting the proliferation of bacteria engaged in nitrite respiration, which resulted in reducing overall N2O emissions by 20.7 %. Furthermore, TS outperformed both CK and MS treatments regarding CO2 reduction, achieving declines of 11.2 % and 20.2 %, respectively. The MS treatment diminished the abundance of acidogens and methanogens bacteria, such as Lactobacillus, Weissella, and Leuconostoc, resulting in a 24.2 % reduction in methane emissions compared to CK. In contrast, the CS treatment had minimal effect, reducing methane emissions by 4.2 %. In conclusion, thermophilic compost demonstrated superior effectiveness in reducing pollutant gas emissions compared to other fermented mixtures. Additionally, owing to its economic advantages, thermophilic compost can be produced significantly faster than the others. Therefore, thermophilic compost is recommended as a bioaugmentation technology that synergistically offers environmental benefits and cost-effectiveness.

Fuzzy set-based decision support system for hydrogen sulfide removal technology selection in natural gas processing: a sustainability and efficiency perspective.

Removing hydrogen sulfide (H2S) toxic and corrosive gas from the natural gas processing and utilization industry is a challenging problem for managers of these industries. This problem involves different economic, environmental, and health issues. Various technologies have been employed to remove the H2S gas from these industries, and choosing appropriate H2S removal technologies is a complex multi-criteria decision-making (MCDM) problem. In this regard, the present research work aims at developing a novel decision support system (DSS) by integrating MCDM techniques based upon the (best worst method) BWM and (alternative ranking order method accounting for two-step normalization) AROMAN methods under the decomposed fuzzy set theory. The DSS is used to assess technologies for removing H2S gas and then choose the best scenario for implementing among these technologies. Fourteen criteria in four environmental, social, economic, and efficiency aspects and five technologies of fixed-bed activated carbon technology (A_h2s1), biotrickling filter technology (A_h2s2), scavenger technology (A_h2s3), chemical oxidation scrubber technology (A_h2s4), and liquid redox technology (A_h2s5) have been chosen for evaluation. The final value of each technology is obtained as A_h2s1 (0.5550), A_h2s2 (0.5262), A_h2s3 (0.5809), A_h2s4 (0.6357), and A_h2s5 (0.5790). The results reveal that the chemical oxidation scrubber technology (A_h2s4) and biotrickling filter technology (A_h2s2) with the values of 0.6357 and 0.5262 are respectively the best and worst scenario for the removal of H2S produced by the natural gas processing and utilization plants of Iran. Therefore, the DSS is feasible and applicable and gives reliable and robust findings for acquiring the optimal H2S removal technology.

Analysis and optimization of dynamic response during the startup process of proton exchange membrane fuel cell

Proton Exchange Membrane Fuel Cell (PEMFC) is a prominent application of hydrogen energy in fields such as mobile devices, vehicles and small-scale energy systems. This study experimentally investigates transient response of the automotive PEMFC when starting with different load under various operating conditions, which significantly influences durability. The key finding is that dynamic behavior follows a “two-stage” response mode under drier conditions. The first stage is related to the diffusion processes of cathode gas and the second stage is determined by the membrane rehydration process. Under wetter conditions, dynamic behavior exhibits a “three-stage” response mode with a larger voltage fluctuation and longer startup time. The contributors to the first two stages are the same as the “two-stage” response mode and the third stage is determined by the degree of cathode flooding. Results indicate that increasing cathode stoichiometry, cathode humidity, and temperature obviously enables PEMFC to startup more stable and faster and with a higher load. Besides, a step loading strategy with a gradually reducing magnitude achieves an optimal balance between response time and voltage fluctuation at the target load, with a moderate startup time of 9.6 s and minimal voltage fluctuation of 0.009 V.

End of tubing placement optimization for multi-stage fractured horizontal wells by transient multiphase flow simulation

The exploitation of shale gas offers an effective pathway towards mitigating greenhouse gas emissions and promoting a clean and low-carbon energy system. Nonetheless, liquid loading has been commonly observed in the middle and late production stage, leading to sharp decline of shale gas production. How to improve unloading performance is challenging due to limited liquid-loading capacity. The strategic and timely installation of oil tubing serves as an effective means to mitigate wellbore fluid accumulation in the gas field. Thus, this work tries to improve gas recovery factor by optimizing the End of Tubing (EOT) location and the timing of tubing installation in shale gas wells through transient multiphase flow simulation. The transient simulation approach is adopted owing to its capability to accurately characterize the transient unloading process in gas wells and its closer approximation to field operation conditions compared to steady-state simulation methods. The casing production model and tubing production model have been developed to accurately simulate the flow behavior of fracturing fluid in the casing and tubing production, respectively. The annular flow and the coupling of fluid flow between reservoir and wellbore are the primary focuses of the two models aimed at analyzing fluid flow behavior and the total pressure drop (Δptotal). The casing production model is used to optimize the timing of tubing installation by simulating the liquid loading process of gas well including Δptotal and liquid inventory of wellbore. The optimal time of tubing placement is identified as the point when fluid starts to accumulate in the wellbore, as confirmed through the quantifiable analyses of Δptotal and liquid inventory. The tubing production model is further utilized to optimize the EOT location and tubing diameter considering variations in annular flow and the challenges associated with tubing installation. The placement of tubing in up-dip wells is recommended at Point A, which is determined by considering the influence of fluid gravity as a resistive force. Conversely, for down-dip wells, it is advisable to position the tubing between 1/3 of AB and 1/2 of AB as fluid gravity serves as a driving force in these scenarios. This work offers guidance for the optimum EOT location in shale gas reservoirs experiencing severe liquid loading. Consequently, it is essential to establish guidelines for determining the optimal EOT location across various horizontal well configurations and flow conditions.

INNOVATIVE SOLUTIONS FOR IMPROVING THE EXISTING HEAT EXCHANGE EQUIPMENT OF METALLURGICAL ENTERPRISES

Considered issues regarding the refusal to use natural gas in the technological process of air heating in air heaters ofblast furnaces (coopers) of metallurgical enterprises. An innovative solution is proposed – a technological schemeusing a heat generator for heating flue gases and improving the system of their heat utilization in order to increase thetemperature of air and gas entering the air heater by 150–200°C. The results of the test trials of the technologicalscheme, which were carried out under the following conditions, are presented: calorific value of the mixture of naturaland blast furnace gas – 826.4 kcal/m3 (3,459.3104 KJ); calorific value of blast furnace gas – 750.7 kcal/m3 (3,142.43KJ); air consumption per unit of air heaters – 58 thousand m3/h; temperature of combustion components in front ofheat exchangers: air – 10 °С; fuel – 35 °C; smoke temperature: at the exit from the air heaters – 230 °C; in front of thechimney – 130 °C. The results showed that its application will ensure an increase in the average temperature of blastheating by 60-110°С without the use of natural gas, the duration of operation of heat exchangers can be increased byat least 10 years, and the technical and economic indicators of the blast furnace process will also improve. Theproposed innovative solutions will allow: to abandon the use of natural gas for air heaters; ensure coke savings up to14 kg/t; increase the average heating temperature of blowing by 110°C; stable, long-term provision of the necessaryair heating temperatures. The use of these technical solutions will allow to increase the production of cast iron andreduce the consumption of natural gas to increase the temperature of hot blasting. In the future, similar schemes maybe used in the fuel preparation system for multi-fuel boiler units of metallurgical enterprises

Membrane Materials for Separation of Hydrogen from Process Gas Mixtures

Membrane Materials for Separation of Hydrogen from Process Gas Mixtures

ASSESSMENT OF THE RISKS OF EMERGENCY SITUATIONS IN THE ENERGY SECTOR OF CRITICAL INFRASTRUCTURE

Problem statement. In order to effectively increase the security of critical infrastructure in the energy sector, it is necessary to assess all threats and analyze threat scenarios for the facility itself. Threats to energy security are the possibility of short-term or long-term situations, real or potential factors, phenomena or events that may disrupt the stability and security of the country's energy sector. These threats can lead to restriction or disruption of energy supply to consumers, accidents and other negative consequences. In many cases, the failure of one facility or system will affect the ability of interconnected facilities or systems in the same or another sector to perform their functions. For a relatively stationary system, it is important to identify those components or critical nodes where the potential consequences will be highest and where security and resilience efforts need to be focused. The purpose of the article is to develop and implement an approach to determining the risks of an emergency situation at critical infrastructure facilities using the example of the Dolyna Gas Processing Plant (GPP). Conclusions. An approach to identifying and assessing the risks of the critical infrastructure of the energy sector was developed and implemented, with the help of which potential vulnerabilities of the object to various threats are revealed. A simulation model for cascading effects has been built, which allows obtaining probabilistic estimates of the development of events under defined scenarios for a critical infrastructure object.

Calculation and application research on real-time source term diffusion in NPP auxiliary building under accident circumstances

Abstract In the light of radioactive material leakage (due to containment bypass or failure) to the auxiliary building and its service compartments under severe accident circumstances, key techniques related to accident source terms in the nuclear emergency building were researched. During the research, the source-term diffusion and migration patterns were simulated in the building compartments, including the simulation of the gradual diffusion and decay process (driven by circulating air) of fission-derived gases and aerosol toward each compartment. Besides that, by taking a M310 unit at Fangjiashan NPP as the application object, the scenarios of initial accidents of LB/MB/SB-LOCA, MSLB and SGTR were chosen to establish a real-time diffusion calculation model for in-building radioactive source terms under nuclear accident circumstances. While considering the circumstances before and after being intervened by accident mitigation measures, the concentration distribution of radionuclides in the auxiliary building and personnel total-exposure radiation dose rate were quickly assessed online, and a basis was provided for the visualization of radiation dosage and the assessment of received radiation dosage and injury & death hazards during personnel activities outside the main control room.