Particle size distribution is a critical determinant in the thermochemical conversion of lignocellulosic biomass. This research evaluates the impact of comminution on the calorific value and thermal kinetics of wheat straw, corn stalks, and hardwood chips (0.25–2.0 mm fractions). Experimental analysis demonstrates an inverse correlation between particle size and Higher Heating Value (HHV), with fine hardwood fractions achieving up to 20.03 MJ/kg—a relative increase of ~5.6%. Thermogravimetric analysis (TGA) identified a significant enhancement in thermal reactivity, evidenced by a 25–30°C shift in exothermic peaks toward lower temperatures. Additionally, the improved bulk density of finer fractions contributes to higher volumetric energy density, crucial for efficient storage and logistics. The study concludes that incorporating granulometric data into predictive models is essential for accuracy. Practically, these optimized properties facilitate more stable ignition and lower start-up loads in industrial grate and fluidized-bed boilers.

This study investigates the feasibility and performance of integrating shallow geothermal energy systems into the structural foundations of high-rise buildings with developed underground parts. A comprehensive experimental program was conducted, including field measurements of soil thermal properties and laboratory testing of heat exchange elements. Thermal response tests using a 100-meter geothermal probe demonstrated effective ground heat extraction, with the maximum temperature drop in the active zone reaching 6.8°C and an 83% temperature recovery within 3 months. Energy piles with diameters of 0.8 and 1.2 meters exhibited heat outputs of 4.6 and 7.2 kW, respectively, confirming that larger surface areas enhance thermal capacity. The ground source heat pump system operated with an average coefficient of performance of 4.21 during heating and 3.82 during cooling, achieving up to 98% of the projected thermal load. Numerical simulations confirmed the experimental findings, indicating an annual heating energy yield of approximately 2450 MWh. The results validate the integration of geothermal systems into foundation structures as an efficient and reliable approach to reducing energy consumption and enhancing sustainability in high-density urban development.

This article considers matrix multiplication in the problem of finding the transitive closure of a binary relation with the transitivity property, as well as in the construction of the reachability and counter-reachability matrices in general graphs. An analysis of approaches to practical implementation for finding the transitive closure of a binary relation is presented: the Floyd-Warshall algorithm and raising the adjacency matrix to a power until it stabilises. The problem of processing large (thousands to millions of elements) graph diagrams of parallel algorithms on a processor (CPU), and the primary methods for optimising matrix calculations at both the software (algorithmic) and hardware levels, are considered. The main types of digital devices based on the parallel-pipeline data-processing principle are identified, and their advantages and disadvantages are outlined. A specialised computing device for fast multiplication of square binary matrices of size n × n is considered, whose distinctive feature is pipelining the data read operation from a specialised multiport memory. A mathematical model and a method for organising the parallel-pipeline memory of a specialised square binary matrix multiplication device are presented. An estimate of the matrix-processing time and hardware complexity for the developed and prototype devices is presented. Computational experiments showed that, despite a slightly higher hardware complexity (up to 8.8×) than the prototype device, the proposed device multiplies square binary matrices of size n ≤ 512 up to 52.4× faster. This represents a significant advantage when implemented in a semi-custom design using field-programmable gate arrays or a custom design based on application-specific integrated circuits. In this paper, we present a novel systolic device whose core innovation is a pipelined multiport memory architecture. By ensuring a continuous, high-bandwidth data flow to the processing elements, our contribution enables the systolic array to operate at its theoretical peak performance.

In the context of fossil fuel depletion and growing environmental concerns, the study of alternative energy sources, especially biofuels, is particularly significant. Given the global challenges, this work examines the effects of a blended biofuel produced by adding linseed oil to diesel on the operational and environmental performance of a diesel engine. During the experiments, the physical and chemical properties of the initial components and their mixtures were determined, followed by engine testing on the engine stand in the external speed characteristic mode and in a 13-stage test cycle. Evaluation of the data obtained showed that the addition of linseed oil reduced exhaust gas smoke opacity and nitrogen oxide emissions, while causing a slight increase in fuel consumption and in carbon monoxide and unburned hydrocarbon emissions. To balance these effects, optimisation of the blended biofuel composition using the convolution method determined the optimal component ratio, achieving the best balance between fuel efficiency and reduced harmful emissions. The results of the study demonstrate that the use of linseed oil as a component of blended biofuel for diesel engines has the potential to mitigate the environmental impacts of transport.

Amid the energy crisis and environmental concerns, this study evaluates the characteristics of soya oil-based biofuels. Soybeans are considered a promising renewable resource for biodiesel production. The work aims to characterize the spectral properties, fatty acid composition, and physicochemical parameters of biofuels obtained by transesterification of soybean oil. The study analyzed samples of the original soybean oil and soybean oil methyl ester (SOME) using spectroscopy, gas chromatography, and physicochemical methods. The analysis confirmed the success of the transesterification reaction and revealed changes in molecular structure. Key properties of the resulting biodiesel were a density of 880 kg m⁻³ and a kinematic viscosity of 4.5 mm² s⁻¹ (both at 20 °C), an acid number of 0.2 mg KOH g⁻¹, and a higher heating value of 39.0 MJ kg⁻¹. These values align with EN 14214 requirements for biodiesel. Gas chromatography showed that the main components were linoleic, oleic, palmitic, linolenic, and stearic acids. Measurements of physicochemical parameters revealed that the density and viscosity of SOME met the biodiesel standards. The calculated calorific value of SOME is slightly lower than that of diesel fuel. The results confirm the possibility of using SOME in diesel engines, but further research is needed to optimize the production process.

This study investigates the effectiveness of membrane technologies for the integrated separation and purification of synthesis gas derived from coal gasification. Experiments were conducted using a three-stage hollow-fibre membrane system operating at temperatures between 45 and 55 °C and feed gas flow rates between 120 and 220 Nm³/h. The membrane process demonstrated high CO₂ removal efficiency, reducing the retentate CO₂ concentration from 6.1% to 0.9% and increasing the hydrogen content to 53.7%. The CO₂/H₂ separation factor ranged from 31 to 47, indicating strong selectivity. Energy consumption for single-pass processing was 58 kWh per 1000 Nm³ of synthesis gas, substantially lower than that of conventional absorption methods. The extended 48-hour operation confirmed the stability of membrane permeability and selectivity with minimal performance degradation. The final gas composition provided an optimal H₂/CO ratio suitable for Fischer–Tropsch synthesis. These results highlight the technological and economic potential of membrane separation as an efficient alternative for large-scale coal gasification applications. Beyond reporting performance, this work presents a three-stage hollow-fibre scheme with retentate recompression that maintains stable selectivity over 48 h and achieves energy use of 58–84 kWh per 1000 Nm³. In contrast to Scholes et al. (2015), who reported single-pass CO₂ removal efficiencies and CO₂/H₂ selectivities of 25–30 under different membrane materials and feed compositions, our cascade increases hydrogen in the retentate to 53.7% with CO₂ as low as 0.9%, while keeping CO₂/H₂ selectivity within 31–47 across practical flow rates. Compared with the operating windows surveyed by Brunetti et al. (2010), the 45–55 °C, ~18 bar regime used here couples higher CO₂ permeability with stable H₂ retention and documents sulfur reduction to <20 ppm, directly relevant for Fischer–Tropsch feed conditioning.

This study presents a comprehensive experimental and analytical investigation of energy flows in autonomous power supply systems that integrate renewable energy sources and advanced battery storage technologies. The research evaluated the performance and degradation behaviour of lead-acid AGM and lithium iron phosphate (LFP) batteries under varying discharge currents, discharge depths, and temperature conditions. The experiments showed that Lifepo₄ batteries exhibited superior stability, retaining up to 84% of their nominal capacity at 0 °C and maintaining higher efficiency than AGM batteries, which dropped to 65% under the same conditions. Dynamic load simulations revealed significant increases in internal resistance and temperature, particularly in AGM batteries during high-current discharge cycles. The developed mathematical model captured the combined effects of current, temperature, and discharge depth, predicting capacity degradation with an accuracy of ±3.5%. The integration of renewable generation and battery storage enabled daily energy generation of 8.2- 13.8 kWh, with an average conversion efficiency of 85%. These results highlight the advantages of Lifepo₄ batteries for autonomous systems that require reliable performance across diverse operating conditions. We propose and experimentally validate a lightweight correction term that jointly accounts for discharge current, temperature, and depth of discharge within a single degradation model, yielding a prediction error of ±3.5 % across chemistries and operating regimes. We report a rigorously controlled dataset from 12 batteries (6 AGM, 6 LiFePO₄) tested under dynamic duty cycles and sub-zero conditions using high-precision instrumentation. We present an integrated PV–wind–battery testbed with a measured daily energy balance of 8.2–13.8 kWh and a conversion efficiency of up to 91%, providing actionable guidance for sizing and dispatch in autonomous off-grid systems.

This study presents the results of an experimental investigation into integrating moisture recovery and secondary heat exchange systems into the steam reforming cycle of methanol for marine gas turbine power plants. A pilot installation based on a 3.9 MW Siemens SGT-100 unit was used to simulate realistic maritime operating conditions. The experiments demonstrated that optimizing the water-to-methanol molar ratio, along with effective condensation and heat exchange strategies, significantly improved fuel efficiency and reduced greenhouse gas emissions. At a molar ratio of 4.0 and reforming temperature of 660 K, the hydrogen content in the syngas reached 64.1%, with water recovery at 82% and thermal recovery up to 780 kW. These enhancements increased overall thermal efficiency to 44.4% and reduced specific fuel consumption by 15%. Emission measurements showed a 37.3% decrease in CO₂ compared to direct methanol combustion. The system also maintained combustion stability and temperature control under transient conditions, confirming the viability of the proposed approach for maritime applications. Unlike prior marine reforming studies that addressed moisture management and heat recovery in isolation, this work experimentally demonstrates, using a 3.9-MW-class gas turbine rig, a combined moisture-recovery and secondary heat-integration loop that delivers up to 82% water recovery and 780 kW of thermal recirculation with stable transients.

This study investigates the macroscopic kinetics of hydrogen and ammonia oxidation under high-pressure conditions to compare their ignition characteristics, activation energies, and sensitivity to mixture composition. Experiments were conducted in constant-volume and flow reactors over a pressure range of 3–10. MPa and a temperature range of 550–850 K. Hydrogen exhibited significantly shorter ignition delays, reaching as low as 0.14 seconds at 800 K and 10 MPa, compared with 0.35 seconds for ammonia under the same conditions. The activation energy for hydrogen oxidation averaged 171,000 J/mol, whereas that for ammonia was approximately 209,000 J/mol, indicating a higher ignition threshold. The peak pressure during ignition for hydrogen mixtures exceeded 11.5 MPa, whereas that for ammonia mixtures peaked at 8.9 MPa. Hydrogen also exhibited higher concentrations of reactive radicals (H and OH), which explains its more intense chain reaction. Empirical global reaction equations were developed for both fuels, with deviations of up to 10% relative to experimental values. These findings provide a reliable basis for the kinetic modeling of combustion systems operating at high pressures with hydrogen, ammonia, or their mixtures.

In recent years, diabetes mellitus has been increasing rapidly, and due to that, around 380 million people around the globe have been affected. This disease may cause many people to become blind and other health issues. Diabetic Macular Edema (DME) and Diabetic Retinopathy (DR) are medical conditions in humans caused by prolonged high blood sugar levels and have a direct impact on human eyesight, which can subsequently lead to blindness. In the early stages, DR usually progresses without any remarkable symptoms, making early detection difficult. If left untreated for a prolonged period, it can result in permanent vision loss. To facilitate proper diagnosis and timely treatment, computer-based systems today often rely on clinical images. In fact, a vital indicator of DR is the presence of microaneurysms (MA), which are critical for identifying the onset of the disease. In line with the emergence of the Internet of Things (IoT), a wide range of electronic devices can be usefully interconnected and are very capable of collecting, transmitting, and responding to data in real time. In the field of human healthcare, such IoT-powered systems possess sufficient capabilities to support remote diagnosis, particularly through the use of medical sensors in telemedicine scenarios. Nonetheless, such a shift can lead to critical privacy issues for a patient. The protection of critical health-related information becomes particularly critical. Hence, the major challenge here is implementing remote systems to support remote diagnosis while ensuring strict confidentiality to protect the patient's privacy. In the present research work, an IoT-based deep learning approach achieving 98.86% accuracy for Diabetic Macular Edema (DME) and 86.04% for Diabetic Retinopathy is proposed.