Accurate national-scale short-term load forecasting is essential for reliable dispatch, reserve allocation, and cost-efficient operation under weather-driven demand variability. Most existing models either rely mainly on load and calendar inputs or fuse high-dimensional weather grids through simple concatenation, which underuses spatial structure and can increase error at the national scale. This work addresses this gap with the Hierarchical Spatio-Temporal Refinement Network (HST-RefNet), which learns directly from nationwide meteorological fields and historical load without decomposition. The model uses a Spatio-Temporal Representation Temporal Convolutional Network (STR-TCN) to learn dominant spatio-temporal patterns, a Residual-Focused Refinement Temporal Convolutional Network (RFR-TCN) to correct hard regimes, and a lightweight random-feature calibrator with closed-form training to refine remaining nonlinear errors. Iterative training is concentrated in the two TCN stages, while the final calibrator adds low computational overhead through closed-form estimation. The comparative study includes both generic sequence baselines and dedicated spatio-temporal baselines for structured high-dimensional meteorological inputs. On the Pakistan National Transmission and Despatch Company (NTDC) load and National Aeronautics and Space Administration (NASA) Prediction Of Worldwide Energy Resources (POWER) benchmark, HST-RefNet achieves an average Normalized Root Mean Squared Error (NRMSE) of 5.00% and a Coefficient of Determination (R2) of 0.91 over the first 6 forecast horizons, while reducing NRMSE by 7.09% relative to the strongest spatio-temporal baseline.
The production of sludge is substantial, and its water content is extremely high. The gas and bio-oil produced through sludge pyrolysis can be reused as energy sources or industrial raw materials. Lime (main component: calcium oxide, CaO), a cost-effective and widely used sludge dewatering conditioner, generates calcium hydroxide (Ca(OH)2) during the sludge drying process with lime. This compound elevates sludge pH and significantly influences product distribution during sludge pyrolysis. This study investigated the effects of Ca(OH)2 addition ratios (Ca/SS = 0, 0.25, 0.5, 1, 1.5, and 2) on three-phase product release characteristics during lime-dried sludge pyrolysis. Experiments were conducted in a fixed-bed reactor at 500 degrees C to examine these effects. The results revealed that Ca(OH)2 substantially modifies pyrolysis behavior through two principal mechanisms: enhanced CO2 adsorption leading to promoted H2 generation, and catalytic effects on key chemical transformations. These include decarboxylation and decarbonylation of oxygenated bio-oil compounds, and nitrogen-containing ring-opening reactions coupled with molecular rearrangements. These synergistic processes collectively increase aromatic hydrocarbon yield while producing small-molecule gases, ultimately improving both hydrogen production efficiency and bio-oil quality during sludge pyrolysis.
In this paper, we developed and tested an algorithm for analyzing a stage-by-stage hypergraph of chemical reactions using the example of methane combustion in pure oxygen. The hypergraph visualizes the chemical reaction mechanism. The algorithm’s input data is a detailed mechanism listing components and reactions. At each stage of the hypergraph, the reaction rates and equilibrium amounts of substances present in the system at that stage are calculated. This approach allows us to discard components that cannot form as a result of a chain of interconversions. The calculation results show the dependence of component quantities on the hypergraph stage. Temperature dependences of significant substances were also obtained. The calculations showed that as temperature increases, the number of significant components decreases, and the final result is influenced by the choice of reaction sequence
The pyrolysis (low-temperature carbonization) of a coal bed was investigated to determine conditions for producing briquettes suitable for use as solid fuel or as feedstock for carbon materials. The study used Cheremkhovo coal (Irkutsk coal basin, Russia) with a moisture content of 10 %, volatile matter yield of 38.7 %, and ash content of 30.2 %. Beds of weakly caking coal were pyrolyzed in a laboratory muffle furnace at 300–400°C and subsequently pressed at approximately 20 MPa. Numerical modeling was used to describe the kinetics of thermal decomposition and heat transfer. Briquettes produced at 300–350°C had a strength of approximately 95%, whereas those produced above 350°C disintegrated during pressing. Briquette strength was found to depend primarily on the thermal state of the coal bed, specifically on the uniformity of heating and the degree of thermal decomposition throughout the bed height. A simplified mathematical model was developed to evaluate the uniformity of heating and decomposition. In a one-dimensional approximation, the model describes heat transfer and a two-stage endothermic overall reaction comprising coal softening followed by conversion to semicoke. An oscillation index was introduced as a numerical criterion that accounts for nonuniformity in the conversion profile throughout the bed height; high index values indicate low expected briquette strength. Qualitative comparison of the experimental data with estimates based on different criteria, including the uniformity of the coal-bed state and energy consumption, showed that the mathematical model adequately describes the thermal pretreatment of coal. The calculations indicate that the highest strength of Cheremkhovo coal briquettes is achieved at temperatures of 300–320°C with a heating time of approximately 1 h. The corresponding energy consumption for briquette production is 2–3 MJ/kg of coal.
The research aims to develop method for restoring the functioning of an intelligent electric power system (EPS) in the event of failures. The research relies on the graphoanalytic methods, theory of semi–Markov processes, numerical methods. Research result: the mutual influence and interrelations of the information system and the technological part of the intelligent EPS in the event of failures under cyber attacks are analyzed. The features of the process of restoring the intelligent EPS in the event of emergency situations as a result of cyber attacks are revealed. A model of the intelligent EPS and an algorithm for determining the EPS restoration indicator in the event of cyber attacks are proposed. A method for restoring the intelligent EPS has been developed that prevents the development of emergency situations by detecting, localizing cyber attacks and isolating the operation of the technological part of the EPS into "islands".. The scientific novelty lies in the fact that a method for restoring an intelligent power system has been proposed that takes into account the mutual influence of information and physical systems, and the peculiarities of the propagation of failures during cyber-attacks.
