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.
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.
This paper considers a stochastic modification of the Frank-Kamenetskiy problem of exothermic reaction dynamics in a plane-parallel layer with random temperature fluctuations at the outer boundary as a means of modeling the behavior of chemical reactors when operating under uncontrolled environment impacts. Unlike deterministic formulations, such approaches take into account the possibility of a thermal explosion whose probability depends on the noise intensity. Based on random process theory, the conditions for achieving ignition in the quasi-stationary approximation (i.e., when the thermal relaxation rate is much higher than the rate of temperature change) are estimated. The possibility of using such a formulation to obtain an approximate relationship between the parameters of the noise and the dynamic characteristics of ignition (expected thermal explosion time) is demonstrated. The equation of non-stationary heat transfer in the reacting medium is solved numerically for a large number of random temperature trajectories at the boundary of the region of interest using a scheme combining explicit approximation of the nonlinear source with implicit approximation of the temperature field. By comparing the two approaches, the main regularities of non-stationary development of a thermal explosion in a stochastic environment can be approximated with good accuracy. Such a comparison relies on dependencies obtained when solving the quasi-stationary problem, taking into account a small correction for the critical temperature (marking the stability boundary for the stationary problem). Distributions of ignition characteristics (ignition temperature, maximum ambient temperature, and ignition time) and their dependence on input parameters (reactivity and noise intensity) are discussed.
The use of hybrid renewable energy systems (HRES) based on diesel power plants supplemented with generating plants based on renewable energy sources is an effective way to improve the efficiency and reliability of electricity supply to decentralized consumers. Designing of HRES associated with the need to solve an optimization problem, when it is necessary to determine the optimal equipment configuration and their installed capacities in the context of multi-criteria. In the study, for multi-criteria optimization of HRES sizing a two-level approach is used. The top-level involves the formation of a set of Pareto-optimal alternatives – configurations of the HRES. At the bottom-level, an hourly simulation of the HRES operation is performed to assess each configuration according to the criteria. The most preferred configuration of the obtained set of Pareto-optimal alternatives is determined by the multi-criteria choice method. The methodological foundation of the two-level approach is ambiguous due to the variety of algorithms and methods. Previous research has demonstrated the efficiency of the NSGA-II algorithm for the top-level. The present study compares simulation modeling and linear optimization for simulating the operation of HRES at the bottom-level. A comparison of approaches to estimate criterion weights for the TOPSIS multi-criteria method, used to select the best alternative from the Pareto-optimal set, is also conducted. This study substantiates the expediency of using simulation modeling at the lower level of a two-level approach, based on a comparative analysis with the linear programming method. The Entropy method, the CRITIC method, and direct weight assignment by a decision-maker have been analyzed. The analysis of the multi-criteria evaluation results has been performed using both objective and subjective methods to assign criterion weights. A study of the solutions obtained on the basis of the proposed approach has been carried out using the example of the “Novikovo” of the Sakhalin region. The sensitivity analysis of multi-criteria estimates using the TOPSIS method has confirmed the sustainability of the solution to small changes in the preferences of a decision maker. For multi-criteria optimization and decision making of the HRES sizing, the choice of methods for both generating the Pareto set using a two-level approach and for the final alternative selection from this set is of great importance. A comparative analysis of methods for the bottom-level showed that for a HRES with batteries, photovoltaic panels, wind turbines, and diesel generators, the use of simulation modeling is preferable to linear programming. Objective methods, such as CRITIC and entropy method, can be used for weighting; however, they cannot fully replace subjective weighting methods that reflect the preferences of the decision-maker.
The use of hybrid renewable energy systems (HRES) based on diesel power plants with integration of renewable energy sources (RES) improves the efficiency of electricity supply to decentralized consumers in conditions of expensive fuel delivery. The study aims to develop methods for accounting for the reliability of backup diesel generator sets (DGS) during multi-criteria optimization of HRES equipment composition, since their failure during periods of low RES generation and discharged batteries becomes the primary cause of electricity undersupply. A two-level approach is used for multi-criteria optimization of HRES equipment composition. At the upper level, a set of Pareto-optimal HRES configurations is formed; at the lower level, hourly simulation of HRES operation is performed for a detailed assessment of each option according to the criteria of economic and environmental efficiency, and reliability of power supply. Two approaches to accounting for the reliability criterion are proposed: a simulation-dynamic approach, which models DGS failures as events in time using the Weibull distribution, with consequences of these failures assessed using short-term simulation and the Monte Carlo method, and a combinatorial-probabilistic approach based on the binomial distribution law for analyzing all possible static states of a group of DGS. The impact of the decision-maker's preference structure on the equipment composition is demonstrated through a case study of selecting an HRES for the village of Kovran in Kamchatka Krai. The simulation-dynamic approach provides a more accurate assessment of the risk of long power supply interruptions by accounting for the temporal correlation between DGS failures and restorations. The developed approaches make it possible to substantiate the appropriate composition and capacity of HRES equipment, including the number and unit capacity of DGS, taking into account the stochastic nature of electricity consumption and RES generation, and equipment failure.
