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This article introduces and validates an advanced approach to adaptive automatic control of pyrolysis reactors, aimed at optimizing the utilization of agricultural plastics. By integrating fuzzy logic with enhanced metaheuristic optimization technique, the proposed method achieves superior performance in regulating reactor temperature modes. A key feature of this approach is the incorporation of the reactor's current loading level as an additional variable in the antecedents of the adaptive fuzzy controller's rule base. This dynamic adaptation is further enhanced through the application of the hybrid improved gray wolf optimization algorithm, which optimizes the rule base parameters to increase the robustness and accuracy of control. To assess the effectiveness of this methodology, a fuzzy control system was designed for a specific pyrolysis reactor. Simulation results confirm the approach's superiority over traditional controllers in response speed, accuracy and robustness in various operating conditions. In particular, at a reactor loading level of 100% at startup of the installation and subsequent transition to steady-state operation, the application of the proposed approach made it possible to reduce the transient process duration by 2.26 times and decrease in overshoot by 48.4% compared to existing systems. This, in turn, will lead to a reduction in molecular weight fluctuations and an improvement in the quality of the resulting fuel fractions, thereby significantly enhancing the general energy efficiency and economic performance of agricultural enterprises utilizing pyrolysis technologies.

The increasing activity of unmanned aerial vehicles (UAVs) has intensified the demand for reliable and computationally efficient methods for passive radio-frequency (RF) signal detection. In practical RF monitoring scenarios, the environment is often non-stationary and affected by varying noise conditions. Under such circumstances, classical energy-based detectors are sensitive to noise uncertainty, while more robust approaches, such as cyclostationary analysis, require substantially higher computational resources. This work presents a burst-aware cascade method for UAV RF signal presence detection that explicitly addresses this trade-off. The proposed framework combines fast energy-based screening with temporal burst aggregation, applying spectral correlation function (SCF) analysis selectively and only when sustained signal activity is indicated. Detection is performed on fixed-length RF signal chunks, while additional segment-level duration constraints are used to characterize sustained transmissions. The method is evaluated using the publicly available DroneRF dataset and compared against six baseline detectors, including fixed-threshold energy, wavelet-based, blind cyclostationary, two adaptive energy detector variants, and a lightweight convolutional neural network. Experimental results confirm that chunk-level detection remains difficult for all considered methods. Temporal aggregation across longer intervals yields a substantial improvement: the cascade achieves $P_d$ = 1.000 and AUC = 1.000 at the segment level, matching exhaustive cyclostationary detection while reducing per-segment processing time by a factor of 2.46. An additional result is that burst-level concatenation prior to SCF estimation provides implicit coherent integration, preserving $P_d$ = 1.000 at signal amplitude reductions of up to −20 dB where standalone detectors degrade to $P_d$ = 0.995. Overall, burst-aware cascade architectures offer a practical and interpretable approach to RF-based UAV monitoring, providing a well-grounded compromise between detection reliability and computational efficiency under realistic operating conditions.

Thermal destruction systems are essential for processing hydrocarbon waste, requiring precise temperature control to ensure operational efficiency and high product quality. However, accurately modeling the thermal behavior of such systems remains a challenging task due to the need for precise identification of transient heating characteristics. This study addresses this problem by developing an advanced two-point identification approach for constructing transfer function models of temperature control objects. Unlike conventional methods that rely on fixed reference points for each model order, the proposed approach leverages predetermined empirical coefficients, enabling flexible and more accurate identification from any two points on the transient characteristic, even in the presence of noise. The effectiveness of the developed method is validated through the identification of two distinct experimental transient characteristics of a 100-liter reactor within a pilot thermal destruction system under noise conditions. Comparative analysis against three known identification techniques demonstrates that the proposed approach consistently yields the most accurate models while maintaining relatively low computational complexity, due to its versatility and ability to operate in noisy conditions. In particular, the values of the integral squared deviation were reduced by 0.126·105 for the first case and by 0.092·105 for the second case when compared with the best results achieved using the conventional two-point method. This confirms its high efficiency and the feasibility of using it to create comprehensive mathematical models of various systems for thermal destruction of hydrocarbon waste. The proposed approach is particularly well suited for rapid identification of transfer functions under varying operating conditions and can be applied to enhance control strategies, ultimately improving process stability, energy efficiency, and product quality in thermal destruction applications

The article develops a methodology for modeling the state of the food chain of the aquatic ecosystem in terms of biomass growth of producers based on ecological energy stoichiometry. The research is based on a number of sinusoidal functions developed during the simulation of the monthly solar energy arrival per unit area by latitude. The average forecast indicator of energy input for southern Ukraine of 2518 MJ/m2 is very close to the results of similar applied measurements in Odesa. For modeling, the average latitude for Ukraine is 48°23′, the average inclinations of the sun for each of the 8 months according to the astronomical calendar (about 14), as well as the average duration of a sunny day, and the angle of incidence and reflection of the sun’s rays on water are determined based on them, taking into account only the angle of incidence at noon, and changes during the day (the angle of reflection ranges from 17.5 in June to 29.26 in September). Correction coefficients for latitude were derived, the percentage ratio of days with cyclonic weather, due to which there is an increase in reflection due to cloudiness and waves, was calculated – 638 hours and 30 minutes. The efficiency of photosynthesis for the main groups of phytoplankton organisms was calculated, which is 1.98 % for Chlorophyta, 2.38 % for Cyanophyta, 1.96 % for Euglenophyta and 1.65 % for all others. The overall average efficiency of photosynthesis was thus 1.83 %. Taking into account the area of ​​the water mirror, the arrival of energy, reflection, scattering and absorption. Taking into account the efficiency of photosynthesis, a stoichiometric simulation of the increase in biomass of producers was carried out. The potential of carbon dioxide deposition is 2.6 kg/m2 per year, oxygen release is 1.92 kg/m2, and biomass growth is 1.8 kg/m2. This is the maximum potential. The study is supplemented by derived correction factors based on energy and toxicological indices of environmental safety, ORP and the Redfield ratio. This gives a real total energy per square meter of 12335.66473 kJ, biomass growth of 773.1266184 g/m2. The predictive model yields 577.6 kg/ha per year of fish, which is close to the value of natural bioproductivity for herbivorous fish (505 kg/ha). At the stage of food chain analysis, it is proposed to additionally use the deduced regularity of the effect of salinity on the growth of macroliths and correction coefficients of fish productivity for different soils. The basis for automation and local modeling is laid.