Integration of IoT and predictive modeling is critical for optimizing microclimate management in urban-agglomeration vertical farming. In this study, we present a hybrid digital twin approach that combines a physical microclimate model with a distributed IoT monitoring system to simulate and control the phytotron environment. A set of heat- and mass-balance equations governing the dynamics of temperature, humidity, and transpiration was implemented and parameterized using a genetic algorithm (GA)—an evolutionary optimization method—with real-time data collected over three intervals (72 h, 90 h, and 110 h) from LoRaWAN sensors (temperature, humidity, CO₂) and Wi-Fi-connected power meters managed by Home Assistant. The optimized model achieved mean temperature deviations ≤ 0.1 °C, relative humidity errors ≤ 2%, and overall energy consumption accuracy of 99.5% compared to measured values. The digital twin reliably tracked daily climate fluctuations and system energy use, confirming the accuracy of the hybrid approach. These results demonstrate that the proposed framework effectively integrates theoretical models with IoT-derived data to deliver precise environmental control and energy-use optimization in vertical farming, while also laying the groundwork for scalable digital twins in controlled-environment agriculture.

The increasing share of heavy and high-sulfur crude oils in refinery feed slates worldwide highlights the need for models of delayed coking units (DCUs) that are both physically meaningful and computationally efficient. In this study, we develop and calibrate a simplified yet dynamic one-dimensional model of an industrial coke drum intended for integration into digital twin frameworks. The model includes a three-phase representation of the drum contents, a temperature-dependent global kinetic scheme for vacuum residue cracking, and lumped descriptions of heat transfer and phase holdups. Only three physically interpretable parameters—the kinetic scaling factors for distillate and coke formation and an effective wall temperature—were calibrated using routinely measured plant data, namely the overhead vapor and drum head temperatures and the final coke bed height. The calibrated model reproduces the temporal evolution of the top head and overhead temperatures and the final bed height with mean relative errors of a few percent, while capturing the more complex bottom-head temperature dynamics qualitatively. Scenario simulations illustrate how the coking severity (represented here by the effective wall temperature) affects the coke yield, bed growth, and cycle duration. Overall, the results indicate that low-order dynamic models can provide a practical balance between physical fidelity and computational speed, making them suitable as mechanistic cores for digital twins and optimization tools in delayed coking operations.

The article considers the issue of increasing the structural reliability of a biofuel production plant. A review of the existing basic technological schemes of the biofuel production plant has been carried out. The main structural elements are determined and a functional diagram is constructed. Processed cooking oil was chosen as the input raw material. A structural analysis of the reliability of each element and the entire system as a whole was carried out. The least reliable elements are determined, options for improving the overall reliability of the installation are proposed.

Digital twins (DTs) are increasingly used in controlled-environment agriculture to model microclimates and drive energy-efficient control. However, long-term drift and seasonal variability require continuous recalibration and controller retuning. We develop a self-adaptive DT of a phytotron chamber that combines an MAPE-K loop with an evolutionary heuristic. A genetic algorithm (GA) calibrates the DT parameters against IoT time series and subsequently optimizes heater control settings (two-position, three-position, and proportional modes) subject to comfort constraints on temperature and humidity. Six monitoring intervals (May–June 2025) are used for per-interval calibration and six-fold cross-validation. The calibrated DT reproduces temperature and humidity with high fidelity across unseen intervals: the average cross-validated deviations are 0.27 °C and 7.1%RH (30 transfers). Controller optimization yields cumulative energy savings of 186.54 kWh (3.24%) over six intervals, with per-interval savings ranging from 0.37% to 5.94%. Coupling GA-based DT calibration with model-in-the-loop controller optimization consistently reduces energy use while maintaining microclimate quality, providing a practical pathway for the robust, year-round operation of vertical farms.

Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results. This study introduces a physics-informed combinatorial digital twin for value-optimized coking, aimed at improving energy efficiency and environmental performance through adaptive quality forecasting. The approach builds a modular library of 32 candidate equations grouped into eight quality parameters and links them via cross-parameter dependencies. A two-level optimization scheme is applied: a genetic algorithm selects the best model combination, while a secondary loop tunes parameters under a multi-objective fitness function balancing accuracy, interpretability, and computational cost. Validation on five clustered operating regimes (industrial patterns augmented with noise-perturbed synthetic data) shows that optimal model ensembles outperform single best models, achieving typical cluster errors of ~7–13% NMAE. The developed digital twin framework enables accurate prediction of coke quality parameters that are critical for its energy applications, such as volatile matter and sulfur content, which serve as direct proxies for estimating the net calorific value and environmental footprint of coke as a fuel.

Deterministic microtexture and viscoelastic coating deformation affect the normal response of polymer-coated internal cylindrical surfaces. An additive model separates statistical land roughness, deterministic Fourier displacement, and the coated-minus-bare periodic-cell increment. Half-space (HS) and finite-thickness (FT) single-asperity laws describe the same prescribed summit population for isolated imprints (class A) and a connected helical groove (class B). At 1 MPa, the relaxed texture factor exceeds the instantaneous factor for both classes, and class A exceeds class B. Across the sampled open-contact states, secant and tangent stiffness increase sublinearly with pressure; tangent stiffness exceeds secant stiffness, and relaxation reduces both. Substrate confinement lowers the class A instantaneous total normal approach from 1.626 $\mathsf{\mu }$m in HS to 1.139 $\mathsf{\mu }$m in FT and its instantaneous-to-relaxed increase from 3.23% to 1.27%. For both texture classes in the FT branch, the total normal approach remains below the residual texture depth at all evaluated states and nominal pressures in the range 0.1–5 MPa. Prescribed azimuthal pressure patterns retain their stiffness-spread ranking across classes and elastic endpoints. For a relaxation time of 250 $\mathsf{\mu }$s, a fixed-location hold of 0.509 ms develops approximately 84% of the endpoint change in the hereditary FT base approach and increases the total approach by 1.07% relative to its instantaneous value. Local hereditary histories also resolve surface recovery during unloading. With component-level verification, the framework supports comparisons of texture, confinement and loading duration while identifying intercomponent coupling as a question for a fully coupled reference solution.