Ivan S. Nekrasov

Rotary and vibro-rotary burnishing create regular arrays of imprints whose geometric interaction depends on the pitch, indentation depth, and relative curvature of the tool–workpiece pair. This paper develops a unified kinematic–curvature model that maps machine settings to an imprint layout on the unwrapped cylindrical surface and constructs the relative curvature tensor. The tensor state and full normalized neighbor metric assign the contact state to one of four mutually exclusive categories: non-elliptic, cell-limited, near-conformal (warning), or isolated elliptic. The tensor formulation is invariant under rotation of the tangent basis and provides a common geometric mapping for external cylinders and internal tubes. The kinematic map and tensor classification are independent of the rigid-plastic mean-pressure approximation and are verified by the factor relation between the center-line slope and imprint orientation, tensor invariance, limiting cases, and a closed-form identity. The calibrated approximation is used solely to estimate the maximum-load penetration and an equivalent projected footprint. At a fixed normal force and effective hardness, the projected area is identical in all curvature cases, whereas the curvature changes the penetration and footprint aspect ratio. Neglecting the workpiece curvature underestimates the external cylinder indentation depth by about 5.8% relative to the full tensor calculation; this ratio is independent of the force and effective hardness within the approximation. For the stated internal tube row pitch and hardness, the axis-aligned cell-limited transition force is 5–8 N. Evaluation with the full metric shows that the consecutive-event vectors are separated well; the periodic-row neighbor nevertheless places the reference case in the cell-limited class. A closed-form expression for this transition force is derived. The model provides a transition criterion verified by analytical identity and consistency checks; residual geometry and post-threshold pressure redistribution require unloading calibration and a periodic unilateral contact formulation, respectively.

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.

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.

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.

В условиях роста доли тяжёлого сырья в нефтепереработке целенаправленное получение нефтяного кокса с заданной структурой является ключевой задачей для максимизации экономической отдачи. Существующие методы визуальной классификации обладают субъективностью и значительным временным лагом, что исключает возможность оперативного управления процессом. В данной работе проведён сравнительный анализ эффективности трёх методов машинного обучения: многослойного персептрона, метода опорных векторов и градиентного бустинга для решения задачи многоклассовой классификации структурного типа кокса на основе промышленных данных установки замедленного коксования. Использован массив исторических данных, включающий параметры сырья и технологического режима. Результаты показали, что наивысшую точность (98,5%) и сбалансированность метрик демонстрирует нейросетевая модель, превзойдя метод градиентного бустинга (96,7%) и метод опорных векторов (91,3%). Выявлены и ранжированы ключевые факторы, влияющие на формирование структуры: максимальная температура, коксуемость сырья и скорость нагрева. Разработанная модель предлагается в качестве интеллектуального модуля для интеграции в архитектуру гибридного цифрового двойника технологического процесса, обеспечивая переход к предиктивному управлению качеством продукции.