The valorization of agricultural wastes such as pumpkin peel generated from the food processing industry through thermochemical conversion offers sustainable solutions for both waste management and carbon cycling. This study aims to evaluate the physicochemical properties and environmental impacts of charcoals produced from pumpkin peel waste (PPW), without the use of chemicals or pre-washing. In this context, pumpkin peel hydrochar (PPH) was produced by hydrothermal carbonization (HTC) and pumpkin peel biochar (PPB) by pyrolysis. The systems were modeled according to a pilot-scale scenario based on the processing of 100 kg of PPW, and the functional unit was defined as the processing of this amount. The properties of the products were determined by various physicochemical characterization techniques, and environmental impacts were analyzed using Life Cycle Assessment (LCA). The results showed that PPH has a higher specific surface area (16.35 m2 g−1) than PPB (9.80 m2 g−1), as well as a higher carbon content (76.18% for PPH and 66.07% for PPB). Furthermore, the environmental impact of PPH (16.42 kg CO2-equivalent/FU) is lower than that of PPB (32.33 kg CO2-equivalent/FU). Based on the obtained physicochemical properties, the potential of both materials as soil conditioners has been evaluated. The lower environmental impact values suggest that PPH may be a more advantageous alternative in terms of sustainability. However, this evaluation is not based on direct soil application experiments, and further applied studies are needed to confirm this potential.

To address early water breakthrough and poor residual-oil mobilization in low-permeability reservoirs, a core–shell nanosphere/surfactant composite system was developed for profile control and enhanced oil recovery. The core–shell nanospheres were prepared by a semi-continuous seed-growth method, and a target particle-size window of 100–200 nm was selected based on pore-throat/particle matching. The representative sample, HK-0417, had an average particle size of about 120 nm and showed good dispersion stability in formation brine at 45 °C. After blending with the surfactant ALT-603, the system achieved an ultralow oil–water interfacial tension on the order of 10−3 mN/m and reduced the water contact angle of the oil-aged surface from 125° to 70°, indicating a shift toward near-neutral wettability. Core-flooding tests further showed that, under the same chemical dosage, slug injection (HK-0417 followed by ALT-603) demonstrated better performance than co-injection, with higher incremental oil recovery (15.49% vs. 13.17%) and higher plugging efficiency during subsequent water flooding (81.25% vs. 78.46%). The novelty of this work lies in integrating particle-size-window design, controllable preparation of core–shell nanospheres, and direct comparison of injection strategies within one system. The results provide practical guidance for formulation design and injection-mode selection for enhanced oil recovery in low-permeability reservoirs.

As reservoir development enters the middle and late stages, variations in formation pressure and water cut lead to significant changes in liquid supply capacity. Under conventional fixed stroke-per-minute (SPM) operation, the production capacity of beam pumping wells often fails to match the dynamically varying inflow, resulting in severe dynamic fluid level fluctuations and subsequent pump-off, gas locking, and abnormal rod string loading. To address these issues, this paper develops a dynamic fluid level model based on the operating mechanism of beam pumping wells, explicitly incorporating system uncertainties and reservoir disturbances. On this basis, a tube-based robust model predictive control (Tube-RMPC) strategy is proposed, in which nominal predictions are combined with local feedback compensation to effectively mitigate model uncertainties and external disturbances. Simulation results demonstrate that, compared with conventional PID control and traditional MPC methods, the proposed approach achieves superior performance in dynamic fluid level tracking accuracy, disturbance rejection, and closed-loop stability.

Decarbonizing downstream steel logistics remains underexplored in sustainable supply chain management. This study proposes a machine learning-enhanced tri-objective optimization framework for the ship stowage planning problem (SSPP). The framework handles heterogeneous finished steel products, including coils, plates, ingots, tubes, and sections. The model simultaneously maximizes deadweight utilization and minimizes a novel Adaptive Weighted Moment Balance (AWMB) index. It also minimizes voyage carbon emissions through a trim-and-heel resistance penalty. A spatial-to-sequential discretization strategy transforms the NP-hard placement problem into a tractable permutation optimization. A deep neural network (DNN) surrogate achieves a 3.57-fold speedup with only 1.52% hypervolume degradation. An improved NSGA-III algorithm with adaptive operators ensures Pareto front exploration. Embedded step-wise moment verification guarantees dynamic stability throughout loading and unloading. Validated on real data from a Chinese steel enterprise, the framework achieves 99.88% deadweight utilization, reduces transverse and longitudinal imbalance by 48.27% and 90.54%, and cuts CO2 emissions by 95.5% per voyage. SOLAS constraints, load line limits, and CII/FuelEU targets are addressed through embedded stability and capacity constraints. Multi-route and weather-dependent validation remains necessary before fleet-scale deployment.

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Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines.

We developed an arbitrary Lagrangian–Eulerian (ALE)-based multiphysics model for evaporation from a contact-line-pinned sessile drop of neat water subject to a vertically oriented sinusoidal alternating current (AC) electric field applied across parallel-plate electrodes. The framework fully couples electrostatics, incompressible flow, heat transfer with evaporative cooling, and transient vapour transport in air, and includes an instantaneous, voltage-controlled electrowetting contact-angle response under constant-contact-radius conditions. Validation against published data shows that the model captures both pinned-droplet evaporation and electrically induced deformation. Because Maxwell traction scales with the squared electric-field magnitude, droplet height and contact angle exhibit a robust 2:1 frequency-doubled response, producing two peak–trough events per voltage period. The resulting periodic deformation drives oscillatory interfacial shear and internal recirculation, yielding a synchronous double-peaked evaporative-flux waveform. Gas-side analysis quantifies a time-varying diffusion-layer thickness via a characteristic diffusion length; two thinning events per period coincide with flux maxima, indicating that AC enhancement is dominated by periodic compression of the vapour boundary layer and reduced gas-side mass-transfer resistance. Increasing voltage amplitude (0–60 kV) strongly accelerates volume loss, while frequency has a secondary effect: the cycle-averaged flux rises from 1 to 10 Hz but decreases slightly at 20 Hz due to phase lag and weaker boundary-layer modulation.

The sustainable management of olive wastes represents an important environmental challenge. Biochars and hydrochars derived from biomass are promising adsorbents for removing emerging pollutants from water. In the present work, olive stone wastes were converted into biochar and hydrochar by using pyrolysis (500 °C for 30 min) and hydrothermal carbonization (HTC) processes (220 °C for 10 h). Then, the obtained materials were physically activated by using CO2 gas (750 °C for 30, 60 and 180 min). Various analytical techniques were applied for the chemical, textural and structural characterization of these carbonaceous materials (i.e., ultimate and proximate analysis, scanning electron microscopy (SEM), BET surface area, Raman spectroscopy, X-ray diffraction, and Fourier transform infrared spectroscopy). Afterwards, the selected activated biochar and hydrochar were applied for the removal of amoxicillin from aqueous solutions. The experimental results show that the generated hydrochar has many microspheres on its surface and inside, while the produced biochar exhibits a porous structure with irregular forms. CO2 physical activation has induced an important improvement of the biochar and hydrochar’s structural, textural, and surface chemistry properties. For instance, the activated biochar samples show a highly porous structure, with large specific surface areas that increase with the burn-off, reaching 1349.3 m2 g−1 following 3 h of activation. Regarding the activated hydrochar samples, they exhibit a spherical morphological structure with an important specific surface area, which increased to 846.7 m2 g−1 after 3 h of activation. Moreover, both activated materials have an amorphous structure with low oxygen surface groups. The selected novel CO2-activated biochar and hydrochar efficiently remove amoxicillin from aqueous solutions under wide experimental conditions, with adsorption capacities of 386.4 and 215.9 mg g−1, respectively. These efficiencies are higher than those reported for various activated biochars derived from lignocellulosic biomass, from sewage sludge, and from animal manure. Future research works are required to assess these materials’ effectiveness in treating real pharmaceutical effluents, to optimize the regeneration of the amoxicillin-loaded materials, and to design full-scale devices for a real application.

The successful application of 3D and 4D food printing is fundamentally governed by the rheology and microstructure of edible inks. These factors control every step, from extrusion and nozzle deposition to the final product functionality. This review systematically examines how formulation variables, including starch/protein composition, water content, and hydrocolloids, determine the network architecture and critical rheological properties, such as yield stress and viscoelasticity. These properties determine printing outcomes such as filament formation, stacking accuracy, and the stability of sensitive components. This review explores 4D printing as a “3D + 1D function,” where printed structures provide additional features over time, such as a controlled color change or bioactive release, while post-printing treatment often activates these features. Through case studies of novel inks, we show how interfacial chemistry and process parameters influence texture and stability. Finally, we discuss the application of rheological metrics for predicting printability and outline the critical need for developing multi-parameter, process-relevant printability indices to advance the field of digital food manufacturing.

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises.