Search Results (4,881)

This study explored the optimization of green hydrogen production via seawater electrolysis powered by a hybrid photovoltaic (PV)-wind system in KwaZulu-Natal, South Africa. A Box–Behnken Design (BBD), adapted from Response Surface Methodology (RSM), was utilized to address the synergistic effect of key operational factors on the integration of renewable energy for green hydrogen production and its economic viability. Addressing critical gaps in renewable energy integration, the research evaluated the feasibility of direct seawater electrolysis and hybrid renewable systems, alongside their techno-economic viability, to support South Africa’s transition from a coal-dependent energy system. Key variables, including electrolyzer efficiency, wind and PV capacity, and financial parameters, were analyzed to optimize performance metrics such as the Levelized Cost of Hydrogen (LCOH), Net Present Cost (NPC), and annual hydrogen production. At 95% confidence level with regression coefficient (R² > 0.99) and statistical significance (p < 0.05), optimal conditions of electricity efficiency of 95%, a wind-turbine capacity of 4960 kW, a capital investment of $40,001, operational costs of $40,000 per year, a project lifetime of 29 years, a nominal discount rate of 8.9%, and a generic PV capacity of 29 kW resulted in a predictive LCOH of 0.124$/kg H₂ with a yearly production of 355,071 kg. Within the scope of this study, with the goal of minimizing the cost of production, the lowest LCOH observed can be attributed to the architecture of the power ratios (Wind/PV cells) at high energy efficiency (95%) without the cost of desalination of the seawater, energy storage and transportation. Electrolyzer efficiency emerged as the most influential factor, while financial parameters significantly affected the cost-related responses. The findings underscore the technical and economic viability of hybrid renewable-powered seawater electrolysis as a sustainable pathway for South Africa’s transition away from coal-based energy systems. Full article

As an important technology to enhance nutrient use efficiency and reduce agricultural non-point source pollution, controlled-release fertilizers (CRFs) have been widely applied in modern agriculture. However, during packaging, transportation, and field application, CRF particles are prone to mechanical impacts, which can lead to particle fragmentation and damage to the controlled-release coating. This compromises the release kinetics, increases nutrient loss risk, and ultimately exacerbates environmental issues such as eutrophication. Currently, studies on the impact-induced fragmentation behavior of CRF particles remain limited, and there is an urgent need to investigate their fragmentation susceptibility mechanisms from the perspective of internal stress evolution. In this study, the mechanical properties of CRF particles were first experimentally determined to obtain essential parameters. A two-layer finite element model representing the coating and core structure of the particles was then constructed, and a fragmentation susceptibility index was proposed as the key evaluation criterion. The index, defined as the ratio of fractured volume to peak impact energy, reflects the efficiency of energy conversion at the critical moment of particle rupture (1–5). An explicit dynamic simulation framework incorporating multiple influencing factors—equivalent diameter, sphericity, impact material, velocity, and angle—was developed to analyze fragmentation behavior from the perspective of energy transformation. Based on the observed effects of these variables on fragmentation susceptibility, three regression models were developed using response surface methodology to quantitatively predict fragmentation susceptibility. Comparative analysis between the simulation and experimental results showed a fragmentation rate error range of 0–11.47%. The findings reveal the relationships between particle fragmentation modes and energy responses under various impact conditions. This research provides theoretical insights and technical guidance for optimizing the mechanical stability of CRFs and developing environmentally friendly fertilization strategies. Full article

The upper limb stretching device plays a key role in enhancing physical function. Current commercial upper limb stretching devices often suffer from limited functionality and are poorly aligned with the biomechanics of the human arm. To address these limitations, this paper presents the design of an ergonomic device for upper limb stretching. Firstly, the development of a regression model for the upper limb force test was carried out through the Box–Behnken Design (BBD) response surface methodology. Secondly, the Denavit-Hartenberg (D-H) method was adopted for the kinematic analysis of the human upper limb stretching mechanism. Subsequently, a kinematic model was established by coupling the data from Creo Parametric and ADAMS models. The kinematic characteristics were then investigated throughout the entire range of motion, yielding the corresponding kinematic parameter curves. Next, the finite element method was employed within ABAQUS to model the upper limb stretching mechanism, to allow for a detailed strength analysis of its key components. Finally, a prototype was manufactured and tested through upper limb stretching experiments to validate its performance. The results demonstrate that the designed stretching mechanism achieved the desired range of motion, with its angular velocity and angular acceleration exhibiting smooth variations. The maximum stress observed is 195.2 MPa, which meets the design requirements. This study provides a valuable reference for the development of future human stretching devices. Full article

To address the challenges of complex composition, high chemical oxygen demand (COD) content, and the difficulty of treating organic wastewater from brewery wastewater, as well as the limitations of traditional Fenton technology, including low catalytic activity and high material costs, this study proposes the use of biochemical sludge as a raw material. Coupled with iron salt activation and mechanical ball milling technology, a low-cost, high-performance iron-doped mesoporous nano-sludge biochar material is prepared. This material was employed as a particle electrode to construct a three-dimensional electro-Fenton system for the degradation of organic wastewater from sauce-flavor liquor brewing. The results demonstrate that the sludge-based biochar produced through this approach possesses a mesoporous structure, with an average particle size of 187 nm, a specific surface area of 386.28 m²/g, and an average pore size of 4.635 nm. Iron is present in the material as multivalent iron ions, which provide more electrochemical reaction sites. Utilizing response surface methodology, the optimized treatment process achieves a maximum COD degradation rate of 71.12%. Compared to the control sample, the average particle size decreases from 287 μm to 187 nm, the specific surface area increases from 44.89 m²/g to 386.28 m²/g, and the COD degradation rate improves by 61.1%. Preliminary investigations suggest that the iron valence cycle (Fe²⁺/Fe³⁺) and the mass transfer enhancement effect of the mesoporous nano-structure are keys to efficient degradation. The Fe-O-Si structure enhances material stability, with a degradation capacity retention rate of 88.74% after 30 cycles of use. When used as a particle electrode to construct a three-dimensional electro-Fenton system, this material demonstrates highly efficiency in organic matter degradation and shows promising potential for application in the treatment of organic wastewater from sauce-flavor liquor brewing. Full article

The leaves of Apocynum venetum L. (A. venetum L.) are a functional food that plays an important role in antioxidation due to its high content of flavonoids and phenolic acids. Therefore, the extraction process of leaves of A. venetum L. is closely related to their activity. In this study, ultra-high-performance liquid chromatography (UHPLC) coupled with diode array detector (DAD), electrospray ionization (ESI), and quadrupole time-of-flight mass spectrometry (QTOF/MS) techniques has been established for qualitative and quantitative analysis of three phenolic acids and six flavonoids in the leaves of A. venetum L. Ultrasonic-assisted extraction conditions for the maximum recovery of phenolic and flavonoid compounds with a high antioxidation effect were optimized by response surface methodology (RSM). The optimum extraction conditions were as follows: ethanol concentration 64%, extraction time 20 min, and liquid-to-solid ratio 16:1 mL/g. The yields of three phenolic acids and six flavonoids under the optimal process were found to be 8.932 ± 0.091 and 20.530 ± 0.198 mg/g, respectively, which matched with those predicted (8.751 and 20.411 mg/g) within a 95% confidence level. Antioxidant activities based on ABTS and DPPH assays showed that the optimal extracts had strong activities compared with those of conventional reflux extraction methods. Moreover, the contribution of total and individual phenolic acids and flavonoids to antioxidant activity was also estimated by Pearson correlation analysis. Full article

This work examines the effect of gadolinium (Gd) promotion on nickel-based SBA-16 catalysts for the dry reforming of methane (DRM), with the goal of improving syngas production by optimizing catalyst composition and operating conditions. Catalysts with varying Gd loadings (0.5–3 wt.%) were synthesised using co-impregnation. XRD, N₂ physisorption, FTIR, XPS, and H₂-TPR–CO₂-TPD–H₂-TPR were used to examine the structural features, textural properties, surface composition, and redox behaviour of the catalysts. XPS indicated formation of enhanced metal–support interactions, while initial and post-treatment H₂–TPR analyses showed that moderate Gd loadings (1–2 wt.%) maintained a balanced distribution of reducible Ni species. The catalysts were tested for DRM performance at 800 °C and a gas hourly space velocity (GHSV) of 42,000 mL g⁻¹ h⁻¹. 1–2 wt.% Gd-promoted catalysts achieved the highest H₂ (~67%) and CO yield (~76%). Response surface methodology (RSM) was used to identify optimal reaction conditions for maximum H₂ yield. RSM predicted 848.9 °C temperature, 31,283 mL g⁻¹ h⁻¹ GHSV, and a CH₄/CO₂ ratio of 0.61 as optimal, predicting a H₂ yield of 96.64%, which closely matched the experimental value of H₂ yield (96.66%). The 5Ni–2Gd/SBA-16 catalyst exhibited minimal coke deposition, primarily of a graphitic character, as evidenced by TGA–DSC and Raman analyses. These results demonstrate the synergy between catalyst design and process optimization in maximizing DRM efficiency. Full article

Magnetohydrodynamic (MHD) flow and heat transfer in porous media are central to many engineering applications, including heat exchangers, MHD generators, and polymer processing. This study examines the boundary layer flow and thermal behavior of an electrically conducting viscous fluid over a porous stretching tube. The model accounts for nonlinear thermal radiation, internal heat generation/absorption, and Darcy–Forchheimer drag to capture porous medium resistance. Similarity transformations reduce the governing equations to a system of coupled nonlinear ordinary differential equations, which are solved numerically using the BVP4C technique with Response Surface Methodology (RSM) and sensitivity analysis. The effects of dimensionless parameters magnetic field strength (M), Reynolds number (Re), Darcy–Forchheimer parameter (Df), Brinkman number (Br), Prandtl number (Pr), nonlinear radiation parameter (Rd), wall-to-ambient temperature ratio (rw), and heat source/sink parameter (Q) are investigated. Results show that increasing M, Df, and Q suppresses velocity and enhances temperature due to Lorentz and porous drag effects. Higher Re raises pressure but reduces near-wall velocity, while rw, Rd, and internal heating intensify thermal layers. The entropy generation analysis highlights the competing roles of viscous, magnetic, and thermal irreversibility, while the Bejan number trends distinctly indicate which mechanism dominates under different parameter conditions. The RSM findings highlight that rw and Rd consistently reduce the Nusselt number (Nu), lowering thermal efficiency. These results provide practical guidance for optimizing energy efficiency and thermal management in MHD and porous media-based systems.: Full article

Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R² > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty. Full article

Sugarcane juice (SJ) is a naturally sweet beverage rich in sucrose but prone to microbial contamination, raising concerns among health-conscious consumers. This study aimed to develop a functional SJ enriched with fructooligosaccharides (FOS) using enzymatic treatment, followed by high-pressure processing (HPP) to enhance its safety and quality. The enzymatic conversion of sucrose to FOS was achieved using Pectinex^® Ultra SP-L (commercial enzyme), with varying enzyme concentrations, temperatures and incubation times to identify the optimal conditions via response surface methodology (RSM). Under optimal conditions (1000 U/g enzyme concentration, 48 °C, 13 h), sucrose in raw SJ (124.33 g/L) decreased by 59.17 g/L, resulting in maximum reducing sugars (16.02 ± 0.58 g/L) and enhanced FOS yields, notably kestose (2.37 g/L) and nystose (9.35 g/L). After being treated with HPP at 600 MPa for 3 min, E. coli K12 and L. innocua were effectively inactivated by achieving > 5 log reduction, meeting USFDA standards. Furthermore, it was also observed that HPP could reduce yeast (6.56 × 10² CFU/mL). Meanwhile, mold, E. coli, and coliforms were not detected. Additionally, HPP maintained the juice’s physicochemical properties, outperforming thermal pasteurization (85 °C for 10 min) in quality preservation. This study highlights the potential of enzymatic treatment and HPP in improving SJ safety and functionality. Full article

The urgent need for advanced analytical tools for environmental monitoring and food safety drives the development of novel biosensing approaches and solutions. A computationally driven workflow for the development of a rapid electrochemical aptasensor for okadaic acid (OA), a critical marine biotoxin, is reported. The core of this strategy is a rational design process, where in silico modeling was employed to optimize the biological recognition element. A 63-nucleotide aptamer was successfully truncated to a highly efficient 31-nucleotide variant. Molecular docking simulations confirmed the high binding affinity of the minimized aptamer and guided the design of the surface immobilization chemistry to ensure robust performance. The fabricated sensor, which utilizes a ferrocene-labeled aptamer, delivered a sensitive response with a detection limit of 2.5 nM (n = 5) over a linear range of 5–200 nM. A significant advantage for practical applications is the remarkably short assay time of 5 min. The sensor’s applicability was successfully validated in complex food matrices, achieving excellent recovery rates of 82–103% in spiked mussel samples. This study establishes an integrated computational–experimental methodology that streamlines the development of high-performance biosensors for critical food safety and environmental monitoring challenges. Full article

This study establishes an efficient and eco-friendly ultrasound-assisted extraction (UAE) method for total flavonoids present in Cercis chinensis seeds using natural deep eutectic solvents (NADES). Among nine NADES formulations screened, choline chloride–levulinic acid (ChCl–Lev, 1:2) demonstrated optimal performance, yielding 112.1 mg/g total flavonoids. Through Response Surface Methodology (RSM), the ultrasound-assisted extraction (UAE) parameters were explored. Under the optimized conditions (water content of 30%, time of 28 min, temperature of 60 °C, and solvent-to-solid ratio of 1:25 g/mL), the total flavonoid yield reached 128.5 mg/g, representing a 195% improvement compared to conventional ethanol extraction. The recyclability of NADES was successfully achieved via AB-8 macroporous resin, retaining 80.89% efficiency after three cycles. Extraction kinetics, modeled using Fick’s second law, confirmed that the rate constant (k) increased with temperature, highlighting temperature-dependent diffusivity as a key driver of efficiency. The extracted flavonoids exhibited potent antioxidant activity, with IC₅₀ values of 0.86 mg/mL (ABTS•⁺) and 0.69 mg/mL (PTIO•). This work presents a sustainable NADES-UAE platform for flavonoid recovery and offers comprehensive mechanistic and practical insights for green extraction of plant bioactives. Full article

This study focuses on optimizing the machinability of Al-Fe-Cu (8000 series) alloys by developing new compositions with varying Fe and Cu contents and evaluating their mechanical, microstructural, and energy performance. For this purpose, 6061 Al alloy was melted in an induction furnace and cast into molds, and samples containing 2.5% and 5% Fe were produced. Microstructural features were analyzed using Python-based image processing, while Specific Energy Consumption (SEC) theory was applied to assess machining efficiency. An alloy with 2.5% Fe and 2.64% Cu showed superior mechanical properties and the lowest energy consumption. Increasing cutting speed and depth of cut notably decreased SEC. Machine learning (ML) analysis confirmed strong predictive capability, with R² values above 0.80 for all models. Decision Tree (DT) achieved the highest accuracy for SEC prediction (R² = 0.98634, MAE = 0.02209, MSE = 0.00104), whereas XGBoost (XGB) performed best for SCEC (R² = 0.96533, MAE = 0.25578, MSE = 0.10178). Response Surface Methodology (RSM) optimization further validated the significant influence of machining parameters on SEC and specific cutting energy consumption (SCEC). Overall, the integration of machine learning (ML), response surface methodology (RSM), and energy equations provides a comprehensive approach to improve the machinability and energy efficiency of 8000 series alloys, offering practical insights for industrial applications. Full article

In this study, the use of spent coffee waste as a green precursor of polyphenolic compounds, which are subsequently employed as reducing agents for the synthesis of zero-valent iron nanoparticles (nZVI) aimed at the efficient removal of dyes from aqueous systems, has been investigated. The nanoparticles, generated in situ in the presence of controlled amounts of hydrogen peroxide, were applied in the removal of organic dyes—including methylene blue, methyl orange, and orange G—through a heterogeneous Fenton-like catalytic process. The synthesized nZVI were thoroughly characterized by nitrogen adsorption at 77 K, scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), and powder X-ray diffraction (XRD). A statistical design of experiments and response surface methodology were employed to evaluate the effect of polyphenol, Fe(III), and H₂O₂ concentrations on dye removal efficiency. Results showed that under optimized conditions, a 100% removal efficiency could be achieved. This work highlights the potential of nZVI synthesized from agro-industrial waste through sustainable routes as an effective solution for water remediation, contributing to circular economy strategies and environmental protection. Full article

This study investigated the effects of ultrasonic treatment of olive paste prior to malaxation on oil yield (Y), enzyme activity and virgin olive oil (VOO) quality in four Croatian olive varieties: Istarska Bjelica, Rosulja, Oblica and Levantinka. The oils were extracted using the Abencor system according to a central composite experiment design, with treatment durations of 3–17 min and power levels of 256–640 W. The parameters analyzed included Y, oxidative stability index (OSI), antioxidant capacity (AC), phenolic and α-tocopherol content, volatile compounds, fatty acid profile, and the activity of lipoxygenase, β-glucosidase, polyphenol oxidase, and peroxidase. Olive variety was the most influential factor in all variables. The response surface methodology showed that ultrasonic treatment at low-to-medium intensity improved several quality attributes. For example, Y increased by 4% in Oblica, phenolic content increased by up to 17% in Istarska Bjelica, and OSI and AC increased by 13–15% in Istarska Bjelica and Levantinka. In contrast, longer treatment and higher ultrasound power had a negative effect. No significant differences were found in other parameters examined. Overall, the application of ultrasound led to measurable, though moderate, improvements in Y and VOO quality, with results strongly dependent on olive variety and treatment conditions. These results underline the need for further optimization tailored to each variety. Full article

The subject of this paper is the computational homogenisation and identification of heterogeneous materials in the form of auxetic structures made of materials with nonlinear characteristics. It is assumed that some of the material and topological parameters of the auxetic structures are uncertain and are modelled as interval numbers. Directed interval arithmetic is used to minimise the width of the resulting intervals. The finite element method is employed to solve the boundary value problem, and artificial neural network response surfaces are utilised to reduce the computational effort. In order to solve the identification task, the Pareto approach is adopted, and a multi-objective evolutionary algorithm is used as the global optimisation method. The results obtained from computational homogenisation under uncertainty demonstrate the efficacy of the proposed methodology in capturing material behaviour, thereby underscoring the significance of incorporating uncertainty into material properties. The identification results demonstrate the successful identification of material parameters at the microscopic scale from macroscopic data involving the interval description of the process of deformation of auxetic structures in a nonlinear regime. Full article