Search Results (598)

This study explores the mechanical behavior of polymer and composite specimens fabricated using fused deposition modeling (FDM), focusing on three material configurations: acrylonitrile butadiene styrene (ABS), carbon fiber-reinforced polyphthalamide (PPA/Cf), and a sandwich-structured composite. A systematic experimental plan was developed using the Box–Behnken design (BBD) to investigate the effects of material type (MT), infill pattern (IP), and printing direction (PD) on tensile and flexural strength. Experimental results showed that the PPA/Cf material with a “Cross” IP printed “Flat” yielded the highest mechanical performance, achieving a tensile strength of 75.8 MPa and a flexural strength of 102.3 MPa. In contrast, the lowest values were observed in ABS parts with a “Grid” pattern and “Upright” orientation, recording 37.8 MPa tensile and 49.5 MPa flexural strength. Analysis of variance (ANOVA) results confirmed that all three factors significantly influenced both outputs (p < 0.001), with MT being the most dominant factor. Machine learning (ML) algorithms, Bayesian linear regression (BLR), and Gaussian process regression (GPR) were employed to predict mechanical performance. GPR achieved the best overall accuracy with R² = 0.9935 and MAPE = 11.14% for tensile strength and R² = 0.9925 and MAPE = 12.96% for flexural strength. Comparatively, the traditional BBD yielded slightly lower performance with MAPE = 13.02% and R² = 0.9895 for tensile strength. Validation tests conducted on three unseen configurations clearly demonstrated the generalization capability of the models. Based on actual vs. predicted values, the GPR yielded the lowest average prediction errors, with MAPE values of 0.54% for tensile and 0.45% for flexural strength. In comparison, BLR achieved 0.79% and 0.60%, while BBD showed significantly higher errors at 1.76% and 1.32%, respectively. Full article

Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on lateritic soil deformation remain poorly understood. This study aims to calibrate and validate discrete element method (DEM) models of lateritic soil at varying moisture contents of 20.51%, 22.39%, 24.53%, 26.28%, and 28.04% by integrating the Hertz–Mindlin contact mechanics with bonding and JKR cohesion models. Key parameters in the simulations were calibrated through systematic experimentation. Using Plackett–Burman design, critical factors significantly affecting axial compressive force—including surface energy, normal bond stiffness, and tangential bond stiffness—were identified. Subsequently, Box–Behnken response surface methodology was employed to optimize these parameters by minimizing deviations between simulated and experimental maximum axial compressive forces under each moisture condition. The calibrated models demonstrated high fidelity, with average relative errors of 4.53%, 3.36%, 3.05%, 3.32%, and 7.60% for uniaxial compression simulations across the five moisture levels. Stress–strain analysis under axial loading revealed that at a given surface displacement, both fracture dimensions and stress transfer rates decreased progressively with increasing moisture content. These findings elucidate the moisture-dependent micromechanical behavior of lateritic soil and provide critical data support for DEM-based design optimization of soil-engaging agricultural implements in tropical environments. Full article

Fermented plant-based beverages represent promising functional foods due to their content of bioactive compounds (polyphenols, prebiotics) and viable probiotic microorganisms. Sprouted buckwheat is a rich source of bioactives and nutrients, which makes it a promising ingredient for the development of synbiotic formulations. This study aimed to optimize the fermentation process of a plant-based beverage composed of germinated buckwheat, honey, inulin, and Lactiplantibacillus plantarum (Lpb. plantarum), using Box–Behnken experimental design (BBD) and Response Surface Methodology (RSM) tools. The influence of three independent variables (inulin, honey, and inoculum concentration) was evaluated on five key response variables: total polyphenol content, flavonoid content, antioxidant activity (RSA%), pH, and starter culture viability. The optimal formulation—comprising 3% inulin, 10% honey, and 6.97 mg/100 mL inoculum—demonstrated functional stability over 21 days of refrigerated storage (4 °C), maintaining high levels of antioxidants and probiotic viability in the fermented beverage. Kinetic analysis of the fermentation process confirmed the intense metabolic activity of Lpb. plantarum, as evidenced by a decrease in pH, active consumption of reducing sugars, and organic acids accumulation. Full article

Gallic acid, a major phenolic compound in wine, significantly influences its sensory profile and health-related properties, making its accurate measurement essential for both enological and nutritional studies. In this context, a derivatization protocol for gallic acid using ethyl chloroformate (ECF) was developed and optimized for GC-MS analysis, with experimental conditions refined through a Box–Behnken Design (BBD). The BBD systematically investigated the effects of three critical reagent volumes: ethyl chloroformate, pyridine, and ethanol. This approach elucidated complex interactions and quadratic effects, leading to a predictive second-order polynomial model and identifying the optimal derivatization conditions for maximum yield (137 µL of ethyl chloroformate, 51 µL of pyridine, and 161 µL of ethanol per 150 µL of wine). The BBD-optimized GC-MS method was validated and successfully applied to quantify gallic acid in diverse commercial wine samples (white, red, conventional, natural). A key finding was the method’s wide dynamic range, enabling accurate quantification from 5 up to over 600 µg/mL without sample dilution. This work represents, to our knowledge, the first application of a BBD for optimizing the ethyl chloroformate derivatization of gallic acid, providing a robust, efficient, and widely applicable analytical tool for routine quality control and enological research. Full article

To address the technical problems of broad droplet size spectrum, insufficient atomization uniformity, and spray drift in plant protection unmanned aerial vehicle (UAV) applications, this study developed a novel two-stage aerial electrostatic spraying device based on the coupled mechanisms of hydraulic atomization and electrostatic induction, and, through the integration of three-dimensional numerical simulation and additive manufacturing technology, a new two-stage inductive charging device was designed on the basis of the traditional hydrodynamic nozzle structure, and a synergistic optimization study of the charging effect and atomization characteristics was carried out systematically. With the help of a charge ratio detection system and Malvern laser particle sizer, spray pressure (0.25–0.35 MPa), charging voltage (0–16 kV), and spray height (100–1000 mm) were selected as the key parameters, and the interaction mechanism of each parameter on the droplet charge ratio (C/m) and the particle size distribution (Dv50) was analyzed through the Box–Behnken response surface experimental design. The experimental data showed that when the charge voltage was increased to 12 kV, the droplet charge-to-mass ratio reached a peak value of 1.62 mC/kg (p < 0.01), which was 83.6% higher than that of the base condition; the concentration of the particle size distribution of the charged droplets was significantly improved; charged droplets exhibited a 23.6% reduction in Dv50 (p < 0.05) within the 0–200 mm core atomization zone below the nozzle, with the coefficient of variation of volume median diameter decreasing from 28.4% to 16.7%. This study confirms that the two-stage induction structure can effectively break through the charge saturation threshold of traditional electrostatic spraying, which provides a theoretical basis and technical support for the optimal design of electrostatic spraying systems for plant protection UAVs. This technology holds broad application prospects in agricultural settings such as orchards and farmlands. It can significantly enhance the targeted deposition efficiency of pesticides, reducing drift losses and chemical usage, thereby enabling agricultural enterprises to achieve practical economic benefits, including reduced operational costs, improved pest control efficacy, and minimized environmental pollution, while generating environmental benefits. Full article

Caragana korshinskii Kom. (CKB), widely cultivated in Inner Mongolia, China, has potential for silage feed development due to its favorable nutritional characteristics, including a crude protein content of 14.2% and a neutral detergent fiber content below 55%. However, its vascular bundle fiber structure limits the efficiency of lactic acid conversion and negatively impacts silage quality, which can be improved through mechanical crushing. Currently, conventional crushing equipment generally suffers from uneven particle size distribution, high energy consumption, and low processing efficiency. In this study, a layered aggregate model was constructed using the discrete element method (DEM), and the Hertz–Mindlin with Bonding contact model was employed to characterize the heterogeneous mechanical properties between the epidermis and the core. Model accuracy was enhanced through reverse engineering and a multi-particle-size filling strategy. Key parameters were optimized via a Box–Behnken experimental design, with a core normal stiffness of 7.37 × 10¹¹ N·m⁻¹, a core shear stiffness of 9.46 × 10¹⁰ N·m⁻¹, a core shear stress of 2.52 × 10⁸ Pa, and a skin normal stiffness of 4.01 × 10⁹ N·m⁻¹. The simulated values for bending, tensile, and compressive failure forces had relative errors of less than 10% compared to experimental results. The results showed that rectangular hammers, due to their larger contact area and more uniform stress distribution, reduced the number of residual bonded contacts by 28.9% and 26.5% compared to stepped and blade-type hammers, respectively. Optimized rotational speed improved dynamic crushing efficiency by 41.3%. The material exhibited spatial heterogeneity, with the mass proportion in the tooth plate impact area reaching 43.91%, which was 23.01% higher than that in the primary hammer crushing area. The relative error between the simulation and bench test results for the crushing rate was 6.18%, and the spatial distribution consistency reached 93.6%, verifying the reliability of the DEM parameter calibration method. This study provides a theoretical basis for the structural optimization of crushing equipment, suppression of circulation layer effects, and the realization of low-energy, high-efficiency processing. Full article

This study investigates the optimization of enzymatic hydrolysis for enhancing carbohydrate release from microalgal biomass and its subsequent impact on methane production during anaerobic digestion. Using Response Surface Methodology with a Box–Behnken design comprising 15 experimental runs, the effects of enzyme loading (20–40 mg/gVS), pH (4.5–5.5), and incubation time (24–72 h) were evaluated. A quadratic regression model was developed to predict carbohydrate release, revealing significant interactions between these factors. The optimal conditions for enzymatic hydrolysis were determined to be a cellulase dose of 20 mg/gVS, pH 5.0, and an incubation period of 72 h. The model demonstrated excellent predictive accuracy, with an R² value of 0.9894 and an adjusted R² of 0.9704. Enzymatic hydrolysis significantly improved methane and biogas yields, with cumulative production reaching 52.50 mL/gVS and 95.62 mL/gVS, respectively, compared to 6.98 mL/gVS and 20.94 mL/gVS for untreated samples. The findings highlight the importance of optimizing enzyme loading and reaction time, while pH variations within the studied range had minimal impact. This study underscores the potential of enzymatic hydrolysis to enhance the bioavailability of organic matter, thereby improving the efficiency of anaerobic digestion for biogas production. Full article

Optimizing the design of impellers in turbomachinery is crucial for improving its energy efficiency, structural integrity, and hydraulic performance in various engineering applications. This work proposes a novel modular framework for impeller optimization that integrates high-fidelity CFD and FEM simulations, AI-based surrogate modeling, and multi-objective evolutionary algorithms. A comprehensive analysis of over one hundred recent studies was conducted, with a focus on advanced computational and hybrid optimization techniques, CFD, FEM, surrogate modeling, evolutionary algorithms, and machine learning approaches. Emphasis is placed on multi-objective and data-driven strategies that integrate high-fidelity simulations with metamodels and experimental validation. The findings demonstrate that hybrid methodologies such as combining response surface methodology (RSM), Box–Behnken design (BBD), non-dominated sorting genetic algorithm II (NSGA-II), and XGBoost lead to significant improvements in hydraulic efficiency (up to 6.7%), mass reduction (over 30%), and cavitation mitigation. This study introduces a modular decision-making framework for impeller optimization which considers design objectives, simulation constraints, and the physical characteristics of turbomachinery. Furthermore, emerging trends in open-source tools, additive manufacturing, and the application of deep neural networks are discussed as key enablers for future advancements in both research and industrial applications. This work provides a practical, results-oriented framework for engineers and researchers seeking to enhance the design of impellers in the next generation of turbomachinery. Full article

To address poor film pickup, incomplete soil–film separation, and high soil content in conventional residual film recovery machines, this study designed a belt-tooth type residual film recovery machine. Its core component integrates flexible belts with nail-teeth, providing both overload protection and efficient conveying. EDEM simulations compared film pickup performance across tooth profiles, identifying an optimal structure. Based on the kinematics and mechanical properties of residual film, a film removal mechanism and packing device were designed, incorporating partitioned packing belts to reduce soil content rate in the collected film. Using Box–Behnken experimental design, response surface methodology analyzed the effects of machine forward speed, film-lifting tooth penetration depth, and pickup belt inclination angle. Key findings show: forward speed, belt angle, and tooth depth (descending order) primarily influence recovery rate; while tooth depth, belt angle, and forward speed primarily affect soil content rate. Multi-objective optimization in Design-Expert determined optimal parameters: 5.2 km/h speed, 44 mm tooth depth, and 75° belt angle. Field validation achieved a 90.15% recovery rate and 5.86% soil content rate. Relative errors below 2.73% confirmed the regression model’s reliability. Compared with common models, the recovery rate has increased slightly, while the soil content rate has decreased by more than 4%, meeting the technical requirements for resource recovery of residual plastic film. Full article

Considering the complexity of laser cutting technology, and difficulties and limitations when applying traditional multi-criteria decision-making (MCDM) methods, this study proposes a fuzzy MCDM methodology for the analysis of the fibre laser cutting process, assessment of alternative cutting conditions and selection of favourable cutting conditions. The experiment in fibre laser cutting of mild steel was based on a Box–Behnken design by considering three input parameters (focus position, cutting speed and oxygen pressure) and four relevant criteria for the assessment of cutting conditions (kerf width on a straight and curved cut, surface roughness and surface productivity). The proposed fuzzy MCDM methodology makes use of expert knowledge and experimental data for criteria evaluation and decision matrix development, respectively, while three fuzzy MCDM methods (fuzzy TOPSIS, fuzzy WASPAS and fuzzy ARAS) were used to determine the complete ranking of alternatives. Kendall’s tau-b and Spearman’s rho correlation tests were applied to compare the obtained ranking lists, while the stability of the ranking was assessed with the application of the Monte Carlo simulation. Finally, to approximate the fuzzy decision-making rule, a second-order model was developed to reveal the significance of process parameters and identify favourable laser cutting conditions. Full article

In the silk production process, cocoons from Bombyx mori worm are degummed and separated from their components. This step generates large residual quantities of an aqueous solution containing various chemical substances, including sericin—a protein that, when discarded improperly, negatively impacts the environment. Sodium bicarbonate and coconut soap are commonly used in the degumming process. The phosphates in the soap and the sodium bicarbonate increase the biological oxygen demand (BOD) and chemical oxygen demand (COD), leading to water contamination. In this study, a Box–Behnken experimental design was used to maximize the extraction of sericin through a conventional extraction under chemical-free conditions. From a total of 45 experiments, the optimal extraction conditions were identified as a solid-to-liquid ratio of 1:20 w/v, a temperature of 120 °C, and 90 min of extraction time. Sericin yields ranged from 9% to 18%. Infrared spectroscopic characterization of the extracted sericin confirmed the presence of protein-specific functional groups and common interactions associated with β-sheet structures. Fractions of high molecular weight (50 kDa to 200 kDa), identified by means of Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) analysis, demonstrate the potential functionality of extracted sericin for the development of biopolymer films useful in biomedical and food industry applications. The optimized methodology is a good alternative to recycle the waste of sericulture chain for obtaining extracts enriched in sericin, as well as to promote the mechanization of artisanal production processes. Full article

This research aims to synthesize a novel hydrogel bio-composite based on natural clay, sodium alginate (Na-AL), and iota-carrageenan as adsorbents to remove phosphate ions from aqueous solutions. The adsorbents were characterized by a variety of techniques, such as Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy coupled with energy dispersive X-rays (SEM-EDX), and the determination of point zero charge (PZC). This research investigated how the adsorption process is influenced by parameters such as adsorbent dose, contact time, solution pH, and temperature. In this study, we used four isotherms and four kinetic models to investigate phosphate ion removal on the prepared bio-composite. The results showed that the second-order kinetic (PSO) model is the best model for describing the adsorption process. The findings demonstrate that the R² values are highly significant in both the Langmuir and Freundlich models (very close to 1). This suggests that Langmuir and Freundlich models, with a diversity of adsorption sites, promote the adsorption of phosphate ions. The maximum adsorbed amounts of phosphate ions by the bio-composite used were 140.84 mg/g for H₂PO₄⁻ ions and 105.26 mg/g for HPO₄²⁻ ions from the batch system. The positive ∆H° confirms the endothermic and physical nature of adsorption, in agreement with experimental results. Negative ∆G° values indicate spontaneity, while the positive ∆S° reflects increased disorder at the solid–liquid interface during phosphate uptake. The main parameters, including adsorbent dosage (mg), contact time (min), and initial concentration (mg/L), were tuned using the Box–Behnken design of the response surface methodology (BBD-RSM) to achieve the optimum conditions. The reliability of the constructed models is demonstrated by their high correlation coefficients (R²). An R² value of 0.9714 suggests that the model explains 97.14% of the variability in adsorption efficiency (%), which reflects its strong predictive capability and reliability. Finally, the adsorption behavior of phosphate ions on the prepared bio-composite beads was analyzed using an artificial neural network (ANN) to predict the process efficiency. The ANN model accurately predicted the adsorption of phosphate ions onto the bio-composite, with a strong correlation (R² = 0.974) between the predicted and experimental results. Full article

Spent coffee grounds are the most abundant waste generated during the preparation of coffee beverages, amounting to 60 million tons per year worldwide. Excessive food waste production has become a global issue, emphasizing the need for waste valorization through the bioprocess of solid-state fermentation (SSF) for high added-value compounds. This work aims to identify the operational conditions for optimizing the solid-state fermentation process of spent coffee grounds to recover bioactive compounds (as polyphenols). An SSF process was performed using two filamentous fungi (Trichoderma harzianum and Rhizopus oryzae). An exploratory design based on the Hunter & Hunter method was applied to analyze the effects of key parameters such as inoculum size (spores/mL), humidity (%), and temperature (°C). Subsequently, a Box–Behnken experimental design was carried out to recovery of total polyphenols. DPPH, ABTS, and FRAP assays evaluated antioxidant activity. The maximum concentration of polyphenols was observed in treatment T3 (0.279 ± 0.002 TPC mg/g SCG) using T. harzianum, and a similar result was obtained with R. oryzae in the same treatment (0.250 ± 0.011 TPC mg/g SCG). In the Box–Behnken design, the most efficient treatment for T. harzianum was T12 (0.511 ± 0.017 TPC mg/g SCG), and for R. oryzae, T9 (0.636 ± 0.003 TPC mg/g SCG). These extracts could have applications in the food industry to improve preservation and functionality. Full article

Bacterial cellulose (BC), an extracellular polysaccharide synthesized by various bacterial strains. It exhibits high tensile strength, water retention, crystallinity, and biocompatibility, making it valuable in biomedical, cosmetic, food, textile, and paper industries. This study examined the effects of six carbon sources on BC production by Komagataeibacter sucrofermentans, identifying fructose as the most effective. A Box–Behnken experimental design was employed to investigate the effects of three variables (fructose concentration, temperature, and cultivation time) on cellulose yield. The optimized cultivation conditions were: fructose concentration of 227.5 g/L, temperature of 28.0 °C, and cultivation time of 295 h, resulting in a BC yield of 63.07 ± 2.91 g/L. Subsequently, BC’s potential as a bacteriophage carrier was assessed. Escherichia coli phage T4 and Staphylococcus aureus phage vB_SauS_CS1 (CS1) were immobilized within BC hydrogels, and their antibacterial activities were assessed through in vitro experiments. These findings suggest BC’s promise as a phage delivery platform for biomedical applications. Full article

This study investigates the modeling of surface roughness (Ra) in the laser cutting of EN 10130 steel process by integrating classical statistical and machine learning methods. First, a quadratic model was developed using response surface methodology (RSM) based on a Box–Behnken experimental design with 17 runs, using cutting speed, laser power, and auxiliary gas pressure as input parameters. Although the RSM model achieved an R² value of 0.8227, there were still some nonlinear deviations between the predicted and experimental values. To improve the prediction accuracy, a regression tree algorithm was applied to model the residuals of the RSM output. The resulting hybrid model, which combines RSM predictions with machine learning-based corrections, yielded a higher R² of 0.8889 and a lower RMSE compared to the original RSM model. A leave-one-out cross-validation (LOOCV) was performed to evaluate the generalization, which resulted in an RMSE of 0.3241 and an R² of 0.6039. These findings confirm the effectiveness of the hybrid approach in capturing complex dependencies and improving prediction accuracy, highlighting its potential for advanced process modeling in laser machining. Full article