Second-order Regression Model Research Articles

Calibration of Discrete Element Method Parameters for a High-Fidelity Lunar Regolith Simulant Considering the Effects of Realistic Particle Shape

The Discrete Element Method (DEM) is an important tool for investigating the geotechnical properties of lunar regolith. The accuracy of DEM simulations largely depends on precise particle modeling and the appropriate selection of mesoscopic parameters. To enhance the reliability and accuracy of the DEM in lunar regolith studies, this paper utilized the high-fidelity IRSM-1 lunar regolith simulant to construct a DEM model with realistic particle shapes and conducted an angle of repose (AoR) simulation test. The optimal DEM parameters were calibrated using a combination of the Plackett–Burman test, steepest ascent test, and Box–Behnken design. The results indicate that the sliding friction coefficient, rolling friction coefficient, and surface energy significantly influence the simulation AoR. By optimizing against the measured AoR using a second-order regression model, the optimal parameter values were determined to be 0.633, 0.401, and 0.2, respectively. Under these optimal parameters, the error between the simulation and experimental AoR was 2.1%. Finally, the calibrated mesoscopic parameters were validated through a lifting cylinder test, showing an error of 6.3% between the simulation and experimental results. The high similarity in the shape of the AoR further confirms the accuracy and reliability of the parameter calibration method. This study provides a valuable reference for future DEM-based research on the mechanical and engineering properties of lunar regolith.

Experimental Optimization of the Propeller Turbine Performance Using the Response Surface Methodology

The growing global energy demand necessitates a shift towards sustainable sources to mitigate environmental issues and ensure energy security. This work explores the design and optimization of propeller-type hydrokinetic turbines to efficiently harness renewable energy from water currents. Through experimental testing and regression modeling, the research aimed to maximize the power coefficient (Cp) by determining the optimal values of the number of blades (Z) and the turbine diameter to hub diameter ratio (d/D). By correcting for experimental biases, the study elucidates the importance of factors such as the blockage ratio and turbine configuration on its performance. A second-order polynomial regression model, which was validated through analysis of variance, determined that when Z and d/D were set at 4 and 0.15, respectively, the optimal value for Cp was 53.62%. These findings provide valuable insights for optimizing hydrokinetic turbine efficiency, contributing to the advancement of renewable energy technologies.

Optimization of bio-oil production parameters from the pyrolysis of elephant grass (Pennisetum purpureum) using response surface methodology

Abstract The need to increase bio-oil yield from biomass and enhance its fuel properties has driven research into optimizing the pyrolysis process. This study investigated the influence of three key process parameters—temperature, heating rate, and nitrogen flow rate—on the pyrolysis of elephant grass (Pennisetum purpureum) in a fixed-bed reactor. Response surface methodology was used to study the impact of the aforementioned variables on bio-oil yield to improve its production efficiency. Proximate analysis of the biomass revealed 79.24 wt% volatile matter, 14.22 wt% fixed carbon, and 5.86% ash, with ultimate analysis showing 45.44% carbon, 5.59% hydrogen, and 40.95% oxygen. The high volatile matter content and favourable carbon and hydrogen percentages indicate that elephant grass is a viable energy source due to its potential for high bio-oil yield and energy content. The resulting bio-oil exhibited a higher heating value of 20.9 MJ/kg, indicating its suitability for various heating applications. A second-order regression model was developed for bio-oil yield, with optimal conditions identified as a temperature of 550°C, a heating rate of 17°C/min, and a nitrogen flow rate of 6 ml/min. The study achieved an optimal bio-oil yield of 59.03 wt%, and the model’s high R² value of 0.8683 from analysis of variance analysis confirmed its predictive accuracy. This research highlights elephant grass as a sustainable feedstock for bio-oil production, offering valuable insights into optimizing pyrolysis conditions to enhance bio-oil yield, thus advancing biofuel technology.

Growth of peaches at three altitudes in the Santander mountains of Northeastern Colombia

A study was conducted in the high tropics in the province of Pamplona, Colombia, with the objective of evaluating the growth of peach, Jarillo variety, under three different altitudes (1670, 1870, and 2170 masl). A total of eight trees were sampled per altitude for the purposes of this study. Ten fruits were taken from the middle third, and their mass was measured as follows: fresh and dry from the fruit and fruit pulp, pulp-seed ratio, and the roundness index during the three stages of development after defoliation. A multivariate analysis of variance and a nested longitudinal mixed model were employed to analyze the total fresh mass from three altitudes. The simple effects of altitude and the phenological stages and their interaction were found to be significant when the nested structure of the random effects, fruits nested in trees, and trees in altitudes were incorporated. The effects of time and altitude demonstrated a significant interaction for all variables. In all cases, 1670 masl was the altitude which had the highest fresh mass and the pulp-seed relationship, which it allowed a higher percentage of pulp from harvested fruits for agro-industrial processes. The pattern of relationship between fruit-associated variables as a function of days after defoliation was explained using a second-order polynomial regression model.

Research on the Characterization of Bonding Parameters for Ore Particles Based on Response Surface Methodology

Ore is a crucial component in the process of industrialization, and its crushing is a practice that is inextricably linked to our society. This study aims to simulate the crushing process of minerals in a comminution device using the Discrete Element Method (DEM) by characterizing the bonding parameters of mineral particles. Utilizing the EDEM software (2018) for discrete element simulations, the study investigated the influence of bonding parameters on the compressive strength and other performance indicators of the particle bonding model. The study was executed through the application of a Box–Behnken Design (BBD) and Response Surface Methodology (RSM), which facilitated the construction of a second-order response surface regression model. The optimal values for normal stiffness per unit area, shear stiffness per unit area, critical normal stress, and critical shear stress were meticulously determined. The subsequent simulation experiments strongly verify the feasibility of the proposed characterization method for key parameters.

Optimization and Modelling of the Physical and Mechanical Properties of Grass Fiber Reinforced with Slag-Based Composites Using Response Surface Methodology.

The construction industry's high energy consumption and carbon emissions negatively impact the ecological environment; large-scale construction projects consume much energy and emit a significant amount of CO2 into the atmosphere. Statistics show that 30% of energy loss and 40% of solid waste in the construction industry are generated during construction. Therefore, reducing emissions during construction has significant research potential and value. Many scholars have recently studied eco-friendly building materials to facilitate the use of high-carbon emission materials like cement. Adding fibers to composite materials has become a research hotspot among these studies. Although adding fibers to composite materials has many advantages, it mainly reduces the compressive strength of the composite material. This research used the response surface methodology (RSM) to optimize the raw material ratios and thus improve the performance of plant fiber composite materials. Single-factor experiments were conducted to analyze the effects of grass size, grass content, and quicklime content on the composite materials' compressive strength, flexural strength, and water absorption. The influencing factors and levels for the response surface experiment were determined based on the results of the single-factor analysis. Using the response surface methodology (RSM), a second-order polynomial regression model was established to analyze the interaction effects of the three factors on the composite materials' compressive strength, flexural strength, and water absorption rate. The optimal ratio was determined: the optimized options for grass size, grass content, and quicklime content are 2.0 mm, 8.2 g, and 38 g, respectively. The actual values of compressive strength, flexural strength, and water absorption rate of the composite materials made according to the predicted ratio are 11.425 MPa, 2.145 MPa, and 21.89%, respectively, with a relative error of 8% between the actual and predicted values. X-ray diffraction and scanning electron microscopy were also used to reveal the factors contributing to the relatively high strength of the optimized samples.

Synergizing metakaolin and waste-glass for sustainable and functional UHPCC– a central composite design and ecological footprint assessment

Optimizing ultra-high-performance cementitious composites (UHPCC) with sustainable materials often compromises certain properties due to the chemical nature of alternative binders used to replace standard UHPCC components like Portland cement (OPC) and microsilica (MS). In the context of Waste-Glass-UHPCC, the reduction in early strength, attributed to the varied pozzolanic reactivity of MS and recycled glass powder as cementitious materials, may reduce its application that requires high early strength. This study addresses the early strength deficiency of Waste-Glass-UHPCC by incorporating metakaolin (MK) and also aims to assess the impact of MK on 28-day compressive strength, durability, and carbon footprint. Research investigating the effects of MK on UHPCC with reduced OPC and MS content is currently limited. Specifically, this investigation explores the influence of MK on UHPCC characterized by a cement content of 620 kg/m3. By utilizing advanced statistical methods [i.e., the Central Composite Design (CCD) and Response Surface Methodology (RSM)], this study investigates the substitution of MK for OPC in formulations of Waste-Glass-UHPCC. The experimental design comprises 18 encoded mixtures, allowing for the development of second-order regression models for critical responses [slump flow and compressive strength at different ages]. Besides, visualization through RSM-3D graphs facilitates a comprehensive examination of the effects of various factors. For a deeper analysis, two of the CCD's points, i.e., SP-12 (Waste-Glass-UHPCC without metakaolin), and SP-10 (Waste-Glass-UHPCC with 65.77 kg of metakaolin per cubic meter) were further investigated along with a control dosage that represents a typical UHPCC formulation. The responses studied encompassed 1, 7, and 28 d compressive strength, chloride ion penetration, and carbon footprint measured by a systematic life cycle analysis (following ISO 15804), assessing the environmental impact of the UHPCC's making materials from raw extraction to disposal. Despite encountering challenges in meeting self-compacting concrete criteria due to MK's tendency to accelerate hydration, the enhanced early strength properties (61 % higher than those of the Waste-Glass-UHPCC) make the MK-based mixture appropriate for various applications such as bridge engineering or pavement rehabilitation. Additionally, the addition of MK in UHPCC formulation demonstrated superior resistance to chloride ion permeability even compared to the control mixture, attributed to MK's ability to enhance the concrete's microstructure and capture chloride ions, attributed to Friedel's salt formation. Noteworthy findings include a significant (27 %) reduction in carbon footprint with the incorporation of MK in Waste-Glass-UHPCC compared to the control counterparts. The current study highlights the durability and environmental advantages of MK-based UHPCC blends while emphasizing the critical requirements of workability and strength development of sustainable UHPCC.

Simulation and verification of discrete element parameter calibration of pulverized coal particles

ABSTRACT To enhance the simulation efficiency of the EDEM discrete element numerical simulation software for coal powder mineral particles, experimental data on the rest angle of coal powder particles were collected as the response variable. Five parameters related to coal powder particles within the Hertz-Mindlin no-slip contact model were selected for investigation. Initially, the Plackett-Burman experiment identified three parameters: collision restitution coefficient, static friction coefficient, and rolling friction coefficient, significantly influencing the resting angle. Subsequently, the steepest ascent experiment determined the significant parameter ranges as follows: the collision restitution coefficient ranged from 0.20 to 0.40, the particle-particle static friction coefficient ranged from 0.30 to 0.50, and the particle-particle dynamic friction coefficient ranged from 0.06 to 0.10. Finally, the second-order regression model for the angle of repose and the saliency parameters was developed and optimized using the Box-Behnken experimental design. The following parameter combination was obtained: the collision recovery coefficient was 0.30, the particle-particle static friction coefficient was 0.49, and the particle-particle dynamic friction coefficient was 0.09. This study aimed to compare the angle of repose between the simulation experiment and the actual experiment. The relative error was only 1.02%, indicating that the simulation experiment achieved the optimal parameter combination.

Optimization of iron electrocoagulation parameters for enhanced turbidity and chemical oxygen demand removal from laundry greywater

This study explores the optimization of iron electrocoagulation for treating laundry greywater, which accounts for up to 38% of domestic greywater. Characterized by high concentrations of surfactants, detergents, and suspended solids, laundry greywater presents complex challenges for treatment processes, posing significant environmental and health risks. Utilizing response surface methodology (RSM), this research developed a second-order polynomial regression model focused on key operational parameters such as the area-to-volume ratio (A/V), current density, electrolysis time, and settling time. Optimal treatment conditions were identified: an A/V ratio of 30 m2/m3, a current density of 10 mA/cm2, an electrolysis duration of 50 min, and a settlement period of 12 h. Under these conditions, exceptional treatment outcomes were achieved, with turbidity removal reaching 94.26% and COD removal at 99.64%. The model exhibited high effectiveness for turbidity removal, with an R2 value of 94.16%, and moderate effectiveness for COD removal, with an R2 value of 75.90%. The interaction between the A/V ratio and electrolysis time particularly underscored their critical role in electrocoagulation system design. Moreover, these results highlight the potential for optimizing electrocoagulation parameters to adapt to daily fluctuations in greywater production and meet specific household reuse needs, such as toilet flushing. This tailored approach aims to maximize contaminant separation and coagulant efficiency, balance energy use and operational costs, and contribute to sustainable water management.

Vibration source analysis and structural optimization design of rotary table based on OTPA.

Due to the influence of coal rock shape, hardness, working environment and other factors in the cutting process of cantilever roadheader, the cutting head will produce irregular and violent vibration. As the rotary table of key stress components, its operation process stability, dynamic reliability and life affect the cutting efficiency and cutting stability of cantilever roadheader. In order to study the vibration characteristics of the rotary table in the cutting process, firstly, based on the theory of spatial force analysis and calculation, the spatial mechanical model of the rotary table of the cantilever roadheader is established. By solving the balance equation of the rotary table force system, the variation law of the load at the hinge ear of the rotary table with the cutting pitch angle and the horizontal angle is obtained. Secondly, based on the path transfer analysis method of working condition, the vibration data of cutting head, cutting cantilever, cutting lifting and rotary hydraulic cylinder under stable cutting condition are taken as input signals. By constructing the transfer path analysis model of rotary table working condition, the synthetic vibration of rotary table in cutting process is simulated, and the main vibration source of rotary table is determined. Then, the vibration contribution and contribution degree of each vibration excitation point to the hinge ear of rotary table are studied. By building a cutting test bench, the vibration response of rotary table in cutting process is tested to verify the correctness of the theoretical model.Thirdly, based on the frequency domain analysis method of random vibration fatigue life, combined with the S-N curve of the rotary table, the PSD curve at the maximum stress of the rotary table is obtained by modal excitation method, and the load data is imported into ANSYS nCode software to obtain the life cloud diagram and damage cloud diagram of the rotary table, and then the fatigue life of the rotary table under symmetrical cyclic load is solved. Finally, based on the response surface optimization analysis method, the maximum stress and maximum deformation of the rotary table are taken as the optimization objectives, and the aperture of each hinge ear of the rotary table is taken as the optimization variable. Based on Design Expert, a second-order regression model is established to realize the multi-objective optimization design of the key stress parts of the rotary table in the cutting process. The simulation results show that under the same cutting conditions, the maximum stress of the optimized rotary table is reduced by 15.82% year-on-year, and the maximum deformation is reduced by 24.70% year-on-year. The optimized rotary table structure can better adapt to the cutting process, which is beneficial to improve the service life of the rotary table and enhance its operation stability. The research results are beneficial to enrich the relevant research theory in the field of rotary table vibration of cantilever roadheader, and are beneficial to improve the service life of the rotary table and the efficiency of tunneling and mining.

Discrete Element Model of Oil Peony Seeds and the Calibration of Its Parameters

Oil peony is an important oil crop that is primarily sown by using artificial methods at present. Its seeder encounters the problems of low efficiency of seeding that significantly limits the highly efficient mechanized production of high-quality peony oil. In this study, Fengdan white oil peony seeds were used as the research object and repose angle as the response value to establish a discrete element model (DEM) and parameter calibration. The range of parameters of oil peony seeds was first obtained through an experiment, and their repose angle was obtained by an inclinometer. A three-dimensional DEM of oil peony seeds was then established. The Plackett–Burman (PB) test was utilized to screen the parameters that had a significant influence on the repose angle, and the steepest ascent (SA) test was applied to determine their optimum range of testing. Following this, based on Box–Behnken (BBD) test results, a second-order regression model between the important parameters and the repose angle was constructed. Finally, the absolute minimum difference between simulated and measured repose angles was utilized as the objective of optimization to obtain the following optimum combination of parameters: The values of the seed–steel collision recovery coefficient (CRC), seed–seed static friction coefficient (SFC), seed–steel SFC, and seed–seed rolling friction coefficient (RFC) were 0.704, 0.324, 0.335, and 0.045, respectively. This optimal combination of parameters was confirmed through simulations, and the error between simulated and measured repose angles was only 0.67%, indicating that the calibrated DEM of oil peony seeds was reliable.

Synthesis, characterization of FeNi3@SiO2@CuS for enhance solar photocatalytic degradation of atrazine herbicides: Application of RSM

This study was focused on the response surface methodology modeling (RSM) for the removal of Atrazine residue (ATZ) using nanocomposite. The FeNi3@SiO2@CuS nanocomposite was fabricated by co-precipitation and sol-gel methodologies, and their properties were studied by XRD, FTIR, VSM, and SEM techniques. The synthesis core-shell composite was used to degrade atrazine herbicide (ATZ) from synthetic wastewater. The efficiency of FeNi3@SiO2@CuS was calculated as a function of different input parameters (pH, ATZ concentration, catalyst concentration, H2O2 concentration, and reaction time by using a central composite design (CCD) adapted from response surface methodology. The CCD created a matrix of 60 runs of the experiment to look into how the five input elements interacted with one another. A reduced quadratic model was created using the findings and demonstrated good predictability of outcomes consistent with the research findings. Variance analysis (ANOVA) indicated that the chosen model was highly significant (p < 0.0001), and the coefficients of determination R2, adjusted R2, and predicted R2 were 0.9587, 0.9513, and 0.9380, respectively, confirming that the second-order regression model had been satisfactorily adjusted to obtained data. Furthermore, the findings provide validation that the degradation rate of ATZ adheres to a pseudo-first-order kinetic model, exhibiting an impressive R2 value surpassing 0.98. Through the use of a numerical optimization technique, a 95.24 % ATZ elimination was represented by the desirability of 81.12 %. This study suggests that sunlight, catalyst, and H2O2 together have a significant influence on ATZ degradation. Additionally, RSM was an appropriate process for optimizing the factors related to the elimination of ATZ by a solar-induced photocatalytic process.

Discrete element modeling and experimental study of biomechanical properties of cotton stalks in machine-harvested film-stalk mixtures

To address the current problems of low accuracy and poor reliability of the discrete element model of cotton stalks, as well as the difficulty of guiding the design and optimization of the equipment through simulations, the discrete element modeling and physical-mechanical tests of cotton stalks in machine harvested film-stalk mixtures are carried out. The peak tensile force Fjmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F_{\\rm j}^{\\max }$$\\end{document}, the peak pressure Fymax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F_{\\rm y}^{\\max }$$\\end{document}, the peak bending force Fwmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F_{\\rm w}^{\\max }$$\\end{document}, the peak shear force Fjmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F_{\\rm j}^{\\max }$$\\end{document}, and the force-displacement (F–x) curves of cotton stalks are obtained from the physical tests. The discrete element model of double-layer cotton stalks based on the flat-joint model is established with the PFC3D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\rm 3D}$$\\end{document} software. The Fymax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F_{\\rm y}^{\\max }$$\\end{document} is taken as the response value, and the microscopic parameters of the cotton stalk model are used as the test factors, then the Plackett–Burman test, the steepest climb test, and the Box–Behnken test are sequentially designed using Design-Expert software. The second-order regression model describing the relationship between the Fymax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F_{\\rm y}^{\\max }$$\\end{document} and the microscopic parameters is established. The optimal parameter combinations of the microscopic parameters are obtained, and then they are utilized to construct the compression, bending, and shear models of cotton stalks and to carry out the validation tests. The results confirm that the established discrete element model could accurately characterize the biomechanical properties of cotton stalks and that the parameter calibration method is reasonable, which could provide a reference for the discrete element modeling of cotton stalks and other stalks, and also offer a theoretical basis for the research of the crushing and separation mechanism of the film-stalk mixtures and the development of the equipment.

Optimization and Modeling of a Dual-Chamber Microbial Fuel Cell (DCMFC) for Industrial Wastewater Treatment: A Box–Behnken Design Approach

Microbial fuel cells (MFCs) have garnered significant attention due to their capacity to generate electricity using renewable and carbon-neutral energy sources such as wastewater. Extensive experimental work and modeling techniques have been employed to dissect these processes and understand their respective impacts on electricity generation. The driving force is to enhance MFC performance for practical applications commercially. Among the various statistical modeling approaches, one particularly robust tool is the Design of Experiments (DoE). It serves to establish the relationships between different variables that influence MFC performance and allows for the optimization of the MFC configuration and operation for scaled-up performances in terms of bioelectricity generation. This study focused on optimizing microbial fuel cells (MFCs) for bioelectricity production using industrial wastewater treatment, employing the Box–Behnken design (BBD) methodology. Through an analysis of response surface models and ANOVA tests, it was found that a combined approach of reduced quadratic, reduced two-factor interaction, and linear models yielded sound results, particularly in voltage yield, COD removal, and current density. Second-order regression models predicted optimal conditions for various parameters, with surface area, temperature, and catholyte dosage identified as critical input variables for optimization. Under these conditions, conducted by the four-factor and three-level Box–Behnken design methodology in a double-chamber MFC unit considering eight output variables—CCV yield, % COD removal, current density, power density, % TSS removal, % CE, and % PO43−—the optimum values were 700 mV, 54.4%, 54.4 mA/m2, 73.7 mW/m2, 99%, 21.2%, and 100%, respectively. At optimum operating conditions, the results revealed a desirability of 76.6% out of a total of 92 iterations. The paper highlights the effectiveness of statistical ANOVA fit-statistics modeling and optimization in enhancing DCMFC performance, recommending its use as a sustainable bioenergy source. Furthermore, validation results supported the above optimization output response findings and confirmed the viability of biorefinery wastewater as an anolyte for scaling up DCMFC bioelectricity generation.

Multi-objective optimization of HS-WEDM for hole cutting in thin-walled CFRP composites using COCOSO and genetic algorithms

This study aimed to enhance the cutting process parameters in molybdenum HS-WEDM of thin-walled carbon fiber composite (CFRP) products. Various hole quality characteristics were examined, including out-of-roundness, delamination factor, hole dimensional deviation, and cutting speed. The study utilized input parameters, including pulse-on duration, pulse-off duration, input current, and CFRP thickness. Optimization techniques, involving a second-order regression model and a genetic algorithm were used to individually optimize each hole characteristic. The combined compromise solution (COCOSO) method was employed to concurrently adjust the hole quality characteristics into a unified metric called the multiple performance criteria index (MPCI), with weight criteria for each response established using an entropy principle. The MPCI was then optimized using a genetic search algorithm. The findings reveal that the entropy, COCOSO, and genetic algorithm approaches efficiently optimize the HS-WEDM process parameters at various CFRP thicknesses to obtain precise hole quality characteristics. The implementation of these methods is expected to significantly improve hole quality, with enhancements of 29.57 % at 0.5 mm, 1.35 % at 1.0 mm, 9.65 % at 1.5 mm, and 6.05 % at 2.0 mm CFRP thicknesses.

Estimation of Surface Soil Nutrient Content in Mountainous Citrus Orchards Based on Hyperspectral Data

Monitoring soil conditions is of great significance for guiding fruit tree production and increasing yields. Achieving a rapid determination of soil physicochemical properties can more efficiently monitor soil conditions. Traditional sampling and survey methods suffer from slow detection speeds, low accuracy, limited coverage, and require a large amount of manpower and resources. In contrast, the use of hyperspectral technology enables the precise and rapid monitoring of soil physicochemical properties, playing an important role in advancing precision agriculture. Yuxi City, Yunnan Province, was selected as the study area; soil samples were collected and analyzed for soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) contents. Additionally, soil spectral reflectance was obtained using a portable spectroradiometer. Hyperspectral characteristic bands for soil nutrients were selected from different spectral preprocessing methods, and different models were used to predict soil nutrient content, identifying the optimal modeling approach. For SOM prediction, the second-order differentiation-multiple stepwise regression (SD-MLSR) model performed exceptionally well, with an R2 value of 0.87 and RMSE of 6.61 g·kg−1. For TN prediction, the logarithm of the reciprocal first derivative-partial least squares regression (LRD-PLSR) model had an R2 of 0.77 and RMSE of 0.37 g·kg−1. For TP prediction, the logarithmic second-order differentiation-multiple stepwise regression (LTSD-MLSR) model had an R2 of 0.69 and RMSE of 0.04 g·kg−1. For AN prediction, the logarithm of the reciprocal second derivative-partial least squares regression (LRSD-PLSR) model had an R2 of 0.83 and RMSE of 24.12 mg·kg−1. The results demonstrate the high accuracy of these models in predicting soil nutrient content.

Sludge deep dewatering by liquefied dimethyl ether: selection of operating conditions based on response surface methodology

ABSTRACT Sludge is an inevitable by-product of the sewage treatment process and its high moisture content poses significant challenges for its treatment and disposal. This study focuses on the technology of sludge deep dewatering using liquefied dimethyl ether (DME) and explores the relationship between operating parameters (DME/sludge ratio, extraction time and stirring speed) and the water content of the sludge after deep dewatering. After deep dewatering, the sludge's lower heating value (LHV) was significantly increased. The dehydrated filtrate is highly biodegradable and could be treated together with sewage. Based on the response surface method of central composite design, a second-order regression model of the above three variables and sludge water content as the response was established. Finally, the operating conditions diagram was drawn by target water content (36.96 wt.%) which meets the requirement of self-sustained incineration and model equation. This study provides a valuable perspective on sludge drying and fuelisation.

Optimizing the performance of TPCB/SCA composite-modified asphalt using improved response surface methodology

Abstract A composite-modified asphalt was developed by incorporating tire pyrolytic carbon black (TPCB) with a silane coupling agent (SCA) to enhance its ductility and storage stability. The functional binding between asphalt and TPCB was improved by the addition of SCA. The gray correlation comprehensive evaluation method is incorporated into the response surface methodology (RSM), leading to an improved RSM. The improved RSM was employed to optimize the content of the modifier and the preparation process to enhance overall performance. A central composite design-based experiment was conducted to optimize the penetration, softening point, 10°C ductility, Brookfield rotational viscosity, and storage stability indicators of modified asphalt. The response surface of the gray comprehensive evaluation value of the evaluation indicators was calculated using the gray correlation analysis method, and a second-order regression model was established between independent variables such as TPCB, SCA content, and shear time factor and the gray comprehensive evaluation value. This model analyzed the impact of a single variable and variable interaction on the performance of the TPCB/SCA composite-modified asphalt. The optimized model results showed that the preparation parameters that optimize the expected comprehensive performance of the composite-modified asphalt are 9.2% TPCB content, 2.1% SCA content, and a shear time of 56.3 min. Finally, the TPCB/SCA composite-modified asphalt prepared with the improved RSM exhibited the highest gray comprehensive evaluation value and the best overall performance, indicating the feasibility of the optimized preparation parameters with the improved RSM.

Fuzzy logic-driven genetic algorithm strategies for ultrasonic welding of heterogeneous metal sheets

There are a lot of problems with the conventional fusion welding process, so ultrasonic welding has been used for about 20 years and has helped a lot of manufacturing industries, including aviation, medicine, and microelectronics. Ultrasonic welding takes less than one second, making it suitable for mass production. Poor weld quality and joint strength are common issues that industries encounter as a result of this process. Actually, the success and quality of the welding are determined by its control parameters. This research examines the impacts of weld time, vibrational amplitude, and weld pressure on the welding of 0.6 mm thick sheets of two different metals, specifically copper and aluminum (AA2024). Responses, including tensile shear stress, weld area, and T-peel stress, are acquired through experiments that follow a full factorial design including four replicas. The highest recorded tensile shear stress was 4.34 MPa, the maximum weld area measured was 63.6 mm2, and the peak T-peel stress reached 1.22 MPa. A second-order non-linear regression model was constructed using all of these data points, which related the responses to the predictors. Due to the importance of quality in the production sector, the process parameters were determined by the combination of genetic algorithm (GA) and fuzzy logic (FL) approaches. The impact of the weld zone temperature on various quality characteristics has been investigated through experiments. It has been noted from the confirmatory test that FL produces superior output outcomes compared to the genetic algorithm, with FL achieving a fuzzy multi-performance index of 0.94 compared to 0.61 for GA. By conducting microstructural analysis, weld quality levels, including “under-weld,” “good weld,” and “over-weld,” were established.

Investigating the adsorption potential of char derived from waste latex for methylene blue removal

This study presents the adsorption of methylene blue (MB) dye using latex char derived from pyrolysis of latex gloves. The adsorption process was investigated systematically using Response Surface Methodology (RSM) with a Central Composite Design (CCD). The effects of four key variables, namely pH, time, temperature, and adsorbent dosage, were studied using a factorial design enriched with center points and axial points. Experimental data were analyzed using a second-order polynomial regression model to construct a response surface model, which elucidated the relationship between the variables and MB removal efficiency. The study found that the char obtained at 800 °C exhibited the highest adsorption capacity due to its increased carbonization, expanded surface area, and diverse pore structure. Analysis of Variance (ANOVA) confirmed the significance of the quadratic model, with remarkable agreement between predicted and experimental outcomes. Diagnostic plots validated the model's reliability, while 3D contour graphs illustrated the combined effects of variables on MB removal efficiency. Optimization using DoE software identified optimal conditions resulting in a 99% removal efficiency, which closely matched experimental results. Additionally, adsorption isotherms revealed that the Freundlich model best described the adsorption behavior, indicating heterogeneous surface adsorption with multilayer adsorption. This comprehensive study provides valuable insights into the adsorption process of MB dye using latex char, with implications for wastewater treatment and environmental remediation.