Quadratic Regression Model Research Articles

Structured porous 17-PH stainless steel layer fabrication through laser powder bed fusion

ABSTRACT This study aimed to fabricate thin, porous layers of 17-4 PH stainless steel with a defined porosity using laser powder bed fusion (LPBF). The central composite design (CCD) approach was utilized to examine the effect of process parameters on porosity. Three different methods were employed to measure the porosity of 17-4 PH stainless steel samples, i.e. theoretical analysis, buoyancy, and X-ray computed tomography (X-CT). A statistical quadratic regression model is generated that correlates with LPBF parameters to forecast porosity with high prediction accuracy. The maximum obtained porosity is 51.25% ± 0.33% with a laser power of 60 W, a scanning speed of 1800 mm/s, and a hatch spacing of 0.115 mm, which resulted in an average pore size of 24.8 ± 0.38 µm. Permeability was also analysed, as the volume energy density decrease ranges from 19.30 to 14.22 J/mm3, the permeability coefficient increases from 1.39 to 9.41 × 10−11 m2. In addition, it is observed that the minimum energy density to fabricate the 17-4 PH SS with the highest porosity and free of defects and fragmentation is 14.2 J/mm3.

Investigating the properties of octane isomers by novel neighborhood product degree-based topological indices

A topological index is a real number calculated from the structure of a chemical compound to describe its topology. The use of molecular descriptors has been increasing in recent years, helping to determine the physicochemical and biological properties of drugs. The main purpose of this article is to investigate the properties of the octane isomers using the theoretical method. To study the structures of octane isomers, we have introduced a new approach called “neighborhood product degree” to calculate all the classical degree-based topological indices. The np-degree approach is applied to approximate eight properties of octane isomers, such as the acentric factor, density, refractive index, critical volume, molar volume, radius of curvature, critical pressure, and LogP. The np-degree-based topological indices are the estimated values of the properties of octane structures, so the linear and quadratic regression models and correlation coefficients are applied to check the validity of the estimated results. The quantitative structure property relation are obtained by using the linear, quadratic, exponential, logarithmic and sinusoidal regression methods with the help of SPSS. Two models are applied to all the compuations and three regression models are applied to the np-degree Randic index. The computation showed that quadratic regression model is suitable for study octane isomers and np-degree based graph invariants. If the values of the correlation coefficient r ⩾ 0.7, p-values ⩽ 0.05, and F-values ⩾ 2.5, then the results are significant. The results of np-degree-based topological indices satisfy all the criteria for being significant, so these newly introduced indices are valid to study octane isomers. The information determined in this article is beneficial for chemists and pharmacists.

Assessment of physical and mechanical properties of concrete with carbon nanotubes pre-dispersed in cement

The expanding field of nanoengineering modification of cementitious materials increasingly supports advances and innovations in science and engineering through nanotechnology. In this context, this research evaluates the physical and mechanical properties of concrete with carbon nanotubes (CNT–concrete), including compressive strength (CS), tensile strength (TS), static (Es) and dynamic modulus of elasticity (Ed), water absorption (WA), porosity (P), bulk density (ρap), weight loss (WL), ultrasonic pulse velocity (UPV), and electrical resistivity (ER). Specifically, this paper investigates the correlations between these properties and proposes quadratic regression models based on CNT contents to estimate them. For this, an extensive experimental campaign was carried out. As a novelty, CNT were pre-dispersed in the cement particles in an isopropanol to produce the concrete, a proven effective process but used only in pastes and mortars so far. Concrete samples were produced with CNT contents of 0 % (as a reference), 0.05 %, and 0.10 % by weight of cement, with 30 cylindrical specimens measuring 100 × 200 mm (diameter × height) being molded for each composition. The results revealed that including 0.05 % CNT led to statistically significant results, such as reductions of up to 12 % in P and 14 % in WA and increases of up to 16 % in CS, 29 % in TS, and 3 % at UPV. At 0.10 % CNT, Ed showed up to 10 % improvements, while WL increased to 28 %. It was also shown that pre-dispersing powdered CNT in cement particles using an isopropanol medium is effective for use in concrete, but dispersing CNT in water is simpler, safer, and potentially more efficient, especially for industrial production. Another important conclusion was the identification of significant correlations between some properties, which varied with the amount of CNT added, suggesting the existence of a CNT saturation point. Furthermore, the regression models identified ideal CNT contents for P, WA, CS, TS, and UPV, ranging between 0.045 % and 0.123 % CNT. This research also points to the need for additional studies to improve the quality of the regression models and thoroughly assess the benefits of incorporating CNT into concrete studies.

Optimization of extraction condition to improve blackthorn (Prunus spinosa L.) polyphenols, anthocyanins and antioxidant activity by natural deep eutectic solvent (NADES) using the simplex lattice mixture design method

In the present study, a statistical tool called the simplex lattice mixture design method was used to create a new formulation of Natural Deep Eutectic Solvent (NADES) that is derived from a combination of three compounds (lactic acid, maltose, and water) to extract phenolic compounds, including the anthocyanin content, as well as their antioxidant activity from blackthorn fruits (Prunus spinosa L.). The optimal condition for the highest compounds and antioxidant activity was obtained by simultaneous optimization of the ternary mixture containing lactic acid: maltose: water (66:16:16 v/v/v), which was the most suitable for simultaneous maximization of maximum total phenolic content (TPC) (134 ± 1.6 mg GAE/g) and antioxidant activity (AA) showing a 141 ± 1.4 mmol/TE g, which were measured using the DPPH assay. The ternary mixture containing lactic acid: maltose: water (33:33:33 v/v/v), which was the most suitable for simultaneous maximization of the total anthocyanin content (TAC) (120.3 ± 1.3 mg C3G/g)/TE g. The ANOVA analysis of the mixture design showed that all three responses (TPC, TAC, and AA) were statistically significant, with determination coefficients of 94.41 %, 94.96 %, and 98.38 %, respectively, and fit the quadratic regression models. A chromatographic analysis of the NADES extract of the blackthorn fruit revealed that phenolics (vanillic acid, chlorogenic acid, caffeic acid, and gallic acid) and anthocyanin (Cyanidin-3-O-glucoside, Cyanidin-3-O-rutinoside, Peonidin-3-O-glucoside and Delphidin-3-O-β-O-glucoside) are the most prevalent bioactive compounds. These results indicate that the simplex lattice mixture design effectively optimized the phenolic and anthocyanin content, thereby preserving their antioxidant activity. This optimization is valuable, as it can potentially enhance the nutritional and functional value of blackthorn fruit extracts that are rich in phenolic compounds and have antioxidant properties. These discoveries could be used in the creation of functional food, dietary supplement, and natural antioxidant for several fields.

The relationship between the resistance characteristics and structural parameters of the elongated filter cartridge in the dust collector

Due to the advantages of a large filtration area, elongated filter cartridges are gradually replacing filter bags in dust collectors. However, the production standards for these filter cartridges lack clarity in determining various parameters. Therefore, this paper combines experimental validation with computational fluid dynamics simulations to analyze the resistance characteristics of elongated filter cartridges, aiming to establish recommended parameter combinations for their optimal design. Single factor analysis and Response Surface Methodology (RSM) are employed for parameter optimization. The study establishes a quadratic regression model to predict resistance based on various filter cartridge parameters. Single factor analysis indicates that the optimal combination of length and pleat number is approximately diagonally distributed. The RSM ranks the factors' impacts on resistance as follows: length (L) > pleat number (N) ≈ pleat length (Pl) > filtering velocity (vf) > outer radius (R). The optimal parameter combination under global optimization is: N = 43, Pl=23 mm, R = 76 mm, L = 2 m. Additionally, the study analyzes the distribution of normal and tangential velocities of airflow on the filter medium surface to understand their impact on filter lifespan. Results indicate that tangential velocity has a significant impact on filter wear, and locations with higher tangential velocity are situated in the bottom region of the fold at the outlet.

Board Independence and Earnings Stability: A Non-linear Perspective from Indian Companies

This research examines the relationship between board independence and earnings stability in Indian companies. The study focuses on the BSE-500 index constituents, which represent 4,044 firm-years from 2011 to 2022, and aims to fill gaps in the literature by providing insights into how various board characteristics affect earnings stability. The study employs a novel non-linear approach that integrates Resource Dependency Theory (RDT) and Stewardship Theory to assess the impact of board independence on earnings stability. The study uses a quadratic regression model to examine the relationship between board independence and earnings stability, with earnings stability measured using the Return on Assets Consistency Ratio (ROACR). The findings indicate an inverted U-shaped relationship between board independence and earnings stability, suggesting there is an optimal level of board independence that maximizes stability in earnings. The study also explores other factors such as asset turnover ratio, institutional ownership, leverage, promoter holdings, and firm size. This research highlights the importance of balanced board composition and provides valuable insights for policymakers and practitioners aiming to optimize corporate governance structures for sustainable firm performance.

Optimization of irrigation and fertilization of apples under magnetoelectric water irrigation in extremely arid areas.

Apple (Malus pumila Mill.) is one of the important economic crops in the arid areas of Xinjiang, China. For a long time, there has been a problem of high consumption but low yield in water and fertilizer management, prevent improvements in apple quality and yield. In this study, 5-year-old 'Royal Gala' apple trees in extremely arid areas of Xinjiang were used as experimental materials to carry out field experiments. considering 5 irrigation levels (W1, 30 mm; W2, 425 mm; W3, 550 mm; W4, 675 mm; W5, 800 mm) and 5 fertilization levels (F1, 280 kg·ha-1; F2, 360 kg·ha-1; F3, 440 kg·ha-1; F4, 520 kg·ha-1; F5, 600 kg·ha-1) under magnetoelectric water irrigation conditions. The results demonstrated that magnetoelectric water combined with the application of 675 mm irrigation amount and 520 kg·ha-1 fertilization amount was the most effective combination. These results occurred by increasing net photosynthetic rate of apple leaves, improved the quality of apples, increased apple yield, and promoted the improvement of water and fertilizer use efficiency. Additionally, the quadratic regression model was used to fit the response process of yield, IWUE and PFP to irrigation amount and fertilization amount, and the accuracy was greater than 0.8, indicating good fitting effects. The synergistic effect of water and fertilizer has a positive effect on optimizing apple water and fertilizer management. Principal component analysis showed that the magnetoelectric treatment combined water and fertilizer mainly affected apple yield, water and fertilizer use efficiency and vitamin C content related to quality. This study provides valuable guidance for improving water and fertilizer productivity, crop yield and quality in extreme arid areas of Xinjiang by using Magnetoelectric water irrigation.

SrCaAl mixed metal oxides derived from LDH precursor as a novel and efficient solid base catalyst for Knoevenagel condensation reaction: Multivariate optimization study

The Knoevenagel condensation reaction is one of the most important reactions in the industry for the synthesis of organic materials, medicinal, and biological compounds. In this work, we have proposed an original and efficient solid-base catalytic system for the Knoevenagel condensation reaction. For the first time, a mixed oxide catalyst derived from hydrotalcite-like Sr0.5Ca2.5Al precursor was synthesized by co-precipitation method and subsequently calcined for 5 h at 850 °C. The catalysts were characterized by various techniques. The Sr0.5Ca2.5Al mixed oxide was also compared with the usual Ca3Al sample in terms of structural characteristics and catalytic activity in the Knoevenagel reaction between active methylene compounds and benzaldehyde. The findings revealed that the Sr0.5Ca2.5Al mixed oxide sample is considerably active in this reaction compared to the Ca3Al sample due to its stronger basicity. The kinetics of the Knoevenagel condensation reaction over the Sr0.5Ca2.5Al catalyst was investigated and indicated that it follows from the second-order model. The stability and reusability of the catalyst sample were studied and it was found that the Sr0.5Ca2.5Al catalyst has high stability and a good lifetime. The effects of reaction time, amount of catalyst, and reaction temperature on benzaldehyde conversion (%) and product selectivity (%) were assessed in this study using the Box-Behnken design. The appropriateness of the quadratic regression model was evaluated through the ANOVA method. Under optimal conditions, the conversion percentage of benzaldehyde and product selectivity reached 97% and 84%, respectively.

Linear and curvilinear association of pain tolerance and social anxiety symptoms among youth with different subgroups of childhood trauma

BackgroundSocial anxiety is a common symptom that occurs after exposure to childhood trauma (CT), and pain tolerance is a protective factor against social anxiety in generic populations with CT. However, few studies have investigated whether and how this association varies across different CT subgroups. Thus, this study aimed to investigate (1) the effects of pain tolerance on social anxiety symptoms among youth with different subgroups of CT; (2) the nonlinear relationship between pain tolerance and social anxiety symptoms among different CT categories. MethodsIn this study, 15,682 college or university students with experiences of CT were identified in a large sample and divided into five CT subgroups. Linear and quadratic regression models were conducted to explore the association between pain tolerance and social anxiety symptoms among youth with different CT subgroups. ResultsThe results of model revealed a linear relationship between pain tolerance and social anxiety symptoms among youth with most CT subgroups. Notably, an inverted U-shaped curve was found between pain tolerance and social anxiety symptoms in youth with emotional abuse. Social anxiety symptoms increased gradually with pain tolerance scores between 0 and 16, and then sharply decreased when scores reached above 16. LimitationsLimited by self-report measurements, the results of this study focused only on perceived pain tolerance and ignored behavioral pain tolerance. ConclusionThese findings highlight the importance of assessing pain tolerance thresholds in youth with emotional abuse and improving pain tolerance to prevent social anxiety symptoms in youth with different subgroups of CT.

Taguchi method and neural network for efficient β‐ketoenamine synthesis in deionized water

AbstractThe optimization of performance parameters, in particular the yield of the synthesis reaction of β‐enaminones in demineralized water, is crucial to improve their efficiency and accuracy. In this report, we investigate the optimization of the synthesis of β‐ketoenamines in deionized water by controlling several parameters such as reaction time, temperature, amine equivalent, acid percentage, and stirring rate. An orthogonal L16 (45) network was created using Taguchi's approach, allowing for the best possible parameters. To forecast the contribution of each parameter, analysis of variance (ANOVA) techniques are also used. Multiple linear and nonlinear regression (MLR, MNLR) and multilayer perception artificial neural network (MLP‐ANN) predictive models were developed. Analysis of the results led to optimized design parameters, with time = 6 h, temperature = 25°C, amine equivalent = 1.5, acid percentage = 20%, and stirring rate = 1000 rpm, leading to a maximum yield of 63%. ANOVA analysis revealed that temperature, stirring rate, acid percentage, and time are the parameters with the greatest influence. The least sensitive parameter is the amine equivalent. The two main interactions are temperature * acid % and amine equivalent * rpm. The MLP‐ANN predictions are in good agreement with the experimental values, resulting in a higher R2 compared to the quadratic regression model and the MLR model. By using molecular docking studies, the produced compounds' biological activity was investigated. Some of the synthesized compounds appear to be interesting and could be used for therapeutic applications. The results of this study give us insight into the gentle, cost‐effective, and biologically active synthesis of β‐enaminones in deionized water.

Fibre optic sensing technology for field assessment of thermal conductivity of aquatic sediments

PurposeThis study explores the in situ variability of sediment thermal conductivity (K) in a pond, integrating field-deployed fibre optic sensing with laboratory analyses of sediment properties to enhance our understanding and management of aquatic systems.Materials and methodsA 20-m cable setup, consisting of a fibre optic cable (FOC) and a heating tape, was buried at two depths within a channel-shaped section of a pond. Induced temperatures along the FOC were recorded during several heating and cooling periods using distributed temperature sensing (DTS). Thermal conductivity (K) was estimated at five locations along the FOC during the heating periods using the heat conduction theory for an infinite line source. Sediment core samples collected from these locations were analyzed to determine dry bulk density (DBD), organic matter content (OM), and particle size distribution (PSD), exploring their effects on K variability.ResultsAnalysis of core samples identified three distinct layers, each with varying PSD, OM, and DBD. The study revealed substantial spatial differences in the thermal conductivity of sediments, even over very short distances along the FOC, attributed to variations in sediment properties. Through a combination of field and laboratory results, we developed quadratic regression models (R2 > 0.9) to characterize the influence of DBD and OM on K. These models enabled detailed vertical and horizontal characterization of K within specific sediment contexts.ConclusionThe study demonstrates the effectiveness of active DTS in detecting in-situ variations in K, emphasizing the impact of OM and DBD on temperature propagation. This study highlights the necessity of considering sediment property variability in modelling heat transfer for accurate water resource management and environmental assessments.

Simplified equations for wet bulb globe temperature estimation in Bangladesh

AbstractThe increasing temperatures and shifts in meteorological conditions have heightened the vulnerability of Bangladesh's densely populated regions, known for their high heat and humidity, to potential health hazards. While the Wet Bulb Globe Temperature (WBGT) is a widely acknowledged and robust measure for evaluating heat stress in various occupational and outdoor environments, its widespread application is impeded by the complexity of calculations, the substantial computational resources required, and the need for specialized expertise, particularly in developing nations. Therefore, this study aimed to develop simplified equations for estimating WBGT in Bangladesh using meteorological variables. This study applied Liljegren's model on high‐resolution reanalysis data of the European Centre for Medium‐Range Weather Forecasts (ERA5) to calculate WBGT from 1979 to 2021. Subsequently, linear and nonlinear regressions were used to derive simplified equations for estimating Liljegren WBGT in Bangladesh. The quadratic regression models offer simplified equations for WBGT estimation. The model with only temperature (Ta) as an input estimates WBGT with an R2 of 0.967 and an RMSE of 0.716, effectively capturing a significant portion of WBGT variability. The inclusion of solar radiation (SR) with Ta improved the performance, with an R2 of 0.996 and an RMSE of 0.242. The best parsimonious model, with an R2 of 0.986 and an RMSE of 0.471, is derived when wind speed is considered with Ta and SR inputs, achieving an R2 of 0.993 and an RMSE of 0.331. Comprehensive graphical and statistical analyses confirm the high accuracy of all models. The three‐input model notably demonstrates exceptional performance, attaining optimal values for critical metrics, including extreme WBGT. The results affirm the practical suitability of the derived models for accurate estimations of WBGT in Bangladesh. These equations provide simplified tools for assessing heat stress conditions, contributing to public health initiatives, occupational safety guidelines, and climate change adaptation strategies.

NEW MODELS FOR ESTIMATION OF DIFFUSE SOLAR RADIATION USING METEOROLOGICAL PARAMETERS FOR BENIN, NIGERIA

In this comprehensive study, an extensive 22-year dataset (2001-2022) for Benin (Latitude 6.32 oN, Longitude 5.10 oE and 77.80 m above sea level) were obtained from the National Aeronautic Space Administration (NASA) website. The datasets comprises of the monthly average daily global solar radiation, diffuse solar radiation, relative humidity, atmospheric pressure, wind speed, and mean temperature, was utilized to develop 19 new models for estimating diffuse solar radiation. These models were categorized into five distinct groups: modified Page, Liu and Jordan models; clearness index and one-variable models; two-variable models; three-variable models, and a four-variable model. These models were statistically evaluated using a set of five validation indices—Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, and the coefficient of determination (R²). The study identified the most effective models in each category. Equation 28b from the modified Page, Liu and Jordan category, Equation 28f from the clearness index and one-variable models, Equation 28j from the two-variable models, and Equation 28o from the three-variable models category were found to be the best-performing models. A comparative assessment of these performed models revealed that the quadratic regression model (Equation 28b) stood out as the most suitable for accurately estimating diffuse solar radiation in Benin. This implies that the developed model equation 28b can be used to estimate the diffuse solar radiation for Benin and locations with similar climatic conditions

The analysis of response surface optimization and performance of cross‐linked starch/tannic acid adhesive reinforced by phragmites fibers

AbstractThe present study utilizes starch as the primary raw material for preparing a water‐resistant biomass adhesive to replace formaldehyde‐based resin adhesive, which is subsequently used in plywood production. The preparation process of starch‐based adhesive (SBA) was optimized using response surface methodology (RSM), and the effects of the mass ratio of starch to tannic acid, the dosages of phragmites fiber and boric acid on wet‐bond strength were studied. The quadratic regression model of SBA showed significant results (p < 0.0001). The optimal preparation condition was that the mass ratio between starch and tannic acid was 5:2, in the dosages of 2.9% for phragmites fibers and 3.2% for boric acid. The wet‐bond strength of the plywood prepared with this adhesive was 1.73 MPa. Characterization analysis showed that the hydroxyl group on the surface of starch and tannic acid had complexation with boric acid, which improved the wet‐bond strength, water resistance, and, thermal stability of the adhesive. The alkali‐treated phragmites fibers had better compatibility with the starch matrix than the original fiber, which improved the cohesion of the SBA and increased the wet‐bond strength by 26%.Highlights The phragmites fibers have been used to reinforce starch‐based adhesives. Boric acid facilitates the formation of crosslinked structures. The phenolic hydroxyl group in tannins provides excellent water resistance. Phragmites fiber exhibits good compatibility with the adhesive matrix. The cured starch‐based adhesive has excellent wet‐bond strength.

Study on silage mixing device of King Grass stalk particles based on DEM simulation and bench test

This study designed a silage mixing device for King Grass stalk particles and optimized the core component, the blender, based on the discrete element method (DEM). Through simulation tests, the effect of different blender forms, blade lengths and blade orientations on the Lacey mixing index (LMI) was studied, and the structural form of the blender was selected as the inclined blade-same-up impeller blender. Based on simulation tests, to verify the reliability of the simulation test and further determine the optimal blade inclination angle and mixing speed of the blender, a mixing experimental bench was built for bench tests. The LMI was compared between simulation and bench tests. At three speeds of 60, 80 and 100 r min- −1, the average relative errors are 2.3%, 2.1% and 1.5%, indicating that the simulation test can simulate actual tests well. The central composite design (CCD) was used to establish a binary quadratic regression model of the effects of blade inclination angle and mixing speed on LMI and mixing energy consumption. Using the NSGA-II algorithm, it was determined that the best inclination angle of the mixing blade is 42° and the best mixing speed is 78 r min−1.

A Box-Behnken design-based chemometric approach to optimize the sono-photodegradation of hydroxychloroquine in water media using the Fe(0)/S2O82-/UV system.

The huge utilization of hydroxychloroquine in autoimmune infections led to an abnormal increment in its concentration in wastewater, which can pose a real risk to the environment, necessitating the development of a pretreatment technique. To do this, we are interested in researching how hydroxychloroquine degrades in contaminated water. The main goal of this investigation is to optimize the operating conditions for the sono-photodegradation of hydroxychloroquine in water using an ultrasound-assisted Fe(0)/ /UV system. To get adequate removal of HCQ, a chemometric method based on the Box-Behnken design was applied to optimize the influence of the empirical parameters selected, including Fe(0) dose, concentration, pH, and initial HCQ concentration. The quadratic regression model representing the HCQ removal rate (η(%)) was evolved and validated by ANOVA. The optimal conditions as a result of the above-mentioned trade-off between the four input variables, with η(%) as the dependent output variable, were captured using RSM methodology and the composite desirability function approach. For HCQ full decomposition, the optimal values of the operating factors are as follows: dose, 194.309 mg/L; Fe(0) quantity, 198.83 mg/L; pH = 2.017, and HCQ initial dose of 296.406 mg/L. Under these conditions, the HCQ removal rate, achieved after 60 min of reaction, attained 98.95%.

COVID-19 impact on life expectancy in Mexico. An analysis based on the Global Burden of Disease 2021 study.

Between 2020 and 2021, Mexico documented 2.21 million fatalities, out of which 25.3% were attributable to SARS-COV-2 infection. To evaluate COVID-19 mortality during 2020-2021, determine its impact on national- and state-level life expectancy at birth, and in a group of selected countries of the region, as well as to analyze it according to sociodemographic profiles. Data from the Global Burden of Disease 2021 study were used to report mortality, the impact on life expectancy and underlying causes between 2019 and 2021. These data were evaluated from the perspective of response to the pandemic and according to the sociodemographic structure based on a quadratic regression model. Between 2020 and 2021, 708,971 excess deaths were recorded, which decreased life expectancy at birth by 4.6 years; 76% of this reduction was attributed to COVID-19. The COVID-19 mortality rate was higher than expected according to the sociodemographic conditions of the states. In Mexico and the countries of the region, the pandemic was devastating and generated regressions in life expectancy at birth, which varied from two to nine years. It is not clear why the effect was so different between countries and within Mexico.

An Optimization Study of Advanced Fenton Oxidation Methods (UV/Fenton–MW/Fenton) for Treatment of Real Epoxy Paint Wastewater

The use of various advanced oxidation methods in the treatment of wastewater has become the subject of many studies published in recent years. In particular, it is exceedingly significant to compare these treatment methods for industrial wastewater to reduce environmental effects and optimize plant operations and economics. The present study is the first to deal with the treatability of real epoxy paint wastewater (EPW) using MW- and UV-assisted Fenton processes within an optimization framework. A three-factor, three-level Box–Behnken experimental design combined with response surface methodology (RSM) was conducted for maximizing the chemical oxygen demand (COD) and color removal efficiencies of ultraviolet (UV)/Fenton and microwave (MW)/Fenton processes in the treatment of the real epoxy paint wastewater (EPW, initial COD = 4600 ± 90 mg/L, initial color = 114 ± 4 Pt-Co), based on 15 different experimental runs. Three independent variables (reaction time ranging from 20 to 60 min (UV) and from 5 to 15 min (MW), power ranging from 20 to 40 W (UV) and from 300 to 600 W (MW), and H2O2/Fe2+ ratio ranging from 0.2 to 0.6 (for both UV and MW)) were consecutively coded as A, B, and C at three levels (−1, 0, and 1), and four second-order polynomial regression equations were then derived to estimate the responses (COD and color removals) of two distinct systems. The significance of the independent model components and their interrelations were appraised by means of a variance analysis with 99% confidence limits (α = 0.01). The standardized differences of the independent variables and the consistency between the actual and predicted values were also investigated by preparing normal probability residual plots and experiment-model plots for all processes. The optimal operating conditions were attained by solving the quadratic regression models and analyzing the surface and contour plots. UV/Fenton and MW/Fenton processes, which constitute combined Fenton processes, were performed using advanced oxidation methods, while Fenton processes were utilized as the standard method for wastewater treatment. When UV/Fenton and MW/Fenton processes were applied separately, the COD removal efficiencies were determined to be 96.4% and 95.3%, respectively. For the color parameter, the removal efficiencies after the application of both processes were found to exceed 97.5%. While these efficiencies were achieved in 1 h with a 38 W UV unit, they were achieved in 15 min with a MW power of 570 W. According to the RSM-based regression analysis results, the R2 values for both processes were greater than 0.97 and p values were less than 0.003.

Numerical and statistical analysis of unsteady hydromagnetic hybrid nanofluid flow in a porous medium over an inclined disk with dissipative heat transfer

AbstractThe present analysis deals with the three‐dimensional radiative, convective hybrid nanofluid flow over a rotating and stretching inclined disk under the action of the applied magnetic field, Joule's heating, and viscous dissipation effects. The mathematical model considers nanoparticles graphene oxide (GO) and molybdenum disulfide (MoS) suspended in water as the base fluid within a Darcy medium. The cylindrical coordinate system is used to express continuity, momentum, and energy partial differential equations, out of which momentum and energy partial differential equations are transformed into ordinary differential equations using a suitable transformation method under the boundary‐layer approximation. The transformed equations are further solved using the spectral quasi‐linearization method. Graphical and numerical data are presented to investigate the behavior of velocity and temperature under various parameters and shape factors. The nature of fluid flow at the boundary wall is determined by tables of skin friction and the Nusselt number. Statistical analysis of the parameters for axial and tangential skin friction and the Nusselt number is performed using the quadratic regression model.

Fretting wear characteristics of Ti6Al4V/TiC nano composites using response surface method

This study explores the impact of titanium carbide (TiC) nanoparticles on the microstructure and fretting wear performance of Ti64-GV alloys. The Ti64 alloys, featuring varying TiC concentrations, were fabricated through spark plasma sintering (SPS) process. Fretting experiments followed a face-centered central composite design methodology, employing a second-order quadratic regression model to analyze wear volume loss and coefficient of friction (CoF). The findings revealed a notable reduction in wear volume loss and friction coefficient with increasing TiC content. The presence of TiC significantly enhanced the wear resistance of Nano-composites due to heightened hardness and oxidation wear resistance. Employing hybrid desirability-based response surface methodology (RSM) led to substantial improvements in wear characteristics (37% in CoF and 46% in wear volume loss). Microstructural analysis affirmed enhanced surface quality and textures using the RSM hybrid approach for predicting optimal process parameters.