Composite Desirability Research Articles

Optimizing ironing parameters in material extrusion (MEX) technology: enhancing efficiency and performance

This study explores the optimization of ironing parameters in Material Extrusion (MEX) technology, specifically targeting improvements in the mechanical properties of polylactic acid (PLA) printed objects, with a focus on enhancing Ultimate Tensile Strength (UTS) and minimizing printing time. To achieve this, we employed a systematic experimental approach that varied flow rate, path speed, and spacing between passes, followed by a comprehensive statistical analysis using Analysis of Variance (ANOVA). Our findings indicate that path speed and spacing between passes significantly influence both UTS and printing time, while flow rate has a minor effect within the tested range. Optimal parameters were determined to be a flow rate of 14.34%, path speed of 30 mm/s, and spacing between passes of 0.3 mm, achieving a composite desirability of 0.970537. Validation using the response optimizer feature in Minitab software confirmed a strong correlation between predicted and experimental results. This research provides practical insights for optimizing MEX parameters, contributing to enhanced mechanical properties and efficiency in additive manufacturing processes.

Hybrid CNC–MXene Nanolubricant for Tribological Application: Characterization, Prediction, and Optimization of Thermophysical Properties Evaluation

In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of CNC–MXene nanoparticles (ranging from 0.01 to 0.05 wt%) and characterized using Fourier-Transform Infrared Spectroscopy and TGA (Thermogravimetric Analysis). Response surface methodology (RSM) was used in conjunction with the miscellaneous design model to identify prediction models for the thermophysical properties of the hybrid CNC–MXene nanolubricant. Minitab 18 statistical analysis software and Response Surface Methodology (RSM) based on Central Composite Design (CCD) were utilized to generate an empirical mathematical model investigating the effect of concentration and temperature. The analysis of variance (ANOVA) results indicated significant contributions from the type of nanolubricant (p < 0.001) and the quadratic effect of temperature (p < 0.001), highlighting non-linear interactions that affect viscosity and thermal conductivity. The findings showed that the predicted values closely matched the experimental results, with a percentage of absolute error below 9%, confirming the reliability of the optimization models. Additionally, the models could predict more than 85% of the nanolubricant output variations, indicating high model accuracy. The optimization analysis identified optimal conditions for maximizing both dynamic viscosity and thermal conductivity. The predicted optimal values (17.0685 for dynamic viscosity and 0.3317 for thermal conductivity) were achieved at 30 °C and a 0.01% concentration, with a composite desirability of 1. The findings of the percentage of absolute error (POAE) reveal that the model can precisely predict the optimum experimental parameters. This study contributes to the growing field of advanced nanolubricants by providing insights into the synergistic effects of CNC and MXene in enhancing thermophysical properties. The developed models and optimization techniques offer valuable tools for tailoring nanolubricant formulations to specific tribological applications, potentially leading to improved efficiency and durability in various industrial settings.

AISI D2 steel machining and manufacturing process optimization for tooling applications in biomedical industry

Tool steels such as AISI D2 are famous in the manufacturing industry because of their engineering applications. The precise interplay of improved hardness and toughness makes the machining of complex geometries challenging through conventional machining options. Therefore, non-conventional processes such as wire electric discharge machining (WEDM) are preferred because of their simultaneous machining and surface modification actions. To investigate the complex process parameters and their sensitivity, material removal rate (MRR) and cutting surface roughness (SR) are the corresponding performance measure characteristics for WEDM machining on AISI D2 tool steel. The L18 mixed-level Taguchi technique has been used for obtaining combinations of experiments on two levels of thickness and three levels of other remaining factors (21 × 33). Analysis of variance (ANOVA) and signal-to-noise ratio have been applied to measure the magnitude of effects on each control factor, to investigate the optimum levels of input process parameters on machining characteristics, and to identify their significance. ANOVA analysis revealed that, for both responses, all main effect variables are highly significant, with p-values equal to zero. Moreover, the coefficient of determination (R2) value in the ANOVA findings for both responses is above 97%, indicating the high reliability of the model. In addition, the composite desirability (dG) is considered to maximize MRR and minimize the SR during WEDM of D2; the better combination of optimum levels of machining parameters (T = 25.4 mm, Pon = 4 µs, SV = 95 V, and WT = 5 kg-f) has a dG of 0.5614.

Electrical discharge machining of super alloy incoloy 925: a study based on box behnken design and response surface methodology

Incoloy 925, a Nikel-based superalloy, exhibits low machinability with conventional machining techniques due to its inhomogeneous properties. Therefore, there is a need to establish a non-conventional method to efficiently machine this alloy. This work is a novel attempt to present the electric discharge machining (EDM) of the superalloy Incoloy 925 and subsequent multi-response optimization. The model for the analysis was designed using the Box Behnken Design (BBD) technique, and the Response Surface Methodology (RSM) was used for the optimization of the results. The machining was performed using a cylindrical copper tool of 11 mm diameter. The effect of pulse-on time (Ton), current, and pulse-off time (Toff) on the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) was investigated. The results from variance analysis confirmed the significance of all the three input factors. The investigation revealed that the maximum MRR (99.2154 mm3/min) was obtained at a pulse-on time of 90 μs, pulse-off time of 5 μs, and current of 30 A. The minimum TWR (0.8866 mm3/min) were achieved at Ton = 60 μs, Toff = 8 μs and current = 10 A. The microscopic images of the machined surfaces revealed very few micro-voids and globules and no cracks, resulting in a fine surface finish of 1.2436 μm, achieved through optimal discharge energy transfer and copper electrodes. The optimal values MRR, TWR, and SR according to composite desirability function are 99.1524 mm3/min, 1.0915 mm3/min, and 1.3925 μm. The experimental results were accurately predicted using RSM and an artificial neural network (ANN), with the ANN showing a predicted correlation coefficient (R) close to 1 indicating high accuracy of the model.

Effect of Honeycomb Geometrical Parameters on Equivalent Radiated Power and Frequency Response of Motor Casing and Gearbox Surface of a Powertrain

ABSTRACTThe powertrain, as a central source of noise and vibration, is crucial in determining the overall NVH (noise, vibration and harshness) performance of vehicles, necessitating the optimisation of its structural components for improved durability and passenger comfort. This paper investigates the influence of key geometrical parameters—cell thickness, skin thickness and cell length—on the complex frequency modes of honeycomb and square sandwich structures using the Altair OptiStruct solver 2022 and fast Fourier transform analyser. The driven and non‐driven ends of a motor casing and a gearbox, represented by honeycomb structures and ribs, were subjected to an evaluation of equivalent radiated power (ERP). The results show that the square structure performs better at higher skin thickness when resisting severe lateral stresses than the honeycomb while being less stiff at higher cell thickness. Notably, smaller cell length had a substantial impact on the modes of the honeycomb structure, whereas larger cell length had an impact on the square structure. Response surface methodology was used to optimise the modal frequencies of both square and honeycomb panels simultaneously. An ideal cell length of 5.88 mm, skin thickness of 1.303 mm and cell thickness of 0.381 mm were found for the honeycomb construction, yielding a composite desirability of 0.981. On the other hand, with a cell length of 5.045 mm, skin thickness of 1.484 mm and cell thickness of 0.331 mm, the square structure achieved a composite desirability of 0.989. It is interesting to note that the driven end casing of the motor casing made more noise with the addition of ribs than the non‐driven end and gearbox. But compared to the original powertrain model, adding a honeycomb structure resulted in a noise reduction of about 10%.

Immersion cooling of a cylindrical battery module: An optimum configuration using CFD, NSGA-II and desirability function approach

Immersion cooling offers promising advantages for cylindrical battery modules, particularly in applications where compact design, efficient thermal management, and enhanced safety are critical factors. In this study, the geometric, thermal, and dynamic parameters of an immersion-cooled battery module with 14 NCM cylindrical cells are analyzed using CFD methods. The developed battery model is experimentally validated, establishing the constraints through numerical analysis of the impact of design parameters on response values. The design parameters are optimized using a multi-objective optimization technique. The study models the battery module with mineral oil cooling and examines the impact of each parameter on variables such as maximum battery module temperature (Tmax), pressure drop (ΔP), maximum temperature variance (ΔTmax), and battery module mass index (Im). A Design of Experiment set, created with Central Composite Design (CCD), is solved using CFD methods, and quadratic correlation equations are derived using backward regression. The regression models for Tmax, ΔTmax, ΔP, and Im show high accuracy with R2 values close to 1.0. Optimization is performed using the entropy weight-based composite desirability function approach, yielding optimum values for cell center spacing (t), coolant mass flow rate (ṁc), and coolant inlet temperature (Ti) at 20.606 mm, 1.5 kg/s, and 23 °C, respectively. The optimized design sets are validated through CFD analysis, showing a maximum deviation of 4.27 %. Reducing t boosts thermal performance but lowers hydraulic performance, while the reverse occurs for ṁc. As Ti decreases, effective cooling improves, but temperature uniformity suffers.

Optimization of microwave-produced coal-derived char insulation foam using response surface methodology

A new char-based insulation foam (CIF) is developed using coal-derived pyrolysis char (PC) and lignin, a paper and pulp industry byproduct. In this study, CIF is prepared by adopting the microwave method and Response Surface Methodology (RSM) is utilized to optimize predictions about the properties such as thermal conductivity, foam texture, compressive strength, and pore distribution of CIF. The three independent variables including percent char content, lignin/char ratio, and foaming agent/polyol ratio are considered in the RSM methodology. The face-centered central composite design is selected to obtain 17 test runs. Optimization of the predicted responses is performed to find the recipe considering a composite desirability function. Confirmatory test results show agreement between the predicted and observed values, with the percent error ranging from 0.00 % to 11.35 %, thereby confirming the accuracy of the prediction equations. The optimized CIF exhibits a thermal conductivity of 0.0633 W/m.K and a compressive strength of 695 kPa making it comparable to or better than traditional building insulation products such as fiber insulation, polymer composite, particle board, and ceramic glass foam. Therefore, this new CIF has the potential to be used as an energy-saving insulation material for building envelopes.

OPTIMIZATION OF ANNEALING AND 3D PRINTING PROCESS PARAMETERS OF PLA PARTS

Fused Filament Fabrication (FFF) has gained significant popularity as the prevalent additive manufacturing technique due to its ability to reduce production time and expenses. However, the constraints of limited dimensional precision, poor surface quality, and relatively low Ultimate Tensile Strength (UTS) hinder compliance with the stringent regulatory norms of conventional manufacturing, necessitating post-processing for enhancement. In this investigation, the response surface method was used to optimize annealing and certain printing parameters in order to enhance the quality of PLA parts produced by FFF. The optimization technique aimed to enhance the UTS and match the CAD dimensions while also minimizing surface roughness. The RSM optimization analysis identified the optimal parameters as: layer height of 0.1 mm, build orientation at 0 degrees, annealing temperature of 110 degrees, and annealing time of 180 dk mm. The proposed models are substantiated by the consistent achievement of high levels of agreement between estimated and experimental response values. A composite desirability value of 0.80 was derived for the variables as a consequence of the optimization investigation.

Investigation on varied sewage sludge producer gas and methane blend equivalence ratios operated SI engine intact with Miller cycle strategy

Decentralised power generation through a gasifier-engine genset can reduce both reliance on power grid and pollution. However, the low calorific value of the produced gaseous fuel moderates engine power and efficiency, which is a big challenge for its utilisation. For early and cost-effective solution prediction, a simulation tool of adequate precision can effectively envisage dual-fuel (DF) mode engine performance. Thus, this study simulates DF-SI engines with late inlet valve closures (LIVCs) to improve thermal efficiency using the Miller cycle strategy. Simulation is performed using quasi-dimensional thermodynamic modelling (QDTM). After validating the model using referred experimental results, QDTM was employed to study the parametric impacts of inputs (blends, LIVC and equivalence ratio) variations on performance and emissions response parameters. Power improves by blending sewage sludge-based producer gas with methane. Multi-objective optimisation through the response surface method (RSM) determined the optimum operating parameters as 0.814 equivalence ratio, 68.23% SSPG-blend and 55.9° CA (ABDC) LIVC. Corresponding optimal responses were 5.906 bar IMEP, 3.85 kW BP, 28.6% BTE, 12.596 MJ/kWh BSEC, 0.134 V% CO and 2045.29 ppm NO emissions. ANOVA-based optimal results presented a composite desirability of 0.843 with a 95% confidence level. Conclusively, methane mix and LIVC approach increase the power and efficiency of the DF-SI engine; additionally, optimisation guides to balance power and emission trade-off.

A novel, intelligent and computer-assisted electrochemical sensor for extraction and simultaneous determination of patulin and citrinin in apple and pear fruit samples

In this work, a novel electrochemical sensor was fabricated for simultaneous determination of patulin (PT) and citrinin (CT) in apple and pear fruit samples. A glassy carbon electrode (GCE) was modified with graphene-multiwalled carbon nanotubes-ionic liquid (Gr-MWCNTs-IL) which was used as a platform to electrochemical synthesis of molecularly imprinted polymers (MIPs) by using PT and CT as templates, maleic acid as a functional monomer, and ethylene glycol dimethacrylate as a cross linker with the aim of preconcentration and simultaneous determination of the PT and CT. Experimental variables affecting fabrication of the structure of the sensor and hydrodynamic differential pulse voltammetric (HDPV) response of the sensor were optimized by a small central composite design and desirability function. After optimization, the HDPV responses of the sensor were calibrated by multivariate calibration methods in the ranges of 0.5–13 fM and 1.5–18 fM for PT and CT, respectively, with the help of PLS-1, RBF-PLS, rPLS, LS-SVM, and RBF-ANN with the aim of selecting the best algorithm to assist the sensor. Our results confirmed the best performance was observed from RBF-ANN which was used for the analysis of apple and pear fruit samples. Limit of detections of the sensor assisted by RBF-ANN for determination of PT and CT were 0.08 and 0.61 fM, respectively. Several commercial brands were analyzed by the use of sensor assisted by RBF-ANN and HPLC-UV, and the results confirmed performance of the sensor was admirable and comparable with the reference method with lower cost, faster response, and easier procedure which made it to be a reliable alternative method for simultaneous determination of PT and CT in real matrices.

An effective parametric sensitivity analysis to improve electrical and thermal energy/exergy efficiency of PVT system using nanofluid

A 3D steady-state model of a photovoltaic/Thermal system with nanofluid is studied to investigate the electrical and thermal performance of PVT by utilizing a combination of statistical modeling and numerical simulation. The 3D CAD (Computer Aided Design) modeling of the photovoltaic thermal (PV/T) is performed using Ansys Space Claim. The current study is based on using a 2-way coupling FSI approach for modeling nanofluid-based PVT systems. Five output responses electrical power, thermal power, electrical exergy, thermal exergy, and entropy generation are chosen against four input parameters, solar radiation (G_sun), ambient temperature (Tamb), mass flow rate (ṁ), and nanoparticles concentration % (φ) to analyze system performance. A statistical prediction model is developed using response surface methodology (RSM) that starts from the design of experiment (DOE). The predicted outcomes obtained from the statistical model and the numerical simulation of the heat transfer model exhibit the existence of good fitness. Furthermore, optimization is performed using RSM for various optimization objectives to suggest the ideal design parameter values for optimal system performance. Sensitivity analysis illustrates that a rise in ambient temperature causes a drop in electrical power production and leads to an increase in thermal power. However, the increase in solar radiation boosts electrical power and causes a drop in thermal exergy, entropy generation increases with an increase in solar irradiation. Similarly, an increase in mass flow rate leads to an increase in thermal power. Multi-objective optimization with the highest composite desirability values depicts the influence of input design parameters and has a favorable effect on the objective of concurrently maximizing electrical and thermal power.

Ultrasonic assisted extraction of water-soluble vitamins from minor components of bee pollen with deep eutectic solvents (DESs) as green solvent

In this study, different deep eutectic solvents (DESs) were used for vitamin extraction from bee pollen. The DES combination with the highest total amount of individual vitamins was choline chloride: ethylene glycol (molar ratio 1:2). The response surface method was then used to optimize the extraction conditions. The independent variables used for optimization were ultrasonic power, time, and temperature, while the dependent variables were the total individual amino acids, total individual phenolic compounds, and total individual vitamin contents. Spectrophotometric analyses and antimicrobial activity were also determined in the extracts. In addition, AGREEprep, ComplexGAPI and BAGI tools were used to evaluate the greenness of the method. The results obtained with DES were compared with water, ethanol, and methanol extracts, which were considered control groups. As a result of optimization, the optimum conditions were found to be 75 % for ultrasonic power, 71.5 min for sonication time, and 40 °C for ultrasonic bath temperature. The model’s composite desirability value (d) was found to be 0.81. The predicted value and the experimental values were close to each other. Furthermore, the most dominant vitamin in DES extracts was ascorbic acid, the most dominant amino acid was proline, and the most dominant phenolic compound was myricetin. The amounts of these compounds were higher than those obtained from water, ethanol, and methanol extracts. The spectrophotometric analysis results obtained higher yields with DES compared to the control groups. Antimicrobial activity analysis showed that pollen extracts prepared using choline chloride: ethylene glycol had a weak inhibitory effect for Gram-positive and Gram-negative bacteria but not yeast-like fungal strains. In conclusion, DES has been successfully used to extract vitamins and other bioactive compounds from bee pollen. The results obtained showed that DESs can be an alternative to organic solvents for the extraction of vitamins, which are among the minor components of bee pollen. Besides, the potential of the application different DESs for more efficient extraction of vitamins and components with important pharmaceutical properties from bee pollen needs to be evaluated in detail.

Optimizing bimetallic-insect exuviate melanin composite cathodes using response surface methodology to enhance the H2O2 yield and selectivity of the electro-Fenton process

Electro-Fenton is a fast and efficient oxidative degradation technology for treating many refractory organic substances. However, the poor electrochemical performance and power generation capacity of the cathode during electro-Fenton treatment limit H2O2 generation and pollutant removal. A FeCu bimetal–melanin cathode catalyst with high H2O2 production, low Fe3+ formation, and low resistance was optimized using response surface methodology. The composite desirability coefficient was as high as 0.9281, indicating that the two-factor setting conditions yielded the optimal response results. The synergistic effect between Fe and Cu and the ionic conductivity of melanin promote the conversion between Fe2+ and Fe3+ and improve the charge transfer ability of the cathode. The optimized Fe–Cu–Me cathodic catalyst had the highest H2O2 generation (217.91 ± 4.65 mg/L) and the lowest resistance value (190.33 Ω), 4.88 times and 5.05 % that of the unmodified cathode, respectively; also the lowest Fe3+ generation was 32 % and 73 % that of the Fe and FeCu cathodic catalysts, respectively. Moreover, the optimized Fe–Cu–Me cathode catalyst exhibited up to 80 % selectivity for 2-electron oxygen reduction reaction-catalyzed H2O2 generation based and excellent O2 and H2O2 adsorption performance on ring disk electrode analysis and density function theory calculation. This study is the first to use FeCu bimetals combined with insect exuviate melanin as a cathode catalyst for the in situ generation of cathodic H2O2. The high performance and stability of the fabricated electrocatalyst can be used in the subsequent treatment of wastewater through the electro-Fenton process, providing an innovative approach to improve the performance of the cathode.

Performance analysis of methanol fuelled SI engine with boosted intake pressure and LIVC: A thermodynamic simulation and optimization approach

Methanol (CH3OH) is a low-carbon alternative fuel that can be derived through renewable power-to-fuel conversion, offering lower emissions and a higher resistance to knock in SI engines. However, the sole methanol-powered SI engine delivers less brake power. Fuels with a higher calorific value have been blended to solve this problem. However, this may cause overburden with storage, handling, and correct blending (like when methane and hydrogen are blended). To resolve this, this research suggested a novel approach for increasing the power and efficiency of a SI engine using the LIVC (late inlet valve closing) miller cycle and boosted intake pressure under 14 geometrical compression ratio (GCR) with the addition of the start of spark timing (SOI) and equivalency ratio (λ) effects. A quasi-dimensional thermodynamic model (QTDM) was applied to simulate the engine performance with respect to operating variables of 0.6–1.0 λ, 35.50 ––650 aBDC (after Bottom Dead Centre) LIVC timing, 30.00 ––150 bTDC (before Top Dead Centre) SOI Timing and 0.8–1.5 bar intake pressure. After that, design of experiments (DoE) based response surface methodology (RSM) was applied to determine optimum operating setting to maximize power and efficiency and minimize emission and fuel consumption. The results from the RSM illustrate the optimum input parameters to be 0.793 equivalence ratio, 46.900 aBDC-LIVC, 20.750 bTDC-SOI timing, and 1.5 bar intake pressure. Correspondingly, response output performances were found to be 12.36 kW-indicated power (IP), 11.20 kW- brake power (BP), 43.32 % indicated thermal efficiency (ITE), 39.32 % brake thermal efficiency (BTE), 9150.6 kJ/kWh brake specific energy consumption (BSEC), and 0.3 V%- carbon mono oxide (CO), 1247.8 ppm-nitric oxide (NO) emission at exhaust valve opening (EVO). The ANOVA regression models resulted in 95 % accuracy in forecasting output response and composite desirability of optimization of 0.83. Overall, it was found that miller-based LIVC and boosted intake pressure considerably improve the performance of SI engines, and the results projected would be helpful for further study and industry application.

Removal of an organophosphate insecticide from aqueous media using phyllosilicate clay: multivariable optimization, nonlinear kinetic modelling and thermodynamic study

The excessive and irrational use of organophosphate insecticides in our lives has resulted in a serious environmental problem, necessitating the proposal of novel and cost-effective plans to remove these organophosphate insecticides from agricultural or industrial wastewater. In this approach, we investigated the use of phyllosilicate clay as a low-cost and environmentally friendly adsorbent to eliminate an organophosphate insecticide (Diazinon DZN) from an aqueous medium. To achieve an adequate elimination of this insecticide from contaminated water, three main parameters including clay dose ( D c ) (0.1–1.5 g L−1), medium pH (5–9), and initial DZN concentrations ( C DZN ) (55–40 mg L−1) were optimized using a multivariable optimization based on Box-Behnken Design (BBD). The response surface method (RSM) and composite desirability function technique were used to find the ideal conditions for the three input variables and the adsorption amount ( Q e ) was selected as the output variable. The higher adsorption amount ( Q max ) was achieved with an adsorbent amount of 1.5 g L−1 in an acidic medium (pH = 5) at room temperature and a Diazinon initial dose of 25.85 mg L−1, with a maximum adsorbent loading of 12.20 mg g−1. The nonlinear modeling study showed that the adsorption kinetics of Diazinon follows the pseudo-second-order model. The thermodynamic study shows that the DZN removal process is exothermic, spontaneous, and of a stable configuration.

Multi-objective optimization of glass/carbon hybrid composites for small wind turbine blades using extreme mixture design response surface methodology

Small wind turbines (SWTs) are a prominent renewable energy technology for decentralized power generation. Blade material and its profile are vital parameters for the aerodynamic performance of SWTs. Traditionally E-glass fiber-reinforced composites (FRCs) are the widely accepted material for developing SWT blades. However, its application is limited by moderate tensile and fatigue properties. Alternatively, other FRC materials such as carbon, basalt and natural fiber composites are proposed as future materials for SWT blades. However, individual materials are observed to satisfy the requirements partially. Therefore, the hybridization of these materials, particularly Glass/Carbon composites is foreseen as a prospective solution for developing cost-competitive and high-strength SWT blades. There are various studies performed to obtain optimized glass/carbon hybrid composites. However, overall material properties required for SWT blades such as low cost, lightweight, moderate flexural strength and higher tensile and fatigue strengths have not been considered simultaneously during the optimization process. This work presents multi-objective optimization of Glass/Carbon hybrid composites using extreme mixture design response surface methodology (RSM) for SWT applications. The weight percentages of glass and carbon fibers are optimized to achieve desired material properties for SWT blades. The experiments are planned using extreme mixture design RSM and the regression models for desired material properties are developed with a 95% confidence level. RSM-based desirability function is employed to perform multi-objective optimization. Maximum composite desirability of 93.5% is achieved with optimal proportions of 37.9% and 27.1% for glass and carbon fibers respectively. An adequate tensile, flexural and fatigue strengths of 486.02, 435.41 and 316.27 MPa respectively are obtained for optimized glass/carbon hybrid composite at an optimum cost of 2228.76 Rs Kg−1 and density of 3.39 g cm−3. The regression models and optimization results are validated through a confirmation experiment with an error of less than 6.1%.

Osmotic dehydration optimization of butternut squash (Cucurbita moschata Duch) using response surface methodology

Butternut squash (Cucurbita moschata Duch) has a reasonably high carotene content. The flour-making process of it is related to the carotene content and antioxidant activity. It is essential to determine the optimum conditions of osmotic dehydration process by treating the concentration of the salt solution (5-15%), immersion time (60-180 mins), and stirring speed (10-40 rpm) on colour, beta-carotene content, and antioxidant activity using Response Surface Methodology (RSM) with Box-Behnken Design (BBD). All responses had a quadratic model fit (p<0.05). The R2 values are 86.7 for water loss, 84.9 for a solid gain, 96.3 for colour (ΔE), 94.3 for colour (chroma), 65.5 for beta-carotene, and 70 for antioxidant activity, respectively. The decrease in water content was strongly influenced by the salt solution concentration, stirring speed, and soaking time. However, among three factors, the salt solution concentration was the most influential. The stirring speed significantly influences the addition of solids to the material (solid gain) during the osmotic dehydration process. In contrast, ΔE and chroma were influenced by the stirring speed and immersion time for colour changes. Stirring speed influences beta-carotene levels. The amount of antioxidant activity was influenced by the stirring speed and immersion time. The optimization results obtained a salt solution concentration of 15%, a stirring speed of 25 rpm, and an immersion value of 155.8 mins. The magnitude of the three factors above provides a predictive value for water content to decrease to a maximum of 8.57%, solid gain to increase by 8.34%, beta-carotene content to decrease by 11.07% on a wet basis, antioxidant activity to decrease by 2.74, ΔE 20.22, and chroma of 71.07. The composite desirability value was 77.30%. Treating the salt concentration, soaking time, and stirring frequency affect the colour change, water loss, addition of solids, beta-carotene levels and antioxidant activity. The research finds that reducing the water content in the DO process has a significant effect, so the drying process can be carried out more quickly. In addition, the significance of the optimal conditions for the honey gourd DO process can be applied to further processing, for example, for the manufacture of industrial-scale flour

On the optimized fused filament fabrication of polylactic acid using multiresponse central composite design and desirability function algorithm

Efficient optimization of polymeric materials in fused filament fabrication 3D printing (FFF 3DP) is crucial for productivity, cost reduction, resource conservation, consistency, and enhanced part performance. This study employed a multiresponse central composite design of experiments (CCD-DOE) with the desirability function algorithm (DFA) to optimize printing settings on polylactic acid (PLA) using a commercial FFF 3D printer. The goal was to identify optimal parameters for faster build time and reduced material usage in PLA part fabrication. The fabrication process involved computer-aided design and modeling of standard PLA dogbone specimens, meeting ASTM-D638 Type 1 tensile test standards. These specimens were then 3D printed using Ultimaker Green RAL 6018 PLA filament and a 2+ model printer set at varying print parameters. Reduced second-order polynomial models for printing time and PLA weight were generated using stepwise regression, eliminating noninfluential parameters. The models revealed that higher layer thickness, increased print speed, and lower infill density resulted in faster printing times, while lower infill density and higher layer thickness led to lighter PLA prints. DFA analysis determined the optimal settings as a layer thickness of 0.26–0.30 mm and an infill density of 35% for minimizing printing time and PLA weight. The stress–strain curves displayed characteristic high-strength, brittle behavior under tension, while tensile testing of optimized PLA parts revealed increased strength with low strain at the break when layers were aligned parallel to the applied force. These findings advance additive manufacturing and provide practical guidelines for high-quality 3D-printed PLA components. Optimizing FFF 3DP parameters enables efficient production with reduced time and material usage, enhancing cost-effectiveness and the fabrication of high-performance 3D printed products.

Optimization of the QuEChERS Extraction of Mycotoxins in Maize Using Response Surface Methodology (RSM)

Several methods have been developed for the analysis and detection of mycotoxins in food; however, most do not make use of critical statistics and mathematical tools for precise optimization. This study developed, optimized, and validated a modified quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction procedure for the extraction of multiple mycotoxins in maize and subsequent validation using liquid chromatography tandem mass spectrometry (LC–MS/MS). Central composite design (CCD) was used to optimize extraction conditions. Data analysis of full factorial screening experiments revealed that MeCN (%), FA (%), and extraction time significantly affected the mycotoxins recovery. Assessment of the statistical significance of the generated model using analysis of variance (ANOVA), coefficient tables, and surface plots showed the relative interactions of factors and the adequacy of the model. Thus, P values from the lack of fit (LOF) test ranged from 0.137– 0.467 and a composite desirability function of 0.91 was obtained. Using the optimum extraction conditions of 0.1% (v/v) FA in 80.2% MeCN for 74 min, 10 mycotoxins were effectively extracted with satisfactory recoveries (85–114%), coefficients of regression (R2 > 0.98), coefficients of variation (CVs < 15%), limit of quantifications (LOQs) (0.33–60.45 µg/kg), and other associated method validation parameters. The method validation was carried out according to Commission Implementing Regulation 2021/808 and Commission Regulation (EC) No 401/2006 of 23 February 2006. Application of this method to 20 maize samples collected from markets in Botswana showed detectable mycotoxins in 13 samples, with 2 exceeding the European Union (EU) maximum aflatoxin B1 (AFB1) limit, suggesting potential exposure to high levels of toxic mycotoxins in Botswana.

Optimizing roads for sustainability: Inverted pavement design with life cycle cost analysis and carbon footprint estimation

Inverted pavements have proven performance across the world and there is a need to optimize the layer thickness and material properties of the pavement addressing the critical failures in the Mechanistic-Empirical Pavement Design. The present study is made on bituminous concrete pavement with traffic up to 50 MSA and studying the critical factors affecting the pavement performance. The Minitab’s Response Surface Methodology − Box Behnken method was used for the Design of Experiments which includes critical factors and responses obtained from ANSYS finite element modeling of the inverted pavement. The critical factors and responses are normally distributed and indicate a linear relationship with the least error. The composite desirability for minimum stress and strains in the pavement layers was found to be 0.89. The optimized pavement thickness and layer material properties were validated with two pavement field cross sections of different Indian National Highways and it is observed that the optimized cross-section is safe. Further, this research paper carried out Life Cycle Cost Analysis and Life Cycle Assessment of inverted pavement with optimized pavement cross-section obtained including the carbon footprint during the vehicle operation phase. The study demonstrated the benefits of inverted pavement with reduced costs and carbon emissions. Thus, this approach paves the way towards sustainable and long-lasting pavements.