RSM Model Research Articles

Modeling surface quality, cost and energy consumption during milling of alloy 2017A: a comparative study integrating GA-ANN and RSM models

ABSTRACT The use of simple machining features to determine cutting parameters and the machining process is limited, as parts may contain complex features interacting with each other. This study, therefore, focuses on pocket/groove features and proposes an approach integrating hybrid GA-ANN and RSM algorithms to predict surface quality, cost, and energy consumption (QCE). A parametric study was carried out, taking into account the swarm population size (pop) and the number of neurons (n) in the hidden layer, to find the best prediction using the hybrid GA-ANN algorithm. The results showed the highest correlation values (R2) for all output variables (above 0.97%). The study also revealed that the allocation of machining strategies and sequences can have a significant impact on energy consumption, with a 99.25% variation between minimum and maximum values. Mean square error (MSE) data confirmed the effectiveness of the GA-ANN model. Compared with RSM model predictions, energy consumption (Etot), cost (Ctot), and surface quality (Ra) values all showed statistically significant increases of 90.9%, 96.55%, and 40.18%, respectively. This study highlights the potential of the GA-ANN hybrid approach for multi-criteria prediction (quality, cost, and energy: QCE) in comparison with the RSM method, offering potential improvements for machining 2017A alloy.

Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat

This paper demonstrates the analytical and numerical investigations for the obtainment of the predefined critical parameters of double-layer tape spring (DLTS) hinges. The DLTS hinge is utilized for the coupling between the solar panels to assist the accommodation and formulation of the assumed origami-based pattern of the solar arrays. They are examined for the assurance of safety, durability, non-permanent deformation, and stability from the stowed to the deployment configuration. Von Misses stress (σv) and steady-state moment simulations are investigated by varying the critical hinge design parameters of curvature radius (R), subtended angle (θ) and layer thickness (t). Two optimization models, Taguchi and response surface methodology/RSM, are utilized by employing the computational findings to obtain and validate the modified optimal geometric parameters within this analytical experiment. For the Taguchi method, the optimization of σv and the steady-state moment is accomplished with a t of 1.75–2.25 mm, R of 1.5–2.0 mm, and θ of 1–1.2°. Furthermore, the RSM model shows that the t, R, and θ parameters are determined to be 2.90 mm, 2 mm, and 1.35°, respectively. For optimization of the hinge design, both models should be considered for improved verification and accuracy of the results.

Investigation of Modified EMST Micromixing Model Performance on Lagrangian PDF Transported in Lifted Hydrogen/Air Fames

This article presents a numerical simulation of high hydrogen/air flames using the Lagrangian transported PDF method. This method enables the calculation of fluid composition changes resulting from convection and reaction without the need for modelling, while requiring modelling for molecular mixing. Consequently, the accuracy of calculations in this L-PDF method heavily relies on an accurate representation of the mixture model term. The Euclidean model, which provides a better description of physical mixing processes, is well-suited for modelling the molecular mixing term EMST. Additionally, the accuracy of this model depends on the value of the mixing constant, representing the ratio between the mechanical time scale and the scalar time scale. Two algebraic models for the mixing constant have been implemented in the computational code, employing a well-defined function to calculate this ratio for each cell. These models contribute to memory and CPU time savings. To account for turbulence and its interaction with physical phenomena, the RSM model is employed due to its ability to identify different areas of turbulent stresses. Hence, the primary objective of this study is to evaluate the capabilities of these algebraic models in predicting scalar fields within such flames. Overall, the predictions align well with experimental data, affirming the validity of these models.

Comparison of an Ultrasound-Assisted Aqueous Two-Phase System Extraction of Anthocyanins from Pomegranate Pomaces by Utilizing the Artificial Neural Network-Genetic Algorithm and Response Surface Methodology Models.

As a by-product of pomegranate processing, the recycling and reuse of pomegranate pomaces (PPs) were crucial to environmentally sustainable development. Ultrasound-assisted aqueous two-phase extraction (UA-ATPE) was applied to extract the anthocyanins (ACNs) from PPs in this study, and the central composite design response surface methodology (CCD-RSM) and artificial neural network-genetic algorithm (ANN-GA) models were utilized to optimize the extraction parameters and achieve the best yield. The results indicated that the ANN-GA model built for the ACN yield had a greater degree of fit and accuracy than the RSM model. The ideal model process parameters were optimized to have a liquid-solid ratio of 49.0 mL/g, an ethanol concentration of 28 g/100 g, an ultrasonic time of 27 min, and an ultrasonic power of 330 W, with a maximum value of 86.98% for the anticipated ACN yield. The experimental maximum value was 87.82%, which was within the 95% confidence interval. A total of six ACNs from PPs were identified by utilizing UHPLC-ESI-HRMS/MS, with the maximum content of cyanidin-3-O-glucoside being 57.01 ± 1.36 mg/g DW. Therefore, this study has positive significance for exploring the potential value of more by-products and obtaining good ecological and economic benefits in the future.

Synergistic potency of ultrasound and solar energy towards oxidation of 2,4-dichlorophenol: a chemometrics approach.

Industrial units based on chemical processes-the textile and paper industries-are major sources of chlorophenols in the environment, and chlorophenolic compounds persist within the environment for a long time with high toxicity levels. The photo-assisted Fenton's and photocatalysis processes were investigated for the degradation of chlorophenols in the present study. Response surface methodology was employed to get optimised conditions for photocatalysis and photo-Fenton process-governing factors, thus, yielding a profound removal efficiency. Under optimised conditions, with a photocatalyst dose of 0.2 g/L, oxidant concentration of 10.0 mM and pH 5.0, complete removal of 2,4-dichlorophenol (2,4-DCP) was observed in 210 minutes in photocatalytic treatment. In the case of the photo-Fenton process, at an H2O2 dose of 5.0 mM and Fe2+ concentration of 0.5 mM, the organic pollutant was eliminated within 5 minutes of reaction time under acidic conditions (pH 3.0). The RSM model reported the perfect fit of experimental data with the predicted response. Among different isotherm models, the Langmuir isotherm was the best fit. The process followed pseudo-first order rate kinetics among various kinetics models. For the obtained optimised conditions, sonication and solar energy-driven processes were incorporated to study enhanced mineralisation. The solar-assisted Fenton process reported maximum mineralisation (90%) and cost-effective ($0.01/litre for 100 mg/L 2,4-DCP) treatment among different hybrid oxidation processes. The work provides insight into harnessing the naturally available solar energy, reducing the overall treatment cost and opting for a sustainable treatment method.

Photocatalytic degradation of glyphosate using TiO2/Al2O3 using Box-Behnken Design

The TiO2/Al2O3 photocatalyst was produced through a hydrothermal process. The experiment was based on Box-Behnken design (BBD) considering three factors with three levels. A total of 17 experiments were designed and conducted to study the effect of variables such as TiO2/Al2O3 photocatalyst ratio (50:50 - 90:10), TiO2/Al2O3 photocatalyst amount (1.0 g – 40.0 g), and initial concentration of glyphosate (5.0 - 10.0 mg/L) on the percentage of degradation. Glyphosate degradation was conducted under UV light for three hours and monitored using a UV-Vis spectrophotometer (DR6000 HACH). Regression analysis was used to model the experimental results, and a quadratic model was established to study the effect of selected variables on the percentage of degradation. The optimal degradation percentage of 99.87% at the ratio of 50:50 TiO2 to Al2O3, the amount of 40.0 g photocatalyst TiO2/Al2O3 used, and the initial concentration of glyphosate 7.5 mg/L was obtained from the developed RSM model, and the optimized conditions were verified with experimental results. The degradation percentage was predicted using a second-order polynomial model, and the coefficient of determination (R2) were determined as 0.9133. Process variables optimized for percentage degradation were a good match for the BBD method. Finally, the prepared TiO2/Al2O3 photocatalyst was beneficial in degrading herbicides.

Residual regression on laser welding of CFRP-metal hybrid structures

The laser welding between high strength steel and carbon fiber reinforced polymer (CFRP) was explored in order to improve the mechanical properties and capacity to reflect physical relationships. The influence of process parameters on the mechanical properties of the joint was analyzed by RSM based on the experimental results. A residual regression method was proposed, using the residual block as the core structure and combined it with the fundamental concept and structure of the residual network. The residual regression model with two-layer residual block structure was compared with the RSM model at the same time, and the feasibility of this method was validated by experiments. The results show that the mechanical properties of the hybrid joint increase with the increase of the input laser energy per unit time. The proposed residual regression model can directly characterize the relationship between process parameters, intermediate physical quantities, and shear force and has better prediction accuracy and more physical relationships than RSM. The optimized parameters are laser power 1100 W, welding speed 9 mm/s and defocus distance 15 mm.

Muscovite clay for methylene blue removal: advanced optimization and Al-guided breakthroughs-an independent application from prior antibiotic removal investigation.

This study evaluates the efficacy of muscovite mineral clay as an adsorbent for removing Methylene Blue (MB) from water-based solutions. The research examined the impact of initial MB concentration, adsorbent mass, and time on the MB removal process. Two modeling techniques, namely Box-Behnken design with response surface methodology (BBD-RSM) and Artificial Neural Network (ANN), were employed to accurately predict the MB removal efficiency. The RSM and ANN models yielded satisfactory results in estimating MB removal efficiency. To further enhance the optimization process, conventional and techno-economic methods were implemented. The conventional method aimed to maximize dye removal efficiency (R), while the techno-economic approach incorporated multiple objectives. The comparative analysis demonstrated that the techno-economic optimization method outperformed the conventional method. This study emphasizes the significance of considering multiple objectives and integrating techno-economic factors in optimizing clay adsorption processes. The successful application of the techno-economic optimization approach highlights its potential as a robust optimization method, particularly in the field of wastewater treatment. The findings provide valuable insights for optimizing adsorption and advancing environmental remediation practices.

Batch Experimental Studies and Statistical Modeling for the Effective Removal of Tetracycline from Wastewater Using Bimetallic Zn-Cu-Metal-Organic Framework@Hydrogel Composite Beads.

Antimicrobial resistance (AMR) is on an upsurge as more and more broad-spectrum antibiotics are being used haphazardly, resulting in imbalances in the ecosystem and disrupting common/systematic clinical protocols. To combat this issue, metal-organic framework embedded zinc-copper-benzenedicarboxylate@calcium alginate composite beads (Zn-Cu-BDC@CA CBs) were synthesized and utilized for the adsorption of tetracycline (TC) from water. The surface morphology, presence of functional groups, surface area, and thermal stability of Zn-Cu-BDC@CA CBs were evaluated by field emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), and thermal gravimetric analysis (TGA), respectively. Batch adsorption experiments were also carried out to optimize the adsorption performance of Zn-Cu-BDC@CA CBs for TC by adjusting the key parameters, including pH of the solution, contact time, adsorbent dosage, temperature, and initial concentration of TC. From the RSM model, 96.8% removal of TC takes place under the optimum conditions (pH = 7.3, mass = 17.2 mg, concentration = 21.3 ppm, time = 3.4 h, and temperature = 31.8 °C), which aligns closely with the experimental batch study, where the addition of 20 mg of adsorbent to a 20 mL TC solution (20 mg/L) at a pH of 7 and a temperature of 27 °C yielded an impressive TC removal efficiency of 96.55% within 180 min. Zn-Cu-BDC@CA CBs possess homogeneous adsorption surfaces, and TC is adsorbed via monolayer chemisorption, according to the results derived from the Langmuir isotherm model and pseudo-second-order kinetic model. The thermodynamic analysis indicated that the adsorption process is both endothermic and spontaneous. In their entirety, the synthesized Zn-Cu-BDC@CA CBs exhibit certain operational advantages, such as simple separation, satisfactory adsorption performance, and decent recyclability, indicating their viability for industrial application of elimination of TC residues from aquatic environments.

Predictive optimization of surface quality, cost, and energy consumption during milling alloy 2017A: an approach integrating GA-ANN and RSM models

Predictive optimization of surface quality, cost, and energy consumption during milling alloy 2017A: an approach integrating GA-ANN and RSM models

Comparison of various turbulence models in wind tunnels

Wind tunnel test is widely used in aviation, automobile, construction and other fields to simulate the force and flow field distribution of objects in the wind field. However, due to the existence of complex flow phenomena such as turbulence, the accuracy of the wind tunnel test is affected to a certain extent. Therefore, it is of great significance to study the performance of various turbulence models in the wind tunnel to improve the accuracy of the wind tunnel test. This study compares and analyzes the performance of turbulence models in wind tunnel experiments. Based on various turbulence models, numerical simulation methods are employed to simulate and calculate the flow field in wind tunnel experiments, and the results are compared. Through the comparison and analysis, it is found that different turbulence models exhibit different performance in simulating wind tunnel experiments. Among them, the RSM model demonstrates better accuracy and stability, without the presence of boundary layer effects. The purpose of this research is to evaluate and analyze the applicability of various turbulence models in wind tunnel experiments, provide references and guidance for flow field simulations in wind tunnel testing. However, limitations of this study lie in the constraints of the models and computational methods used, and further research and exploration are needed to address these limitations.

Use of Doehlert Matrix as a Tool for High-Throughput Screening of Organic Acids and Essential Oils on Miniaturized Pork Loins, Followed by Lab-Scale Validation That Confirmed Tested Compounds Do Not Show Synergistic Effects against Salmonella Typhimurium

The many possible treatments and continuously changing consumer trends present a challenge when selecting antimicrobial interventions during pork processing. Thirty-five potential antimicrobials were screened at commercial working concentrations by individually adding them to miniaturized (69 cm3) disks of pork loin ends, followed by inoculation with Salmonella Typhimurium ATCC 19585. Two organic acids and nine essential oils significantly inhibited Salmonella counts on pork (p < 0.05). However, six compounds that represent different levels of significance (p < 0.05–p < 0.0001) were selected as independent variables to build a Response Surface Methodology model based on a Doehlert matrix (Doehlert Matrix—RSM): lactic acid 1.25%, formic acid 0.25%, cumin 0.25%, clove 0.25%, peppermint 0.5%, and spearmint 0.5%. The goal of the Doehlert Matrix—RSM was to study single and paired effects of these antimicrobials on the change in Salmonella over 24 h. The Doehlert Matrix—RSM model predicted that lactic acid, formic acid, cumin, peppermint, and spearmint significantly reduced Salmonella when added alone, while no significant interactions between these antimicrobials were found. A laboratory-scale validation was carried out on pork loin end slices, which confirmed the results predicted by the model. While this screening did not identify novel synergistic combinations, our approach to screening a variety of chemical compounds by implementing a miniaturized pork loin disk model allowed us to identify the most promising antimicrobial candidates to then formally design experiments to study potential interactions with other antimicrobials.

Influence of the turbulence model on the numerical predictions of cold turbulent flow in a heavy-duty gas turbine DLN combustor

The influence of different turbulence models on the numerical predictions of cold turbulent flow in a 300 MW F-class DLN combustor was investigated in this study. PIV and PLIF test data were obtained and used for comparison. The simulation results obtained using the LES method exhibited a high level of agreement with the experimental results regarding the velocity distribution and recirculation zone scale. Of the RANS models, only the results from the RNG k-ε and Realizable k-ε are comparable to the experimental data and are suitable for rapid simulation of the combustor flow field. Conversely, the Standard k-ε, SST k-ω, and RSM model results display notable differences from the experiment. Regarding the mixing simulation in the combustor, the LES method was the closest to the experiment. The RANS models roughly reflect the same mixing trend as the experiment, despite the tracer gas diffusing much slower than in the experiment.

Employing Response Surface Methodology for Investigating the Impact of Nigella sativa L. Oil on Physicochemical and Organoleptic Changes of Gouda Cheese upon Accelerated Storage

The primary goal of many producers was to preserve the quality of Gouda during maturation storage. The concentration of Nigella sativa L. oil and ripening time were two main variables in the present research that were improved using the Response Surface approach (RSM). The optimal conditions for ripening Gouda cheese were 0.29 (v/v) SFNSO concentration and 2.8 months at 14°C. It was determined that the RSM models were the best methodology for maintaining the selected Gouda cheese. The optimal Gouda cheeses: pH, fat content, moisture content, total protein, water activity (aw), ADV, and overall acceptability were found to be 5.9, 30.67%, 38%, 23.8%, 0.9131, 4.3, and 8.4, respectively. Additionally, there was a sizable overlap between the experimental and anticipated outcomes, which supported the accuracy and dependability of the proposed approach. The use of supercritical fluid Nigella sativa L. oil SFNSO on Gouda cheese improved and sustained the stability of Gouda cheese over storage time while higher black cumin concentrations had a negative effect on the sensory properties, resulting in dark appearance and bitter taste. Finally, it is necessary to produce excellent Gouda cheese on a practical scale under ideal RSM conditions.

Improving the CFD predictive accuracy of particle fate in turbulent duct flows: Four key issues and applications in in-duct UVGI system

In order to refine the CFD methodology for predicting particle fate in duct flows, this study investigated four key issues largely influencing the predictive accuracy. The flow field modeling method applicable to in-duct UVGI systems (ID-UVs) was also investigated. The particle number independence test indicated that at least 30000 particles should be released. The use of 2D duct cannot consider particle trapping at the sidewalls, thus underestimating the particle deposition behavior. The two-equation k‐ε models significantly overestimated the fluctuation velocity perpendicular to the duct walls in the near-wall region, resulting in them providing Vd+ and η were on average 1767.9 % and 451.3 % higher than those predicted by the RSM model, respectively. Corrections for fluctuation velocity had very limited benefit in improving the predictive accuracy of the two-equation k‐ε model, which unexpectedly resulted in a decrease in the predictive accuracy of the RSM model. Therefore, it was recommended to use the uncorrected RSM model to predict the 3D duct flow field to obtain a high prediction accuracy of particle fate. Similarly, the RSM model was also recommended for modeling the flow field in ID-UVs instead of the two-equation k‐ε model that was often used. This study also revealed that missing the “Wall Reflection Effects” would lead to confusing results and analyzed the reasons for the quantitative differences in the results obtained in previous studies. The findings presented may form a CFD methodological framework for further investigation of the in-duct air purification technologies.

Microwave-assisted aqueous two-phase extraction of polysaccharides from Hippophae rhamnoide L.: Modeling, characterization and hypoglycemic activity

Natural polysaccharides are concerned for their high biological activity and low toxicity. Two kinds of polysaccharides were extracted from Hippophae rhamnoide L. by microwave-assisted aqueous two-phase system. Under the optimal conditions predicted by RSM model (K2HPO4/ethanol (18.93 %/28.29 %), liquid to material ratio 77 mL/g, power 625 W and temperature 61 °C), the yield of total polysaccharides reached 35.91 ± 0.76 %. Moreover, the polysaccharides extraction was well fitted to the Weibull model. After purification by Sepharose-6B, the polysaccharides from top phase (PHTP, purity of 81.44 ± 1.25 %) and bottom phase (PHBP, purity of 88.85 ± 1.40 %) were obtained. GC, FT-IR, methylation and NMR analyses confirmed the backbone of PHTP was composed of a repeated unit →4)-β-D-Glcp-(1 → 2)-α-L-Rhap-(1 → 4)-β-D-Galp-(1 → 4)-α-D-GalAp-(1 → 3)-α-L-Araf-(1 → 3)-α-D-Manp-(1→, while the repeated unit in PHBP was →3)-α-L-Araf-(1 → 2)-α-L-Rhap-(1 → 4)-β-D-Glcp-(1 → 3)-α-D-Manp-(1 → 4)-β-D-Galp-(1 → 4)-α-D-GalAp-(1→. Compared with PHTP (6.46 × 106 g/mol), PHBP with relatively low molecular weight (8.2 × 105 g/mol) exhibited the smaller particle size, better water-solubility, thermal and rheological property, stronger anti-glycosylation and α-amylase inhibitory effects. Moreover, PHTP and PHBP displayed a reversible inhibition on α-amylase in a competitive manner. This study provides a high-efficient and eco-friendly method for polysaccharides extraction, and lays a foundation for sea buckthorn polysaccharides as potential therapeutic agents in preventing and ameliorating diabetes.

Modeling and optimization of the yield of pyrolytic oil from waste face masks using RSM‐ANN‐LM hybrid approach

AbstractPyrolysis is one of the most widely practiced thermochemical conversion technique to convert biomass into bio fuel. In this investigation, waste surgical masks were taken for pyrolysis process and the pyrolysis oil yield was determined experimentally. The experiments were designed and optimized based on Box Behnken Design (BBD) with operating parameters (feed size, temperature, and time) to study the effect on the yield of the pyrolysis oil and char. Experiments were conducted by varying the feed size (10–50 mm), pyrolysis temperature (400–600°C), and time (30–90 min). A hybrid Response Surface Methodology –Artificial Neural Network‐ Levenberg–Marquardt algorithm (RSM‐ANN‐LM) modelling approach has been to optimize the process parameters for the prediction of pyrolysis oil yield. The optimized network architecture was found to be 3‐10‐1 and the authenticity of the developed model has been evaluated using Regression Co‐efficient (R2) and Mean Squared Error (MSE). The developed RSM‐ANN‐LM model has outperformed the RSM model on predicting the pyrolytic oil yield. For the optimized conditions of 10 mm size, 600°C of pyrolysis temperature and 90 min pyrolysis time, 85.30% oil yield was obtained.

Predicting the adsorption capacity of organic dye using zirconium-based metal–organic framework: a comparative analysis of RSM and ANN-based models

Predicting the adsorption capacity of organic dye using zirconium-based metal–organic framework: a comparative analysis of RSM and ANN-based models

Modeling and optimization of poly(lactic acid)/poly(ℇ-caprolactone)/Nigella sativa extract nanofibers production for skin wounds healing by artificial neural network and response surface methodology models

Electrospun fibrous scaffolds have great potential for the effective treatment of wounds. Novel blend scaffolds were fabricated from poly(ℇ- caprolactone) (PCL)/poly (lactic acid) (PLA) with Nigella sativa (NS) extract in different concentrations of 10 %, 15 %, 20 %, and 25 % by one nozzle electrospinning. RSM and ANN models were used to determine optimal nanofiber. The results showed that the ANN model had average goodness values of almost 1.992 which was higher than the RSM model with an amount of 1.823. The best sample was determined with the combination of parameters such as PLA/PCL (70:29) concentration, voltage 17 kV, and flow rate 0.2 ml/h in diameter of nanofiber 410 nm by Genetic Algorithm (GA) model with cost value 0.0216 that was lower than cost value (0.0927) of ANN model. The effect of NS extract on nanofibers properties showed that loading high concentrations of NS extract in PLA/PCL polymer solutions caused a decrease in nanofibers diameter, hydrophilicity, and tensile strength. Overall, PLA/PCL/NS 25 % nanofiber was selected as an optimal web with an average diameter of 370 ± 68 nm with a young modulus 5.94 MPa. This scaffold also exhibited the highest antibacterial activity, cell attachment, and cell viability based on the MTT assay.

An Experimental Study ofthe Physio-Mechanicaland Microstructural Performances of Escherichia Coli Bacteria-Based Bio-Concrete

A balanced mixture of cement, sand, stone or brick chips, and water is carefully allowed to form concrete, a man-made building material. These elements can be adjusted appropriately to produce concrete with a variety of qualities. Although concrete may endure compressive forces, like natural stone, tensile forces can cause it to crack. As a result, crack formation is afrequentoccurrence in concrete, allowing various foreign chemicals and water to enter the structures and shortening their life span. The likelihood of cracking grows with time due to variations in humidity and temperature. It can be exceedingly expensive to maintain or repair concrete construction items. The use of bio-concrete for the construction of durable structures has shown to be quite advantageous in this perspective. It is beneficial for improving the properties of concrete as well as lowering maintenance costs. In this investigation, concrete samples measuring 100×100×100 mm weremade and periodically tested for compressive and split tensile strength testing. Following a 28-day curing period, the concrete treated with Escherichia coli bacteria had compressive and split tensile strengths that were 10% and 23% higher than identical bacteria-free. The non-destructive test on cylindrical samples was then conducted to evaluate the material qualities. The mortar samples of crystalline structures were also validated by SEM examination. In order to properly and reliably anticipate the strength of concrete, the RSM model was also formulated.