Response Surface Methodology Method Research Articles

The Response Surface Methodology for Assessment of HLB Values of Mixtures of Non-Ionic Surfactants Using Parameters from Their π-A Isotherms.

The aim of the work was to determine important parameters of the course of π-A isotherms, which can determine the HLB (hydrophilic-lipophilic balance) value of surfactant mixtures with selected structural features, such as a straight or branched hydrocarbon chain and a double bond, using RSM (response surface methodology) computational methods. Mixtures of surfactants derived from fatty acids and sorbitan with specific HLB values were evaluated by Langmuir trough. The resulting elasticity modules (ELM) and molecules surfaces (SAM) were evaluated via response surface methodology and respective equations were calculated. The π-A isotherm determined in a Langmuir trough and the ELM and SAM parameters determined on the basis of this isotherm may be useful for determining the HLB of a fixed surfactant mixture. The RSM method used, in which ELM and SAM were assumed as two independent variables, can be a useful technique for tracking the influence of individual molecular characteristics on the hydrophilic-lipophilic properties of mixtures of surfactant compounds. Changes in HLB as a dependent variable can be described as a function of ELM and SAM.

Encapsulation of W/O/W Acerola Emulsion by Spray Drying: Optimization, Release Kinetics, and Storage Stability.

Acerola (Malpighia emarginata DC.) is a sub-tropical and tropical fruit renowned for its high levels of vitamin C and phenolic compounds, which offer health benefits. This study aimed to optimize the spray drying process by determining the inlet and outlet temperatures using response surface methodology (RSM) with the central composite design. Additionally, it aimed to evaluate the release kinetics in the hydrophilic food simulation environment and the stability of the resulting powder under various storage temperatures. The RSM method determined the optimal inlet and outlet temperatures as 157 °C and 91 °C, respectively. High-accuracy prediction equations (R2 ≥ 0.88) were developed for moisture content (3.02%), process yield (91.15%), and the encapsulation yield of total polyphenol content (61.44%), total flavonoid content (37.42%), and vitamin C (27.19%), with a predicted monolayer moisture content below 4.01%, according to the BET equation. The powder exhibited good dissolution characteristics in the acidic hydrophilic food simulation environment and showed greater stability when stored at 10 °C for 30 days, compared to storage at 35 °C and 45 °C.

Development of partitioned rectangular-shaped heat sink for photovoltaic thermal systems and performance evaluation using TiO2/Water-based nanofluids

The current study presents a partitioned rectangular-shaped heat sink for improving the PVT system performance. The experiment was examined under various experimental parameters. Five cooler block types with different dimensions were arranged to share the same total contact area with the panel back surface. These cooler blocks were placed in different arrangements, namely (PVT-I, PVT-II, PVT-III, PVT-IV, PVT-V), and studied comparatively using water. The dimensions and numbers of each cooler block arrangement were altered, while maintaining the same surface area in contact with the photovoltaic panel. Through the response surface methodology (RSM) optimization, the optimal arrangement with the best performance was determined, and then TiO2/nanofluid was applied to that arrangement with different volumetric concentrations. Using water, the highest electrical and thermal efficiencies were obtained by case PVT-III with values of 19.39 % and 63.72 %, respectively. This was followed by PVT-II, PVT-V, PVT-I, and PVT-IV in that order for electrical efficiency; and at the same time by PVT-II, PVT-I, PVT-IV, and PVT-V in that order for thermal efficiency. The RSM method recommended the optimal arrangement case as case PVT-III. By using nanofluid in that case arrangement, the highest electrical efficiency value was recorded upon using 0.3 wt% and operating the system at 1.5 L/m and 900 W/m2. Regarding the thermal efficiency results, in that case, the highest and lowest values were recorded upon using 0.3 wt% and 0.1 wt%, respectively, with values of 72.49 % and 31.19 %. Increasing the volumetric concentrations positively reflected on the system's performance, resulting in high efficiencies being achieved. In addition, the highest electrical efficiencies were obtained at high levels of solar irradiance and flow rates.

Bioethanol production from sugarcane molasses by co-fermentation of Saccharomyces cerevisiae isolate TA2 and Wickerhamomyces anomalus isolate HCJ2F-19

PurposeCo-culturing is a widely used method to improve bioethanol production from biomass enriched in fermentable sugars. This study aims to produce bioethanol from sugarcane molasses by simultaneous co-fermentation of S. cerevisiae isolate TA2 and W. anomalus isolate HCJ2F-19.MethodsResponse surface methodology (RSM) based on the central composite design (CCD) was employed to optimize fermentation conditions, including mixing rate (110–150 rpm), temperature (25–35 °C), molasses concentration (25–35 obrix), and incubation time (36–72 h). The ethanol concentration was analyzed using HPLC equipped with a UV detector.ResultsThe monocultureS. cerevisiae isolate TA2 produced 17.2 g.L−1 of ethanol, 0.33 g.g−1 of ethanol yield, and 0.36 g.L−1.h−1 of productivity compared to W. anomalus isolate HCJ2F that produced 14.5 g.L−1, 0.30 g.g−1 and 0.28 g.L−1.h−1 ethanol, ethanol yield, and productivity under laboratory conditions, respectively. In comparison to single cultures of S. cerevisiae TA2 and W. anomalus HCJ2F, the co-fermentation using both isolates showed an increased ethanol yield of 29% and 53% compared to the single species fermentations, respectively. The results showed that the growth of W. anomalus HCJ2F-19 and S. cerevisiae TA2 was not influenced by each other during the co-fermentation process. The one variable at a time optimization (OVAT) analysis resulted in an ethanol concentration of 26.5 g.L−1 with a specific yield and productivity of 0.46 g.g−1, 0.55 g.L−1.h−1, respectively, at pH 5.5, 25 obrix, 48 h, 150 rpm, 30 °C, 60:40 inoculum ratio, and 10% overall inoculum size. The maximum ethanol concentration of 35.5 g.L−1 was obtained by co-fermentation using the RSM-CCD tool at 30 obrix, 30 °C, 54 h, and 130 rpm.ConclusionThe results suggested that the co-fermentation of S. cerevisiae isolate TA2 and W. anomalus isolate HCJ2F improves bioethanol production from sugar cane molasses under optimum fermentation conditions.

Multi-Objective Optimization of Vortex Magnetohydrodynamics (MHD) Generator using Response Surface Methodology

The introduction of electromagnetic fields in fluid dynamics in magnetohydrodynamics (MHD), particularly when those fields are vector and non-uniform, complicates its application in vortex geometry. The imperative to optimize MHD generators arises from the inherent trade-off between voltage and pressure drop in energy conversion systems, to maximize voltage output while minimizing associated pressure drop. This study focuses on optimizing vortex MHD generators by applying Response Surface Methodology (RSM), which is based on mathematical models that capture the complex relationships between factor and response variables. This method offers a comprehensive approach to obtaining the optimum solution to the objectives, voltage and pressure drop, based on fluid velocity and magnetic field strength input parameters. Numerical optimization RSM generates 11 solutions. The optimum solutions obtained are a velocity of 1.415 m/s, and magnetic field strength of 0.43 T, and the corresponding optimum output voltage and pressure drop will be 4.264 mV and 4.254 psi, respectively, with a desirability level of the selected solution is 0.770. This study suggests the RSM method shows a good measurement of R2 and RSME. Our findings contribute to the understanding of optimizing vortex MHD generators and offer insights into achieving efficient energy conversion systems of a set of optimum generator operating parameters.

Optimization of Pb (II) Ion Removal from Synthetic Wastewater Using Dead (Chlorophyta) Macroalgae: Prediction by RSM Method

The Pb2+ ions biosorption removal onto dead biomass of Chlorophyta algae is optimized by employing response surface methodology (RSM). Central composite design (CCD)-based experiments were carried out, and RSM was used to evaluate the results. The effects of contact time (15-120min), with pH solution (2-7), initial lead concentration (25-100 mg/L), biomass dose (0.01-1 g/100 mL), agitation speed (100-300 rpm) on the biosorption process were investigated. The optimal conditions of the experimental, data were pH (5), metal concentration (50mg/L), dosage (0.2g/100mL), agitation speed (200 rpm), and contact time of 120 min with constant particle size (63 mm), which gave 98.88% removal efficiency. All the variables and reactions in the biosorption experiments were evaluated using the desirability function to determine the optimal point at which the desired parameters may be attained. The promising results obtained indicate the potential use of Chlorphyta green macroalgae to treat industrial wastewater polluted with toxic metals.

Optimization of Malabar Nut (Justicia adhatoda L.) Leaves’ Phenolic Compounds by Alginate Emulsion-gelation Approach using Response Surface Methodology

Background: The encapsulation technique is an effective method for coating bioactive molecules and protecting them against various technological treatment conditions during production processing. Objective: The aim of this study was to optimize the encapsulation conditions of phenolic compounds extracted from Malabar nut (Justicia adhatoda L.) leaves by alginate emulsion-gelation approach using response surface methodology Methods: The ionotropic gelation method was used to encapsulate the phenolic compounds of Malabar nut (Justicia adhatoda L.) leaves. The optimization of this phenolic compounds encapsulation was carried out using response surface methodology through Box˗Behnken design. Four parameters with three levels (-1, 0, +1) were chosen including sodium alginate concentration (2 – 3 - 4%), calcium chloride concentration (4 - 6 - 8%), plant extract-alginate solution ratio (0.02 – 0.11 - 0.2 mg/ml), and gelation time (5 – 12.5 - 20 min). of the total phenolic and flavonoid contents encapsulation efficiency was assessed. Likewise, the antioxidant activity was evaluated using ferric-reducing power (FRP) and free radical scavenging activity (DPPH). Results: The results of response surface methodology analysis using Box˗Behnken design showed that the optimal encapsulation conditions were 3.11% for alginate concentration, 5.74% for calcium chloride concentration, 0.1 mg/ml for the plant extract-alginate solution ratio, and 10.80 min for gelation time. Under these conditions, the optimum values of total phenolic and flavonoid encapsulation efficiency were 86.17% and 75.69%, respectively, 126.75 mg AAE/100 g for ferric reducing power and 97.29% for DPPH. The experimental and prediction results have expressed a high significant level for all responses. Conclusion: The method revealed the validity of elaborated models through response surface methodology optimization processes for phenolic antioxidants encapsulation of Malabar nut (Justicia adhatoda L.) leaves extract.

Response surface methodology for the green obtention of polyphenols from yerba mate

AbstractOptimal conditions for polyphenols extraction from plants are moderate conditions and being water‐based is mandatory. In this work, a response surface methodology (RSM) was used to attain optimal conditions for polyphenols extraction from yerba mate (YM, South‐America infusion) using water as solvent under ultrasound (US). The following parameters were varied: mass of YM/volume of water (YM/W), pH, temperature, and time. The optimal conditions were 90°C with a YM/W of 150 g L−1 for 5 min, at which the highest total polyphenol content (TPC) concentration (11,300 mg GAE L−1) was found, together with an antioxidant activity of 132 mmol Trolox equivalent (TE) L−1 of extract. The conditions obtained from the RSM method were applied to other methods as follows: extraction without stirring, magnetic stirring assisted extraction and US with magnetic stirring assisted extraction. Results indicated that the US‐assisted extraction was the most efficient for extracting polyphenols under these conditions. This study helps to developing an eco‐friendly and cost‐effective experimental design, reducing the production of wastes, and using a very fast water‐based method.

Comparison of modeling, optimization, and prediction of important parameters in the adsorption of cefixime onto sol-gel derived carbon aerogel and modified with nickel using ANN, RSM, GA, and SOLVER methods

Today, the main goal of many researchers is the use of high-performance, economically and industrially justified materials, as well as recyclable materials in removing organic and dangerous pollutants. For this purpose, sol-gel derived carbon aerogel modified with nickel (SGCAN) was used to remove Cefixime from aqueous solutions. The influence of important parameters in the cefixime adsorption onto SGCAN was modeled and optimized using artificial neural network (ANN), response surface methodology (RSM), genetic algorithm (GA), and SOLVER methods. R software was applied for this purpose. The design range of the runs for a time was in the range of 5 min–70 min, concentration in the range of 5 mg L−1 to 40 mg L−1, amount of adsorbent in the range of 0.05 g L−1 to 0.15 g L−1, and pH in the range of 2.0–11. The results showed that the ANN model due to lower Mean Squared Error (MSE), Sum of Squared Errors (SSE), and Root Mean Squared Error (RMSE) values and also higher R2 is a superior model than RSM. Also, due to the superiority of ANN over the RSM model, the optimum results were calculated based on GA. Based on GA, the highest Cefixime adsorption onto SGCAN was obtained in pH, 5.98; reaction time, 58.15 min; initial Cefixime concentration, 15.26 mg L−1; and adsorbent dosage, 0.11 g L−1. The maximum adsorption capacity of Cefixime onto SGCAN was determined to be 52 mg g−1. It was found the pseudo-second-order model has a better fit with the presented data.

Parametric and sensitive analysis of Pd-Ag membrane reactor performance in biogas reforming to generate decarbonized hydrogen by Computational Fluid Dynamic-Response Surface Methodology

The efficient production of hydrogen from renewable sources is pivotal for the development of sustainable energy systems. Biogas steam reforming (BSR) conducted in palladium-based membrane reactors (MRs) offers a pathway for high-purity H2 generation at lower temperatures compared to traditional processes. In this study, a computational fluid dynamics (CFD) model was developed to simulate the BSR process within a Pd-Ag MR. The CFD model has integrated comprehensive kinetics, mass transport, and hydrodynamics, and its accuracy was confirmed through validation against experimental data, demonstrating substantial agreement. As a novel approach, to optimize the BSR reaction conditions to get the best MR performance, the CFD model was coupled with response surface methodology (RSM), which has been employed to enable the identification of optimal values for key parameters such as reaction temperature, pressure, gas hour space velocity, feed molar ratio, and sweep gas ratio, where the temperature was found to have the most significant impact on the MR’s performance within the examined intervals of input parameters. The objective of RSM method applied to CFD modeling was to maximize with high accuracy CH4 conversion, H2 recovery, and reduce CO2 emissions. RSM models effectively established correlations between input parameters and responses. The determined optimal conditions for the BSR process were a temperature of 683 K, a reaction pressure of 6 bar, a GHSV of 3000 h−1, a H2O/CH4 ratio of 1.34, and a sweep ratio of 9. Under these conditions, the system achieved remarkable results with 100 % CH4 conversion, 43 % H2 recovery, and an 81 % reduction in CO2 emissions. This study underscores the effectiveness of integrating CFD with RSM for the precise modeling and optimization of MRs in BSR reactions. The synergy between these approaches provides a robust framework for advancing the efficiency and sustainability of hydrogen production processes from renewable sources.

WITHDRAWN: Effects of hydraulic retention time and cultivation on nutrients removal and biomass production in wastewater by membrane photobioreactor: modeling and optimization by machine learning and response surface methodology

Discharge of excess nutrients in wastewater can potentially cause eutrophication and poor water quality in the aquatic environment. A new approach of nutrients removal in wastewater is by utilizing microalgae which grow by absorbing CO2 from air. Furthermore, the use of membrane photo-bioreactor (MPBR) that combines membranes and photo-bioreactor has emerged as a novel wastewater treatment method. This research sought to model, forecast, and optimize the behavior of dry biomass, dissolved inorganic nitrogen (DIN), and dissolved inorganic phosphorus (DIP) in MPBR by response surface methodology (RSM) and artificial neural network (ANN) algorithms, which saved time and resources of experimental work. The independent variables that have been used for modeling were hydraulic retention time (HRT) and cultivation. For this purpose, the dry biomass of algal production, DIN and DIP behavior were modeled by RSM and ANN algorithms, to identify the optimum mode of processing. RSM modeling has shown good agreement with experimental data. According to RSM optimization, the optimum mode for DIN and DIP occurred at 1.15 days of HRT and 1.92 days of cultivation. The ANN showed better performance than the RSM model, with the margin of deviation being less than 10%. Furthermore, the ANN algorithm showed higher accuracy than RSM method in predicting the dry biomass, DIN and DIP behavior in MPBR.

Experimental Investigation to Optimize the Manufacturing Parameters of Ankle–Foot Orthoses Using Composite and Titanium Nanoparticles

The optimum structural characteristics of lamination materials used in the fabrication of prosthetic and orthotic parts were investigated in this work. Optimization was chosen based on high yields, ultimate stresses, and bending stress properties. The ideal materials were determined through the use of an RSM (response surface methodology) which considers three factors: Perlon reinforcement, a layer of glass fiber, and the percentage of titanium nanoparticles combined with the matrix laminating resin. The RSM approach suggests thirteen samples by manipulating two variables: the Ti nano percentage and the number of Perlon layers. Laminating materials, defined by RSM methods and treated with a vacuum system, were submitted to a series of tests. The ideal lamination material was compared with the laminations from the initial study through the use of tensile, flexural, and fatigue testing according to ASTM standards. Tests carried out using version 10.0.2 of Design Expert software showed that, compared with the 12 other laminations, the one with 10 Perlon layers and 0.75 percent Ti nano had the highest overall yield and ultimate and bending loads. Fatigue eventually showed that stamina tension constraints were applied for optimal lamination, compared to ten Perlon lamination layers. We additionally tested the fatigue life of the best material and compared it with the available materials used at prosthetics and orthotics centers.

Effects of glass powder and Gypsum (Timchemt) on the thermal and mechanical properties of plaster bricks

The present research aims to study the effect of glass powder extracted from the glass waste disposal area in Ouargla (southern Algeria) on the thermo mechanical properties of plaster bricks. Therefore, density, compressive strength, flexural strength, thermal conductivity, and thermal resistance were measured in the laboratory using mixtures generated through the response surface methodology (RSM). This RSM technique was used to assess the influence of gypsum (95% – 100%) and glass powder (0% – 5%) on the behavior of hardened plaster bricks. The experimental results and RSM evaluation show a significant increase in compressive and flexural strength at the maximum and minimum levels of Timchemt and glass powder, and detailed scanning electron microscopy (SEM) analyses supported the results. Consequently, quadratic mathematical models were developed using the RSM method applied to the obtained results. Analysis of variance (ANOVA) yielded a (P-value <0.05) and correlations exceeding 91% for all the studied parameters. Probability plots showed excellent correlation between predicted and experimental values, and the 2D and 3D contours of the RSM demonstrated that the fitted models work very well and can provide the appropriate relationship between the selected parameters and thermo mechanical properties.

Heavy metal (Cu2+) removal from wastewater by metal-organic framework composite adsorbent: Simulation-based- artificial neural network and response surface methodology

Metal-organic framework (MOF)--based composites have received significant attention in a variety of applications, including pollutant adsorption processes. The current investigation was designed to model, forecast, and optimize heavy metal (Cu2+) removal from wastewater using a MOF nanocomposite. This work has been modeled by response surface methodology (RSM) and artificial neural network (ANN) algorithms. In addition, the optimization of the mentioned factors has been performed through the RSM method to find the optimal conditions. The findings show that RSM and ANN can accurately forecast the adsorption process's the Cu2+ removal efficiency (RE). The maximum values of RE are achieved at the highest value of time (150 min), the highest value of adsorbent dosage (0.008 g), and the highest value of pH (=6). The R2 values obtained were 0.9995, 0.9992, and 0.9996 for ANN modeling of adsorption capacity based on different adsorbent dosages, Cu2+ solution pHs, and different ion concentrations, respectively. The ANN demonstrated a high level of accuracy in predicting the local minima of the graph. In addition, the RSM optimization results showed that the optimum mode for RE occurred at an adsorbent dosage value of 0.007 g and a time value of 144.229 min.

Research on the effects of slurry uniformity and mixing power of continuous cementing slurry mixers based on statistical and CFD methods

To enhance the efficiency of continuous cement mixing in well cementing, a Computational Fluid Dynamics (CFD) approach was employed to investigate the mud flow and mixing processes within the continuous mixing system. This study aims to analyze the influence of various design factors and their interactions on the mixing of different slurries in the well cementing slurry continuous mixer. Using the Response Surface Methodology (RSM) with a Box-Behnken model, this work combines the Euler-Euler multiphase flow model, k-turbulence model, and the Multiple Reference Frame (MRF) method to simulate two-phase fluid flow and agitator rotation, establishing the CFD model of the mixing system. The CFD model, coupled with the RSM method, analyzed four design factors: impeller diameter (400–800 mm), design density (1.4–2.6 g/cm³), impeller speed (100–300 rpm), and particle size (20–500 μm), studying their impact on two response variables, slurry mixing uniformity, and total power consumption. Through RSM and multiple regression analyses, predictive models for slurry mixing uniformity and corresponding power consumption were established under different design factors. Data comparison analysis revealed excellent agreement between predicted and actual results. Findings indicate that, when using uniformity or power as evaluation criteria, the P-value is smallest for impeller speed and largest for particle size. Hence, the impeller speed exhibited the strongest correlation with both, followed by impeller diameter, and lastly, design density and particle size. Only the interaction between impeller diameter and impeller speed (P-value <0.0001) significantly influenced both power and mixing uniformity, with its significance ranking just below the linear effects of impeller speed and diameter. Slurry uniformity decreased with increased design density and particle size, while power consumption exhibited the opposite trend. Uniformity increased with increasing impeller speed, but when the uniformity reached its maximum, further speed increments led to a slight decrease in uniformity, accompanied by a sharp increase in power. This was significantly influenced by the quadratic effect of impeller speed (P-value <0.0001), with its significance weaker than the linear effects. The proposed predictive model accurately forecasts response values for any given design parameter, providing a research direction for optimizing mixer design and selecting corresponding design parameters. This approach streamlines the study of mud mixing systems, reducing research time and costs. Moreover, it offers scientific guidance for optimizing operational parameters corresponding to well cementing conditions.

Development, characterization, and in vitro biocompatibility of evening primrose oil nanoemulsions using ultrasonic nano-emulsification technology

BackgroundIn this study, evening primrose oil (EPO), recognized for its bioactivity and safety, played a pivotal role as a primary component in the production of nanoemulsions (NEs) through a combination of homogenization and ultrasonic nano-emulsification technology. The significance of EPO NEs lies in their potential impact on various aspects of health and well-being. The research aimed to explore the physicochemical properties, encapsulation efficiency, and in vitro biocompatibility of the resulting EPO NEs by employing three distinct surfactants: dioctadecyl dimethylammonium bromide (D+), dihexadecyl phosphate (D−), and saponin (Sn) for stabilization. MethodsResponse surface methodology (RSM) was used to optimize the preparation parameters of the NEs. The physicochemical properties of NEs were assessed using various techniques, including dynamic light scattering, transmission electron microscopy, differential scanning calorimetry, fluorescence polarization spectroscopy, and viscometer measurements. The encapsulation efficiency of the NEs was evaluated through the use of quercetin. Furthermore, the in vitro biocompatibility of the NEs was assessed via a cell viability assay. FindingsRSM effectively determined the optimal ultrasonication operation parameters, including time and amplitude, to achieve EPO NE with the desired size and distribution. The thermal phase change and intra-particle fluidity of NEs decreased as the oil/surfactant ratio increased and varied with the type of surfactant used. Furthermore, EPO NE exhibited nearly Newtonian fluid characteristics. Most importantly, all NEs demonstrated excellent in vitro biocompatibility and high encapsulation efficiency. In conclusion, the high potential of EPO NEs underscores their pivotal role in advancing drug delivery systems.

Enhancing mechanical performance of TiO2 filler with Kevlar/epoxy-based hybrid composites in a cryogenic environment: a statistical optimization study using RSM and ANN methods

This research aims to investigate the mechanical performance of the different weight proportions of nano-TiO2 combined with Kevlar fiber-based hybrid composites under cryogenic conditions. The following parameters were thus considered: (i) Kevlar fiber mat type (100 and 200 gsm); (ii) weight proportions of TiO2 nanofiller (2 and 6 wt%); and (iii) cryogenic processing time (10–30 min at −196°C). The composites were fabricated through compression molding techniques. After fabrication, the mechanical characteristics of the prepared nanocomposites—such as tensile, bending, and impact properties—were evaluated. The optimal mechanical strength of nanofiller-based composites was analyzed using response surface methodology (RSM) and artificial neural networks (ANNs). Compositions, such as four weight percentages of nano-TiO2 filler, 200 gsm of the Kevlar fiber mat, and 20 min of cryogenic treatment, were shown to produce the maximum mechanical strength (65.47 MPa of tensile, 97.34 MPa of flexural, and 52.82 J/m2 of impact). This is because residual strains are produced at low temperatures (cryogenic treatment) due to unstable matrices and fiber contraction. This interfacial stress helps maintain a relationship between the reinforcement and resin and improves adhesion, leading to improved results. Based on statistical evaluation, the ratio of correlation (R2), mean square deviation, and average error function of the experimental and validation data sets of the experimental models were analyzed. The ANN displays 0.9864 values for impact, 0.9842 for flexural, and 0.9764 for tensile. ANN and RSM models were used to forecast the mechanical efficiency of the suggested nanocomposites with up to 95% reliability.

Fischer–Tropsch synthesis over the Co–Ni/Al2O3 nanocatalyst: influence of process variables, modeling and optimization using response surface methodology

AbstractBACKGROUNDLight olefins production greatly influenced by the process conditions. In the present work Al2O3 supported Co‐Ni nanocatalysts were evaluated for production of light olefins. Incipient wetness impregnation procedure was used for catalyst synthesis. Catalysts were characterized for determining the physicochemical properties using various techniques of XRD, SEM, EDS, H2‐TPR, TPD, TEM, XPS, TGA, DSC, FT‐IR, and BET. The influence of process conditions (Pressure, Temperature and H2/CO inlet feed molar ratio) was investigated on the catalytic performance of Co‐Ni/γAl2O3 nanocatalyst toward lower olefins. Temperature, pressure, and feed ratio factors were changed in wide ranges of 250–450 °C, 1–12 bar, and 1–4 respectively. The Response Surface Methodology (RSM) method was employed for modeling and optimization.RESULTSThe optimum process conditions were obtained using the RSM method (T = 383 °C, P = 4 bar, and H2/CO = 1.92). The highest selectivity of light olefinic products and conversion of carbon monoxide and the lowest selectivity toward methane were obtained concurrently under the optimized conditions with desirability of 0.896.CONCLUSIONThe impact of inlet feed ratio, temperature, and pressure factors was investigated and it was found that the performance of the catalyst greatly depends on the operational parameters. © 2023 Society of Chemical Industry.

Prediction of tribology in (Mg2Si+TiB2)/A356 composites based on RSM method and correlative with topographical characteristics

This work deals with the prediction of tribological performance in (Mg2Si+TiB2)/A356 composites. In this work, tribological properties are also correlated with topographical characteristics. The effect of several input factors such as load, sliding distance and TiB2 wt% on tribological characteristics of (Mg2Si+TiB2)/A356 composites was explored by Response Surface Methodology (RSM). Central composite design (CCD) with three input factors and three levels of variables in RSM was used to develop a statistical model. RSM suggests a quadratic model to express a mathematical relationship between input factors to assess wear rate and coefficient of friction (COF). Analysis of variance (ANOVA) was applied to investigate the influence of input factors on wear rate and COF and found that most significant variable that influences wear rate and COF is applied load followed by TiB2 wt% and sliding distance. The desirability function approach is applied to optimized input variables to minimize wear rate and COF. Optimum wear rate is noticed at input variables of 26 N load, 2766 m sliding distance and 3.36 wt% of TiB2 and optimum COF was observed at 29 N of load, 2233 m sliding distance and 1 wt% of TiB2. A confirmatory test was conducted to validate the developed model and observed that experimental results matched the predicted value with an error of less than 5%. SEM and AFM examined surface topography of wear surfaces to understand the wear mechanism and surface features at various input factors. Microstructural features were also examined under optical microscope and SEM, revealing that increased TiB2 content in hybrid composite refined primary Mg2Si and eutectic Mg2Si phase and resulted in increased hardness and improved tribological behaviour of hybrid composite.

Combined sand eco-mortar reinforced with polyethylene Terephthalate: Behavior and optimization using RSM method

Today, economic and environmental interests have become imperative in construction works. Therefore, the use of substitute materials for natural aggregates or cement contributes to the preservation of natural resources and the reduction of carbon dioxide diffusion. The large quantity of dune sand available in the Algerian Sahara and the affordable cost of recycled aggregates and plastic waste encourage their use as a replacement for natural aggregates in the production of cementitious materials. The interaction of dune sand (DS) and recycled sand (RS) with the introduction of a polyethylene terephthalate (PET-F) fibre help to design a normative ecological material. The aim of the study was to explore and optimize the proportions of these materials to create an environmentally-friendly cementitious product. The strategy is based on the progressive substitution of dune sand by another originating from demolition waste at different rates: 0%, 25%, 50%, 75% and 100%, and a gradual rate of PET-F fibres, 20 and 30 mm from 0% to 2% with a step of 0.5%. In this article, we study the effect of the new composites on the physico-mechanical performance of the designed eco-mortars through several tests (spreadability, fresh density, porosity, capillary and immersion absorption, compression strength, and flexural strength). In addition, we utilise Response Surface Methodology (RSM) to predict approximate parameters that influence the properties of new cementitious composites. The results obtained show the significant benefit of the following rates: 50% SD, 50% SR and (0.5%, 30 mm) PET-F fibre. Thus, the RSM model correlates well with the experimental data, which has undoubtedly led to an improvement in the physical–mechanical characteristics of the eco-mortar studied. Otherwise, the proposed model reaches a high pertinence, with a good desirability coefficient of 0.917.