Response Surface Methodology Framework Research Articles

Comprehensive investigation of isotherm, RSM, and ANN modeling of CO2 capture by multi-walled carbon nanotube

Chemical vapor deposition was used to produce multi-walled carbon nanotubes (MWCNTs), which were modified by Fe–Ni/AC catalysts to enhance CO2 adsorption. In this study, a new realm of possibilities and potential advancements in CO2 capture technology is unveiled through the unique combination of cutting-edge modeling techniques and utilization of the recently synthesized Fe–Ni/AC catalyst adsorbent. SEM, BET, and FTIR were used to analyze their structure and morphology. The surface area of MWCNT was found to be 240 m2/g, but after modification, it was reduced to 11 m2/g. The modified MWCNT showed increased adsorption capacity with higher pressure and lower temperature, due to the introduction of new adsorption sites and favorable interactions at lower temperatures. At 25 °C and 10 bar, it reached a maximum adsorption capacity of 424.08 mg/g. The optimal values of the pressure, time, and temperature parameters were achieved at 7 bar, 2646 S and 313 K. The Freundlich and Hill models had the highest correlation with the experimental data. The Second-Order and Fractional Order kinetic models fit the adsorption results well. The adsorption process was found to be exothermic and spontaneous. The modified MWCNT has the potential for efficient gas adsorption in fields like gas storage or separation. The regenerated M-MWCNT adsorbent demonstrated the ability to be reused multiple times for the CO2 adsorption process, as evidenced by the study. In this study, a feed-forward MLP artificial neural network model was created using a back-propagation training approach to predict CO2 adsorption. The most suitable and efficient MLP network structure, selected for optimization, consisted of two hidden layers with 25 and 10 neurons, respectively. This network was trained using the Levenberg–Marquardt backpropagation algorithm. An MLP artificial neural network model was created, with a minimum MSE performance of 0.0004247 and an R2 value of 0.99904, indicating its accuracy. The experiment also utilized the blank spreadsheet design within the framework of response surface methodology to predict CO2 adsorption. The proximity between the Predicted R2 value of 0.8899 and the Adjusted R2 value of 0.9016, with a difference of less than 0.2, indicates a high level of similarity. This suggests that the model is exceptionally reliable in its ability to predict future observations, highlighting its robustness.

Bioplastic from empty fruit bunch cellulose/chitosan /starch: Optimization through box-Behnken design to enhance the mechanical properties

This investigation was centered on the intricate processes of fabricating and fine–tuning the concentration of microcellulose derived from oil palm empty fruit bunch (OPEFB). The objective was to employ it as a reinforcing agent, with sorbitol serving as a plasticizer, and chitosan acting as both a plasticizer and a strengthening agent for the formulation of bioplastic films to gain higher value mechanically. The cellulose extraction involved a methodical delignification approach, extracting lignin and hemicellulose from OPEFB to yield brown pulp. Subsequent double peroxide bleaching was employed to reduce the lignin concentration in the pulp. Leveraging the Box–Behnken experimental within a response surface methodology framework, the study scrutinized the influence of independent parameters (additive concentrations) on the ultimate tensile strength and Young’s modulus of the fabricated bioplastic films. Noteworthy variations in ultimate tensile strength and Young’s modulus were discerned, underscoring the impact of microparticle loading on the mechanical attributes of the bioplastic films. The quadratic polynomial model derived from experimental data exhibited coefficients of determination (R2) of 0.8690 for ultimate tensile strength and 0.6793 for Young’s modulus, affirming the adeptness of the models in navigating the optimization space. Microcellulose was isolated (30 – 40) wt.% of OPEFB as the white pulp which revealed a homogenous dispersion with chitosan in the corn starch matrix. The superior tensile strength and Young’s modulus were observed at 6.84 MPa and 21.94 MPa respectively.

Exploring waste marble dust as an additive in cementitious grouts for semi-flexible pavement applications: Analysis and optimization using RSM

This research aims to explore the potential utilization of waste marble dust (MD) within cementitious grouts for applications in semi-flexible pavements, a hybrid pavement type combining the benefits of both flexible and rigid pavements. MD was used to replace cement (ranging from 0 to 25%), combined with w/c ratios of 0.35 to 0.50, in the formulation of cementitious grouts. The assessment of grout characteristics encompassed evaluations of flowability and compressive strength (7d and 28d curing). Moreover, the microstructural characteristics of cement grouts incorporating MD were scrutinized through Scanning electron microscope-SEM and Energy dispersive X-Ray spectroscopy-EDS analyses. Additionally, response surface methodology (RSM) was used for experimental design, analysis, and the optimization of the factors (MD and w/c ratio). The significance of the model was examined through various statistical indicators. The optimal composition of cementitious grouts was determined using optimization technique within the RSM framework. Furthermore, optimal grout combinations were employed in fabricating semi-flexible mixtures and evaluated for Marshall stability, moisture susceptibility, indirect tensile stiffness modulus, dynamic modulus, compressive strength, and resistance to fuel spillage. The results indicated that an optimal combination of 15% waste marble dust and a 0.40 w/c ratio was identified through optimization techniques within the RSM methodology. Furthermore, Analysis of variance formula-ANOVA outcomes exhibiting a higher R² value (>0.80) and higher adequate precision (>4.0) underscore the significance of the proposed models, revealing a robust correlation between experimental and predicted data. The semi-flexible mixtures also showcase superior performance in terms of Marshall stability, stiffness, dynamic modulus, moisture resistance, and resistance to fuel spillage. Moreover, the integration of waste marble dust in cementitious grouts for constructing semi-flexible pavements not only addresses disposal concerns but also contributes to environmental sustainability.

Box–Behnken Design to Optimize Standardized Mangiferin-Rich Mango Peel Extract from Agro-Industrial Waste Product

The light mango or “Ma-Muang Bao” (Mangifere indica L. var.) is a native mango species originating from Malaysia and southern Thailand. The whole Ma-Muang Bao fruit, except peels, is popular as both a raw and ripe fruit for consumption, as well as being used in various processed food products. This study aims to transform the peel of a specific mango variety, which is a byproduct of the agro-industrial sweet, pickled mangoes industry, into a valuable bioactive ingredient for healthcare products. This is achieved through the establishment of a standardized mangiferin-rich mango peel extract (SMPE). Employing the Box–Behnken design (BBD) within the framework of response surface methodology (RSM), an optimal microwave-assisted extraction procedure was developed. A total of 27 experiments, each with four independent variables, including solvent ratio, extraction power, extraction time, and ethanol (EtOH) ratio, were conducted to optimize the extraction method in terms of mangiferin content and extraction yield. The optimized extraction conditions encompassed a solvent ratio of 120 mL EtOH/100 g sample, an extraction power of 450 W, an extraction time of approximately 4.3 min, and an EtOH ratio of 69.44% (EtOH in water). Small-scale extractions were carried out using the following specified parameters: solvent ratio of 120 mL, extraction power of 450 W, extraction time of 4 min, and EtOH ratio of 70% EtOH. These extractions yielded an extract with a mangiferin content of 27.24 ± 2.05 mg/g and an extraction yield of 3.71 ± 0.17% w/w. Notably, these outcomes were better from the mangiferin content of 19.62 mg/g and a yield of fresh peel of 5.61% estimated through BBD analysis. Furthermore, a pilot-scale extraction was performed using 7 kg of fresh mango peel and 70% EtOH (8.4 L) for 4 min, resulting in an extract with a mangiferin content of 51.85 ± 0.35 mg/g and a fresh peel yield of 4.35% w/w. This method emerges as the most suitable for mango peel extraction and forms the basis of the SMPE. The results from biological activities highlight the potential use of SMPE as the active ingredient for cosmeceutical or healthcare products for wound-healing and skin-brightening agents. Additionally, the knowledge from this study presents an alternative approach to various plant sources and sustainable extraction methods for the herbal extract industry.

Adsorptive removal of oxytetracycline using MnO2-engineered pine-cone biochar: thermodynamic and kinetic investigation and process optimization.

Indiscriminate use of oxytetracycline is linked to the development of antibiotic-resistant genes, posing a serious threat to human health and ecosystem balance. This article reports the adsorptive elimination of oxytetracycline (OTC) from aqueous solution using a newly developed MnO2-modified pine-cone biochar (MnO2/PCBC). The MnO2/PCBC was characterized using X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, CHNS analyzer, inductively coupled plasma-optical emission spectroscopy, and Brunauer-Emmett-Teller N2 adsorption analyzer. Batch adsorption experiments, designed using the central composite design framework of response surface methodology, were conducted to investigate the influence of process variables on the adsorption of OTC onto MnO2/PCBC. The optimized conditions for achieving maximum removal (88.1%) were found to be at pH 8, MnO2/PCBC dose 0.44 g/L, initial OTC concentration 200 mg/L, and temperature 303 K. The adsorption process follows Langmuir (R2=0.95) and Freundlich (R2=0.95) isotherms and pseudo-second-order (R2=0.99) adsorption kinetics. The adsorption process was found to be endothermic (ΔH0 = 33.04 kJ/mol) and spontaneous in nature (ΔG0 from -1.33 kJ/mol at 283 K to -5.65 kJ/mol at 313 K). The synthesized MnO2/PCBC could be recycled and reused for OTC removal with a percentage removal of around 80% after fifth cycle. The results indicate an effective removal of oxytetracycline with only 0.44 g/L MnO2/PCBC with maximum adsorption capacity of 357.14 mg/g which demonstrates improved performance in comparison to many adsorbents reported in literature. This implies that MnO2/PCBC offers potential to be developed into a cost-effective technique for antibiotic removal from water.

Optimization of QuEChERS Method for Antibiotic Residue Analysis in Animal Foods via Response Surface Methodology

The present study employed a modified QuEChERS method to systematically analyze the presence of fifteen quinolone and seven tetracycline antibiotic residues in local animal food. Additionally, a multi-level four-factor Box–Behnken design (BBD) within the framework of response surface methodology (RSM) was utilized to evaluate the factors impacting the detection efficiency of the sample pretreatment procedure. Optimization was performed via Design Expert® 10.0.3, and the factors, including the volume of the acetonitrile, the addition of formic acid, the duration of the extraction, and the addition of EDTA, were combined with experimental design until an optimal solution was reached. Finally, the sample was tested via ultra-high performance liquid chromatography-quadrupole-linear ion trap mass spectrometry (UPLC/MS/MS) in both multiple reaction monitoring (MRM) and enhanced product ion (EPI) scan modes on a QTRAP® 5500 instrument (AB SCIEX instruments, Framingham, MA, USA). The overall average recoveries from actual samples fortified with 22 antibiotics at three levels ranged from 73.8% to 98.5% based on the use of matrix-fortified calibration, with variations ranging from 5.80 to 12.4% (n = 6). The limits of detection and quantification were 0.3 μg kg−1 and 1.0 μg kg−1, respectively. Lastly, the modified method was applied to practical sample analysis in the daily risk monitoring and assessment of food safety with satisfactory stability and robustness.

Optimizing biogas production from palm oil mill effluent utilizing integrated machine learning and response surface methodology framework

This study presents a novel approach to optimize the anaerobic digestion of palm oil mill effluent (POME) for maximum biogas production on an industrial scale. Unlike most optimization studies which are limited to laboratory scale, this study utilized data from a real-world industrial setting to simulate the anaerobic digestion (AD) process. The processes include feedstock pre-treatment, biogas scrubbing, wastewater treatment, and sludge handling. The impact of process conditions such as hydraulic retention time (HRT), organic loading time (OLR), anaerobic sludge recycling ratio (RRAS), treatment effluent recycling ratio (RRTE), and reaction temperature on chemical oxygen demand (COD) removal and methane yield was analysed. An integration of response surface methodology (RSM) and artificial neural network (ANN) is applied to predict the optimal values for the process conditions. The results showed that the optimal values for HRT, temperature, OLR, RRAS, and RRTE are 38.58 days, 45 °C, 1.033 g COD/(L∙day), 0.052, and 0.95, respectively. The optimization improves the COD removal efficiency, SS removal efficiency, and biogas production by 7.38%, 8.37% and 16.18%, respectively when processing 24,134 L/h of POME feed. The optimized AD process is economically viable with a payback period of 5.34 years and a net present value (NPV) of $5,079,000.

Enhanced Ciprofloxacin Removal from Aqueous Solution Using a Chemically Modified Biochar Derived from Bamboo Sawdust: Adsorption Process Optimization with Response Surface Methodology

Contamination of water by ciprofloxacin has become a significant concern due to its adverse health effects and growing evidence of antimicrobial-resistant gene evolution. To this end, a chemically modified bamboo biochar was prepared from bamboo sawdust to effectively remove ciprofloxacin (CIP) from an aqueous solution. Under similar adsorption conditions, the modified bamboo biochar (MBC) has an excellent CIP removal efficiency (96%) compared to unmodified bamboo biochar (UBC) efficiency (45%). Thus, MBC was used in batch adsorption experiments, and the process was optimized with the central composite design (CCD) framework of response surface methodology (RSM). Sorption process parameters such as initial CIP concentration, pH, adsorbent dose, and contact time were studied and found to have a significant effect on CIP removal. The optimal CIP removal (96%) was obtained at MBC dose (0.5 g L-1), CIP initial concentration (20 mg L-1), pH (7.5), and contact time (46 min). The adsorption kinetic data were well described by the pseudo-second-order model ([Formula: see text]), and both Langmuir ([Formula: see text]) and Freundlich ([Formula: see text]) models gave the best fit in CIP adsorption isotherm analysis. The maximum monolayer adsorption capacity of the MBC was 78.43 mg g-1 based on the Langmuir isotherm model. These results suggest that CIP adsorption was mainly controlled by chemisorption. Moreover, the CIP adsorption process was endothermic and spontaneous. Overall, MBC is a low-cost, efficient, and recyclable adsorbent for eliminating emerging contaminants such as ciprofloxacin from an aqueous solution.

Optimisation of oven-drying of baobab leaves using a central composite design

Baobab (Adansonia digitata) leaves represent a key nutritional resource; although their consumption is apparently restricted to local communities, mainly as a sticky sauce, cosmetics and a variety of purposes. Ready-to-use powder of oven-dried baobab leaves can improve the availability of the product on markets, and hence its utilisation and shelf life. This study was carried out to optimise the oven-drying temperature and duration for the best conservation of baobab leaf powder and its sustainable availability for multipurpose uses. Different combinations of values of temperature and drying duration were generated, using a central composite design, in response surface methodology framework. Findings showed that dry matter, hue, chroma and lightness were significantly influenced by oven-drying temperature and duration; while the least gelation concentration was significantly influenced by the temperature. Based on leaf dry matter, hue and chroma models, the optimal oven drying conditions for baobab leaves for good preservation leading to human consumption and other purposes are set at 45 °C for 23.5 hours.

Optimisation of oven-drying of baobab leaves using a central composite design

Baobab (Adansonia digitata) leaves represent a key nutritional resource; although their consumption is apparently restricted to local communities, mainly as a sticky sauce, cosmetics and a variety of purposes. Ready-to-use powder of oven-dried baobab leaves can improve the availability of the product on markets, and hence its utilisation and shelf life. This study was carried out to optimise the oven-drying temperature and duration for the best conservation of baobab leaf powder and its sustainable availability for multipurpose uses. Different combinations of values of temperature and drying duration were generated, using a central composite design, in response surface methodology framework. Findings showed that dry matter, hue, chroma and lightness were significantly influenced by oven-drying temperature and duration; while the least gelation concentration was significantly influenced by the temperature. Based on leaf dry matter, hue and chroma models, the optimal oven drying conditions for baobab leaves for good preservation leading to human consumption and other purposes are set at 45 °C for 23.5 hours.

Evaporation rates and pollutants emission from heated cooking oils and influencing factors

The heating of edible oils during cooking activities promotes the emissions of pollutants that have adverse impacts on the health of humans. This study investigated the evaporative emissions of fifteen (15) commonly used cooking oils. Split-plot experimental design under the response surface methodology framework was used to study singular and interaction effects of influencing parameters (temperature, volume of cooking oil and time) on cooking oil evaporation rate and pollutants emissions (i.e. Particulate matter of aerodynamic diameter ≤1 μm (PM1.0); ≤2.5 μm (PM2.5); ≤10 μm (PM10); Total Suspended Particulate (TSP); Total Volatile Organic Compounds -TVOCs, and Carbon Monoxide- CO) on a groundnut oil sample that served as a case study. Obtained values of density, viscosity, kinematic viscosity, smoke, flash and fire points were; 873–917 kg/m3; 1.12–9.7 kg/ms; 2.4–3.4 m2/s; 96 -100 °C; 124–179 °C and 142–186 °C, respectively. The role of temperature as the most significant parameter influencing the rate of evaporative emissions was established. Evaporation rate and pollutants emission from unrefined samples were the highest. The restricted maximum likelihood (REML) analysis results suggested a strong relationship between the actual values and the predicted values as R-squared values obtained were greater than 0.8 for all the responses. These results suggest that minimal rates of evaporation and pollutants emission from heating cooking oils can be achieved with a high volume of the cooking oil at moderate temperature levels.

High performance on the moisture reduction in waste oils by a bentonite-based adsorption process

ABSTRACT In this work, the reduction of the moisture content from waste oils by a bentonite-based adsorption process was investigated as an alternative for reuse in energy production. Waste cooking oil (WCO) and waste poultry fat (WPF) were characterised (moisture and viscosity, values of acidity, saponification, and peroxide), as well as bentonite clay adsorbent. The response surface methodology (RSM) was applied to define the best moisture adsorption condition by bentonite-based adsorbent regarding a full factorial experimental design (FFED). In the framework of RSM, the adsorbent mass, oil acidity, and agitation speed were varied in three levels in the range of 1.5–3.0 g, 1–38 mgKOH goil −1, and 50–150 rpm, respectively, while the adsorption time was fixed in 24 h. Under the best moisture adsorption condition, kinetic tests were performed in a 1–24 h contact time range, besides performing equilibrium adsorption tests at 5 h contact time. Around 90% moisture removal was attained with 1.5 g adsorbent, acidity value of 1 mgKOH goil −1, and 50 rpm agitation speed, achieving the equilibrium in 5 h. A multilayer adsorption process, as described by the BET isotherm model, was acting in dried bentonite clay, allowing getting excellent moisture adsorption capacity (∼200 mgwater gBent −1). Therefore, moisture removal from waste oils by a bentonite clay-based adsorption process has provided results that are suitable and recommendable for economically viable biodiesel production.

Adsorptive Removal of Arsenic and Mercury from Aqueous Solutions by Eucalyptus Leaves

The study is a first-time investigation into the use of Eucalyptus leaves as a low-cost herbal adsorbent for the removal of arsenic (As) and mercury (Hg) from aqueous solutions. The adsorption capacity and efficiency were studied under various operating conditions within the framework of response surface methodology (RSM) by implementing a four-factor, five-level Box–Wilson central composite design (CCD). A pH range of 3–9, contact time (t) of 5–90 min, initial heavy metal (As or Hg) concentration (C 0) of 0.5–3.875 mg/L, and adsorbent dose (m) of 0.5–2.5 g/L were studied for the optimization and modeling of the process. The adsorption mechanism and the relevant characteristic parameters were investigated by four two-parameter (Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich) isotherm models and four kinetic models (Lagergren’s pseudo-first order (PFO), Ho and McKay’s pseudo-second order (PSO), Weber–Morris intraparticle diffusion, and modified Freundlich). The new nonlinear regression-based empirical equations, which were derived within the scope of the study, showed that it might be possible to obtain a removal efficiency for As and Hg above 94% at the optimum conditions of the present process-related variables (pH = 6.0, t = 47.5 min, C 0 = 2.75 mg/L, and m = 1.5 mg/L). Based on the Langmuir isotherm model, the maximum adsorption or uptake capacity of As and Hg was determined as 84.03 and 129.87 mg/g, respectively. The results of the kinetic modeling indicated that the adsorption kinetics of As and Hg were very well described by Lagergren’s PFO kinetic model (R 2 = 0.978) and the modified Freundlich kinetic model (R 2 = 0.984), respectively. The findings of this study clearly concluded that the Persian Eucalyptus leaves demonstrated a higher performance compared to several other reported adsorbents used for the removal of heavy metals from the aqueous environment.

Nanofibrous morphology of electrospun chitosan nanocomposites reinforced with WS2 nanotubes: A design-of-experiments study

Chitosan nanofibers reinforced with tungsten disulfide inorganic nanotubes (INT-WS2) were fabricated in this study. The aim was to investigate the effect of the material parameters and the electrospinning process parameters on the obtained nanofibrous morphology of the mats. The INT-WS2 content, the polymer solution concentration, the electric field strength, and the solution's flow rate were the investigated factors within the framework of response surface methodology. Scanning electron microscopic and image analysis were used for the dimensional characterization of the nanofibrous morphology and the estimation of three selected responses. Two responses were related to the quality of the nanofibrous morphology: the number surface density of the beads ( Nbead) and the average bead-to-fiber diameter ( Dbead/ Dfiber). The third response was indicative of the fiber thickness ( Dfiber). The developed models as well as the coupling and the individual effects of the four investigated factors are given. The results indicate that the electrospun nanofibrous morphology is mostly affected by the polymer solution concentration, the electric field strength and the INT-WS2 loading. Furthermore, the response-surface results reveal possible experimental pathways that may be followed in order to obtain specified nanofibrous chitosan/INT-WS2 morphologies.

Multiresponse problems: desirability and other optimization approaches

A diversity of multiresponse optimization methods has been introduced in the literature; however, their performance has not been thoroughly explored, and only a classical desirability‐based criterion has been commonly used. With the aim of contributing to help practitioners in selecting an effective criterion for solving multiresponse optimization problems developed under the response surface methodology framework, and thus to find compromise solutions that are technically and economically more favorable, the working ability of several easy‐to‐use criteria is evaluated and compared with that of a theoretically sound method. Four case studies with different numbers and types of responses are considered. Less‐sophisticated criteria were able to generate solutions similar to those generated by sophisticated methods, even when the objective is to depict the Pareto frontier in problems with conflicting responses. Two easy‐to‐use criteria that require less‐subjective information from the user yielded solutions similar to those of a classical desirability‐based criterion. Preference parameters range and increment impact on optimal solutions were also evaluated.

Analysis of process parameters related to the single-screw extrusion of recycled polypropylene blends by using design of experiments

The process dynamics of single-screw extrusion on mixtures of polypropylene (PP) and recycled PP were studied using a statistical, design of experiments (DoE) approach. For a conventional screw design, the barrel temperature, screw speed and two vastly different melt viscosity polypropylene mixtures were selected as the independent factors, whilst melt pressure, mass output, screw torque and temperature rise at the die due to shear heating were the dependent responses. A central composite design (CCD) in the framework of response surface methodology (RSM) was constructed, and an analysis of variance (ANOVA) was carried out to determine the significance of the response surface models. The resulting statistical and response surface predictions have demonstrated that the low viscosity component concentration in the blend is a dominating factor on melt pressure and screw torque, apart from the expected effect of screw speed on output. Viscous heating is affected only by screw speed and recycled polypropylene concentration. Furthermore, the predictions have identified a wider process operating window with increased low-viscosity component concentration. The data confirm that statistical tools make quantitative predictions for the effects of experimental process variables, in accordance with the expected qualitative trends towards process optimisation, providing scope towards its application in scaled-up industrial processes.

Gaussian Process Model – An Exploratory Study in the Response Surface Methodology

This paper explores the benefits of Gaussian process model as an alternative modeling technique for problems developed in the Response Surface Methodology framework. Three case studies with different type and number of responses were investigated, and the compromise solutions obtained with three modeling techniques were evaluated. Results provide evidences of the Gaussian process model usefulness for stochastic responses, namely, when responses are correlated. Copyright © 2015 John Wiley & Sons, Ltd.

Interpretation of non-linear empirical data-based process models using global sensitivity analysis

Flexible non-linear regression techniques have been widely used for data-based modeling of chemical processes, and they form the basis of process design under the framework of response surface methodology (RSM). These non-linear models typically achieve more accurate approximation to the factor–response relationship than traditional polynomial regressions. However, non-linear models usually lack a clear interpretation as to how the factors contribute to the prediction of process response. This paper applies the technique of sensitivity analysis (SA) to facilitate the interpretation of non-linear process models. By recognizing that derivative-based local SA is only valid within the neighborhood of certain “nominal” values, global SA is adopted to study the entire range of the factors. Global SA is based on the decomposition of the model and the variance of response into contributing terms of main effects and interactions. Therefore, the effect of individual factors and their interactions can be both visualized by graphs and quantified by sensitivity indices. The proposed methodology is demonstrated on two catalysis processes where non-linear data-based models have been developed to aid process design. The results indicate that global SA is a powerful tool to reveal the impact of process factors on the response variables.

Optimizing the nanofibrous morphology of electrospun poly[(butylene succinate)‐ co‐(butylene adipate)]/clay nanocomposites and revealing the effect of the fibre nano‐dimension on the attained material properties

AbstractElectrospun mats of biodegradable polymer/clay nanocomposites were prepared in order to investigate the qualitative and quantitative correlations between electrospinning‐related parameters and the fibrous morphology of the mats. A full factorial design of experiments was used for that purpose and scanning electron microscopy was employed for dimensional characterization and determination of the chosen responses. Statistical analysis revealed that solution concentration and clay loading of nanocomposite were the most important parameters affecting the morphology of the fibrous webs. This, subsequently, allowed prediction of the domain of these two parameter settings where purely fibrous morphology can be achieved and further allowed optimization of the process in the framework of response surface methodology (RSM). The structure and thermal behaviour of the nanocomposites were also characterized before and after electrospinning using X‐ray diffraction, thermogravimetric analysis and differential scanning calorimetry. A multi‐analyte platform is provided by the combination of RSM results for improving the nanofibrous quality and post‐spinning characterization for predicting the overall performance of the electrospun web. Copyright © 2011 Society of Chemical Industry

Multiple response optimisation: methods and results

In multi-response problems, optimal factor settings for one response may be far from optimal or even physically impractical for the other responses. A common approach for multi-response optimisation consists in searching for factor settings that provide the best trade-off among all the responses. To identify the method which can provide the best trade-off when optimisation is made in the response surface methodology (RSM) framework, various methods are reviewed and their performance assessed through examples from the literature. A new measure is proposed to evaluate the optimisation performance of methods which use different criteria and different approaches. Illustrative examples show that, in the RSM framework, it is possible to achieve the lowest cumulative deviation of the estimated responses from their target value by using desirability function-based methods. In particular, those solutions are achieved with a method that can be easily used by practitioners, which is an additional stimulus for its ap...