Response Surface Methodology Approach Research Articles

Bio-hydrogen production from crude glycerol: Optimisation through response surface methodology and artificial neural network approach

Crude glycerol (CG) is the main by-product of biodiesel production from the transesterification of vegetable or animal oils. The CG's high energy content makes it an effective carbon source for bio-hydrogen production. However, the purity of CG derived from the transesterification process may vary due to impurities such as methanol, soap, salt, etc., which influence the hydrogen production yield. The present study investigated the impacts of initial glycerol, methanol, and soap concentration of CG on the hydrogen production by dark fermentation, and the effect of various factors was evaluated and optimised with the response surface methodology (RSM) and artificial neural network (ANN) approach to maximise the yield of hydrogen production. Both the results of these approaches showed good fitting. The correlation coefficient between actual and predicted values from RSM and ANN models is closer to ∼1, and the lower error shows the applicability of these models. A maximum of 17.67 mmol/L of hydrogen production was obtained via RSM with optimum methanol, soap and glycerol concentrations of 2.75 g/L, 6.07 g/L, and 20.45 g/L, respectively. Besides, the 3-D response surface graphs illustrated the suppressive impact of increased initial concentration of methanol and soap, along with higher initial concentrations of glycerol, on hydrogen production. Further, the outcome of the controlled experiment shows that the presence of impurities (methanol and soap) in glycerol reduces the cumulative hydrogen production by 5 %–17 % compared to the control for over five days.

Physicochemical characterization of rice straw before and after alkali-assist photocatalytic pretreatment: a comparative analysis.

The potential of alkali-assist photocatalytic (AAP) pretreatment to overcome the recalcitrant nature of lignocellulose biomass, i.e. rice straw (RS), was investigated in the present study. Box-Behenken Design (BBD) using standard response surface methodology (RSM) approach was considered to obtain optimal conditions for maximum delignification. The model was designed with three variables: alkali concentration (NaOH, 0-3% w/v), photocatalyst (TiO2 NPs (titania nanoparticles), 0-1g/L) and pretreatment time (30-240min). The availability of cellulose was increased by 96.73%, while the concentration of lignin and hemicellulose decreased by 73.89%, and 71.79%, respectively, at a combination of 1.5% NaOH, 0.5g/L TiO2 NPsand 135 min pretreatment time. The structural and morphological alterations in the RS were assessed via Fourier Transform Infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscopy (SEM) both before and after AAPpretreatment. The FTIR measurement indicated that the original RS included a significant quantity of lignin, which was eliminated after the pretreatment procedure. The XRD pattern demonstrates that cellulose crystallinity is significantly affected by the pretreatment. The SEM analysis revealed structural distortion and surface porosity from the pretreatment procedure.

Hexavalent chromium adsorption from aqueous solution utilizing activated carbon developed from Rumex abyssinicus

The indiscriminate release of chromium-saturated effluent into water bodies has raised concerns regarding its effect on human health and environmental ecology. Therefore, this research aimed to remove hexavalent chromium from an aqueous solution utilizing activated carbon developed from Rumex abyssinicus. The adsorbent was prepared through chemical impregnation with H3PO4 and then thermally activated at 500 °C for 2 h. The optimization of the adsorption experiments was performed using the Box‒Behnken approach of response surface methodology. The characterization of the prepared activated carbon revealed good quality in terms of its amorphous structure, maximum specific surface area of 1722.9 m2/g and porous surface. Furthermore, the optimization results showed that a maximum removal efficiency of 95.97 % was attained at an optimum working pH of 3, a contact time of 60 min, an initial chromium concentration of 70 mg/L and an adsorbent dosage of 0.6 mg/100 mL. The Langmuir isotherm model was found to have the best fit with the experimental data. Moreover, the kinetics study revealed that a pseudo-second-order model fit the data best, indicating the occurrence of chemisorption. Overall, this study suggested that Rumex abyssinicus could be an effective adsorbent for removing hexavalent chromium from aqueous solutions.

Enhanced predictive optimization of methane dry reforming via ResponseSurface methodology and artificial neural network approaches: Insights using a novel nickel-strontium-zirconium-aluminum catalyst

This study investigates the molecular dynamics of methane dry reforming catalyzed by a novel nickel-strontium-zirconium-aluminum (5Ni+3Sr/10Zr+Al) catalyst, leveraging both Response Surface Methodology (RSM) and Radial Basis Function Neural Network (RBFNN) for predictive optimization. Focusing on the impact of operational parameters—hourly space velocity, reaction temperature, and CO2:CH4 mole ratio—on the conversion rates and formation of reaction components, we aim to predict optimal conditions and corresponding process variables. Through a comparison of a three-layer Feed Forward Neural Network, optimized at a 3:10:1 topology, with traditional RSM approaches, our findings highlight the superior predictive capabilities of ANN models. Notably, ANN demonstrated exceptional performance with Radj2and F_Ratio values significantly surpass those of RSM, alongside lower statistical error terms. This superiority is attributed to ANN's robust handling of nonlinear relationships between inputs and outputs, asserting its potential for enhancing predictive accuracy in chemical process optimization. At optimum predicted conditions like 1 CH4/CO2,750 °C reaction temperature, 12000 cm3g−1h−1 space velocity, NiSrZrAl outperformed with > 85 % CH4 and CO2 conversion with H2/CO ∼1 up to 20 h time on stream. Our research underscores the importance of integrating advanced modeling techniques for the efficient and accurate prediction of catalytic reactions, offering valuable insights for future applications in chemical engineering and catalysis.

Ultrasound-assisted extraction of bioactive compounds from goji berries: Optimization, bioactivity, and intestinal permeability assessment

Lycium barbarum L. berries have a remarkable chemical composition and extensive biological activities, being a valuable component of health and nutraceutical practices. Nevertheless, a deep insight on the intestinal permeation of the pro-healthy bioactive compounds is urgently needed to predict the real effects on human body. This study attempted, for the first time, to optimize the Ultrasound-Assisted Extraction (UAE) of goji berries using a Response Surface Methodology approach and establish the intestinal permeation of the principal pro-healthy compounds. The optimal extraction conditions were a solid:liquid ratio of 8.75 % for 56.21 min, using an intensity of 59.05 W/m2. The optimal extract displayed a remarkable antioxidant capacity, with LC/DAD-ESI-MS analysis unveiled a diverse phytochemical profile, encompassing different compounds (e.g. glu-lycibarbarspermidine F, 2-glu-kukoamine, rutin, 3,5-dicaffeoylquinic acid). The intestinal co-culture model demonstrated that glu-lycibarbarspermidine F (isomer 2) (73.70 %), 3,5-dicaffeoylquinic acid (52.66 %), and isorhamnetin-3-O-rutinoside (49.31 %) traversed the intestinal cell layer, exerting beneficial health-promoting effects.

Production and testing of mahua oil-based biodiesel synthesized through heterogeneous catalyst using experimental and numerical method.

A two-step treatment of mahua oil was conducted to synthesize mahua biodiesel using heterogeneous biomass-based catalyst derived from mahua shell. Mahua oil having higher free fatty acid (FFA) content (about 19%) was esterified to reduce the FFA content up to 1%. The esterification process was carried out using 200mL mahua oil, 5:1 molar ratio (methanol:oil), and 2.25 weight% of H2SO4 at a temperature of 60°C for 3h. Post esterification, a set of 16 experiments were created using a Box-Behnken design (BBD)-based response surface methodology (RSM) approach to conduct the transesterification of the esterified oil. Molar ratio, catalyst loading, reaction temperature, and reaction time were the four input variables chosen for the design of experiments. The optimized conditions for maximum biodiesel yield (87.7%) were found to be 14.88 molar ratio, 3.578% catalyst loading, 69.7°C reaction temperature, and 81.9min reaction time. The Diesel RK engine simulation tool which was experimentally validated for baseline diesel fuel was used for numerical simulation of mahua biodiesel. The performance, combustion, and emission behavior of mahua biodiesel analyzed using numerical simulation presented the sustainability of mahua biodiesel as an alternate fuel.

Optimization of pretreatment of potato waste for release of fermentable sugars for vodka production: response surface methodology approach

Optimization of pretreatment of potato waste for release of fermentable sugars for vodka production: response surface methodology approach

Linguistic response surface methodology approach to measure the quality of nonlinear frame‐pixel and bit place‐based video steganography

AbstractSteganography refers to the practice of hiding sensitive information inside seemingly unrelated data sets. Steganography in the video is one of the best methods available for hiding data without compromising the film's appearance. For improved security and compatibility, the traditional system uses different video steganography techniques with linear or precise positions. Traditional linear video steganography practices face vulnerability, a lack of security, limited embedding options, and inadequate compatibility. Here nonlinear frame(s) and pixel positions based information hiding techniques have been developed to overwhelm the following. Both the nonlinear frame positions and nonlinear pixel positions are selected for the video‐based steganography. In the beginning, the nonlinear frame positions are selected through the key and the key may be with any prescribed range and alphanumeric characters. A single or more frames may be selected through the key and that entirely depends upon the corresponding run‐through. Then the nonlinear pixel and bit positions are also selected through a similar key. The proposed method is also compared with some former techniques and gives a magnificent result. Furthermore, a security analysis of the suggested algorithm has also been conducted using the differential attack method. To validate the suggested method and ensure that it is accurate, the author of this article made use of a very specific and innovative methodology known as the linguistic response surface methodology (LRSM). This model is framed based on achieving a few steganography assessment measures like PSNR, SSIM, and MSE metric values after incorporating hidden text in various nonlinear frames' nonlinear pixel locations of the video. The analysis of the variance using LRSM for PSNR, SSIM, and MSE response reveals very substantial results with confirmation.

Using Response Surface Methodology Approach to Modeling and Optimization of the Combustion Process in Cement Kiln

AbstractIn this study, this process has been modeled and optimized using the design of experiment technique and response surface methodology (RSM). The RSM was employed to optimize the combustion process in the cement kiln. Kiln feed (X1 = 160–190 t h−1), fan speed (X2 = 840–870 rpm), kiln rotational speed (X3 = 2.8–3.4 rpm), kiln fuel rate (X4 = 3800–4150 L h−1), calciner fuel rate (X5 = 5700–6200 L h−1), pressure of kiln inlet (X6 = 98–102 Pa), and limestone saturation factor (LSF) (X7 = 94–95.8 %) as independent variables were investigated. The carbon monoxide and the kiln body temperature in RSM were considered as response variables. The absolute average error in the model predictions for the combustion process in the cement kiln, for the kiln body's temperature, is about 1.6 % and for carbon monoxide is about 15.5 %.

Optimization of parameters and formulation of numerical model employing GRA–PCA and RSM approach for friction stir welded Ti–6Al–4V alloy joints

In this work, an endeavour was made to determine the impact of the tool’s shoulder diameter, pin profile, rotational and traverse speeds on the mechanical properties of the friction stir welded namely Ti–6Al–4 V alloy joints. A total of 21 experiments were carried out based on the central composite design (CCD) concept of response surface methodology (RSM). A quadratic regression based numerical model was formulated to ascertain the relationship amidst the parameters of FSW process and the mechanical properties of the fabricated Ti alloy joints. Analysis of variance (ANOVA) was employed to confirm the importance of parameters and their interactive impacts. Optimized process parameter combinations were ascertained by grey relation based analysis (GRA) was coupled together with principal component analysis (PCA), a hybrid based approach. Single score of GRG based response was determined and GRG scores were ranked for all experiments. 1st rank was attained by the experiment no. 20 possessing a GRG score of 2.9989. Optimized values of 25 mm shoulder diameter having a taper cylindrical pin geometry employed at a tool traverse speed of 40 mm min−1, rotational speed of 1400 rpm resulted in the generation of flaw free Ti alloy joint possessing a largest tensile strength of 809.8 MPa, yield strength of 778.7 MPa and percentage of elongation of 6.9%. SEM based fractography of Ti alloy joint specimens announced that taper cylindrical pin geometry along with 25 m shoulder diameter of employed tool have an inevitable part in generating frictional heat in ideal volumes and a perfect stirring action during FSW process.

Exploring Oxytenanthera abyssinica as a sustainable alternative for pulp and paper production: A response surface methodology approach for optimized soda pulping

Oxytenanthera abyssinica has been investigated as a possible substitute raw material for pulp and paper production because of its long fiber (2.40 mm), larger fiber width (21.83 μm), and flexibility ratio (0.72), smaller Runkel ratio (0.35), large slenderness ratio (109.98), and thinner cell wall thickness (2.74 μm). A response surface methodology (RSM) method was applied to explore and identify the best conditions for soda processing of the O. abyssinica stem. Soda concentration, cooking temperature, and cooking time were used to optimize the process of pulp production and the Kappa number (KPN). Under optimal conditions such as 180 min cooking time, 20 % active alkali, and 165 °C cooking temperature, the highest pulp yield and the lowest KPN were 43.48 % and 19.9, respectively. The optimized pulp was used to make a paper sheet having mechanical characteristics of the tensile index (28.23 Nm/g), tearing index (10.70 mNm2/g), and Burst index (1.11 kPam2/g). This study demonstrates that O. abyssinica pulp can produce paper with mechanical properties of medium strength equivalent to that of paper using a more environmentally friendly pulping process such as soda pulping compared to kraft and sulfite pulping. In summary, it can be inferred that O. abyssinica holds significant promise as a substitute raw material for pulp and paper industries. However, it is recommended to conduct pilot plant studies for further exploration.

Comparative analysis of sliding wear behaviour of A356 nanocomposites using response surface methodology and artificial neural network approaches

Abstract The present study is aimed at analysing the predictive capacity of response surface methodology and artificial neural network of wear behaviour of A356/Al2O3 nanocomposites. In order to develop nanocomposites with different Al2O3 content the mechanical milling and powder metallurgy routes were adopted. The wear testing experiments were conducted using pin on disc tribometer to study the influence of parameters such as Al2O3 content, load, sliding speed and distance on wear loss. The testing was conducted based on the experimental design generated through Taguchi’s L27 technique. The response surface methodology and artificial neural network were used to predict the wear loss of A356 nanocomposites and comparative analysis was performed to analyse the predictive capability of these two techniques. Analysis of variance results showed significant influence of sliding speed on the wear loss while impact of sliding distance was minimal. The average relative error between the artificial neural network predicted and experimental value was 4.861% while for response surface methodology it was 9.307%. This comparative analysis indicated better predicting capacity for artificial neural network model. Worn surface analysis showed dominant abrasion and mild delamination as wear mechanisms for both unreinforced and nanocomposite samples.

Synergistic effect of nano silica and metakaolin on mechanical and microstructural properties of concrete: An approach of response surface methodology

Nano and micro materials are both effective individually in enhancing the mechanical and microstructural properties of concrete. The combination illustrates an iconic shift in material engineering to produce concrete with distinctive characteristics. In this work, the combined effect of Nano Silica (NS) and Metakaolin (MK) on the mechanical and microstructure properties were examined by conducting compressive strength, split tensile strength, and Field Emission Scanning Election Microscope (FE-SEM) with Energy-dispersive X-ray (EDX) analysis, X-ray Diffraction (XRD) spectroscopy, Fourier Transform Infra-Red (FTIR) spectroscopy, respectively. The concrete matrix was investigated by varying the concentration of NS and MK with different Water-Binder Ratio (WBR). Further, the Response Surface Methodology (RSM) technique was used to identify the complex interaction between NS (1–3 %) and MK (5–20 %) at different WBR (0.35, 0.40 and 0.45). The synergistic effect between NS and MK resulted in enhanced compressive and split tensile strengths of the concrete. The optimum mix proportion of the concrete was found to be 2 % NS and 12.5 % MK at a WBR of 0.4. The compressive strength enhancements for the optimum mix proportion were 35.75 %, 34.4 %, 33.1 %, and 32.5 %, while the split tensile strength enhancements were 26.56 %, 25.92 %, 20.95 %, and 17.8 %, respectively on the 7th, 28th, 56th, and 90th day. The RSM technique developed for all the mixes has been statistically validated to have less than 5 % of mean error. Moreover, the microstructural analysis revealed early hydration, formation of additional Calcium Silicate Hydrate (C-S-H) products, and presence of dense microstructure resulting in improved microstructural properties. The incorporation of NS and MK in the concrete changes the concrete’s configuration at the nano and micro levels, thus paving the way for the development of novel and sustainable construction materials.

Stability Assessment of Ultrasound-Assisted Dispersion of Boron as a High-Energy-Density Particle in Hydrocarbon Fuel: A Response Surface Methodology Approach

Stability Assessment of Ultrasound-Assisted Dispersion of Boron as a High-Energy-Density Particle in Hydrocarbon Fuel: A Response Surface Methodology Approach

Optimizing industrial waste in road construction: a response surface methodology approach

Optimizing industrial waste in road construction: a response surface methodology approach

Optimizing durability and performance in high-volume fly ash-based alkali-activated mortar with palm oil fuel ash and slag: A response surface methodology approach

While extensive research has been conducted on the production of binary or ternary binders incorporated into alkali-activated mortars (AAMs), significant efforts continue to be directed towards determining the optimal blends that not only address strength requirements but also durability criteria. The present study seeks to optimize highly durable AAMs subjected to harsh conditions that would most likely occur in the mortar during service, through both experimental and optimization modeling. A ternary binder system was developed with a high proportion of fly ash (FA) at 50%, 60%, and 70%, combined with ground granulated blast furnace slag (GBFS) and palm oil fuel ash (POFA) at varying levels from 0% to 30%. Visual inspection, compressive strength loss, and microstructure tests were used to evaluate the performance of the proposed AAMs with exposure to freezing-thawing cycles, wet-dry cycles, and an acidic environment for up to one year. Results indicated that the acid, wet-dry, and freezing-thawing resistances of the proposed mortar were satisfactory at the binder level (60% FA, 10% POFA, and 30% GBFS). Furthermore, the response surface methodology model's accuracy and robustness were attained, with mean absolute percentage error (MAPE) and scatter index (SI) values less than 0.11. It is recommended that a ternary binder of high-volume FA incorporating POFA and GBFS should be used in AAMs to minimize environmental problems, enhance durability performance, and reduce natural resource depletion.

Enhancing biodiesel yield and purification with a recently developed centrifuge machine: A response surface methodology approach

Biodiesel production processes, such as gravity settling, have limitations in terms of biodiesel yield, purification efficiency, operating time in the separation process, and more extensive equipment. Therefore, this study has focused on using a recently developed centrifuge machine for biodiesel separation to address these challenges due to its compact design, high efficiency, and simplicity. Additionally, this study aimed to optimize the separation efficiency of glycerol from biodiesel using a centrifuge machine, employing response surface methodology (RSM) with central composite design (CCD). The optimum conditions for separating glycerol from biodiesel via centrifuge machine are a rotation speed of 1800 rpm, a mixture flow rate of 192.25 ml/min, and a temperature of 55 °C, respectively. In optimum conditions, 94.52% separation efficiency was achieved. Biodiesel production can be improved, leading to higher yields and greater purity. The utilization of RSM proved valuable in determining the optimum conditions for separation. Furthermore, the machine successfully separated the biodiesel to meet ASTM D6751 and EN 14,214 standards. The results highlight the potential of the centrifuge machine for efficient and reliable biodiesel production, contributing to the advancement of the biodiesel industry.

Optimization of defoaming-flocculation-dewatering indices of earth pressure balance (EPB) shield muck using response surface methodology and desirability approach

In situ recycling is one of the most effective methods to dispose of earth pressure balance (EPB) shield waste muck with residual foaming agents with high moisture content. In this context, response surface methodology (RSM) was employed to quantify the effects of independent variables, including flocculant dosage, defoamer dosage, and muck drying mass (MDM) and their interactions on defoaming-flocculation-dewatering indices. The polymeric aluminum chloride (PACL) and hydroxy silicone oil-glycerol polypropylene ether (H-G) were selected as the flocculant and defoamer. The contents of surfactants and foam stabilizers in residual foaming agents were determined using the proposed empirical equation. The defoaming ratio, antifoaming ratio, turbidity, moisture content, filtration loss ratio, and fall cone penetration depth were considered as dependent variables. The accuracy of developed RSM models was verified by the analysis results of variance, residuals, and paired t-test. Combined with the desirability approach, an optimal mixing ratio of 0.078 wt% PACL, 0.016 wt% H-G, and 27.882 wt% MDM was recommended, leading to a defoaming ratio of 98.34 vol% for residual foams and a moisture content of 56.72 wt% for pressure-filtration cakes. Our findings were demonstrated to be able to provide useful guidance for prediction and optimization of the in situ recycling indicators of EPB shield waste muck in metro tunnel construction sites.

Development and optimization of a mechanized jackfruit processing unit for enhanced efficiency and commercial viability

AbstractThe jackfruit is the largest edible fruit but remains underutilized due to challenges such as sticky latex, labor‐intensive peeling/coring, and lack of mechanization. This study developed and evaluated a jackfruit peeling, coring, and cutting machine to enhance processing efficiency. Performance was modeled using response surface methodology (RSM) and artificial neural network (ANN). Three jackfruit sizes (small, medium, and large) and three machine speeds (90, 120, and 150 RPM) were evaluated for peeling time (26.1–50.3 s), peeling efficiency (71.6%–85.3%), coring time (15.5–29.9 s), coring efficiency (74.7%–96.0%), and bulb wastage (6.2%–17.6%). RSM showed high model adequacy (R2 ≥ 0.97) and ANN confirmed prediction reliability (R2 = 0.81–0.99; mean square error = 4.4–44.9). Increasing fruit size significantly increased peeling and coring times but decreased efficiencies. Machine speeds caused minor variations. Optimized conditions of 120 RPM fruit holder speed and 150 RPM corer speed gave maximum desirability (0.869). The machine had a payback period of 2 years and benefit–cost ratio of 2.32 versus 2.66 for manual peeling/coring. The mechanized jackfruit processing will promote enhanced utilization of this nutritious fruit.Practical applicationsThe mechanized jackfruit peeling‐coring‐cutting machine developed in this study has significant practical utility. By enabling efficient and rapid processing of jackfruits, the machine can help tap the underutilized potential of this highly nutritious and functionally beneficial fruit. The optimized machine parameters allow jackfruit processing industries to achieve higher throughput with reduced wastage, thereby boosting productivity and profits. Additionally, the mechanization facilitates value‐addition by enabling jackfruit utilization in various processed products like chips, flour, jam, etc. Further, the machine helps create livelihood opportunities in jackfruit value chains, as it reduces drudgery and enhances process efficiency as compared to manual methods. The simple fabrication and operation also enable adoption by farmer‐producer organizations, self‐help groups, and community‐based jackfruit processing enterprises. Overall, the mechanized solution provides an impetus for sustainable utilization of jackfruit, while also addressing issues like food loss, nutrition security, income support, and women empowerment. The practical insights on machine performance modeling using response surface methodology and artificial neural network approaches further facilitate quality improvements in equipment design.

Influence of Novel Al-Ni Electrodes on Roughness and Machining Time in Inconel 625 EDM Machining: A Comprehensive RSM Performance Analysis

In the context of Inconel 625 machining, this study explores the effects of novel Al-Ni nano electrodes on surface roughness and machining time. Thorough performance study was undertaken to clarify the complex connections between the machining settings, electrode composition, and final surface quality. Important insights were obtained via methodical experimentation and data analysis, which helps optimize the machining procedures for improved productivity and surface quality when milling Inconel 625. This research presents an experimental investigation of the effects of the Electric Discharge Machining (EDM) input parameters, namely current, pulse-on time, and pulse-off time, on the surface roughness and machining time output parameters. Inconel 625 was the material of choice for the work piece. Electric discharge machining oil was used as the dielectric fluid and aluminium nanocomposite as the tool electrode. Response surface methodology (RSM) approach was employed in the experimentation. ANOVA was used to optimize the parameters and obtain the lowest possible surface roughness (SR) and machining time. The findings show that pulse-off time is the most significant motivating factor for SR. The current is the main determining factor for machining time. The Central Composite Design approach was used for optimization.