Coupling Response Surface Methodology Research Articles

High potential extracts from cocoa byproducts through sonotrode optimal extraction and a comprehensive characterization

Cocoa pod husk (CPH) and cocoa bean shell (CBS) are by-products obtained during pre-processing and processing of cocoa beans. Several bioactive compounds have been identified in these by-products that can be used for commercial applications as a way to promote the circular economy. Therefore, the objective of this paper was to recover bioactive compounds from CPH and CBS by sonoextraction process, to determine the type, content, and antioxidant activity in optimized extracts. To achieve our purpose, an optimization strategy using Box-Behnken Design coupled response surface methodology (MRS) was applied. The extraction conditions were optimized. The results obtained for CBS were: TPC (193 mg GAE/g), TEAC (1.02 mmol TE/g), FRAP (1.02 mmol FeSO4/g) and ORAC (2.6 mmol TE/g), while for CPH, the reported values were: TPC (48 mg GAE/g), TEAC (0.30 mmol TE/g), FRAP (0.35 mmol FeSO4/g) and ORAC (0.43 mmol TE/g) under the optimized conditions: Time (XA): 15 min, Amplitude (XB): 80 %, Ethanol (XC): 50 %. The LC-ESI-qTOF-MS analysis results allowed the identification of 79 compounds, of which 39 represent the CBS extract, while 40 compounds were identified in CPH extract. To conclude, sonotrode based extraction could be considered as an efficient and fast alternative for the recovery of bioactive substances from CBS and CPH.

Performance assessment and optimization of the PEM water electrolyzer by coupled response surface methodology and finite element modeling

Proton exchange membrane water electrolyzer (PEMWE), integrated with renewable energy sources, is promising in green hydrogen production with its compact system design, high efficiency, and high-purity hydrogen production capacity. Renewable hydrogen production from PEMWE depends significantly on operational factors. This study optimized the process variables affecting the PEM electrolysis cell performance using the response surface methodology (RSM) of the Design-Expert 13 software (trial version) at a fixed temperature of 80 °C. Water flow rate, cell voltage, and bolt torque were defined as the input factors of the model, while hydrogen flow rate and current were considered as the responses of the model. The statistical analysis showed that cell voltage, torque, and cell voltage-torque interaction significantly affected the current. Besides, it has been observed that the water flow rate plays a vital role in the hydrogen flow rate. The optimum conditions for green hydrogen production were a water flow rate of 2.45 mL/min, a cell voltage of 2.2 V, and a bolt torque of 10 Nm. In addition, a good match between the predicted and measured responses of the model was observed. This paper also includes finite element (FE) modeling and simulation of an experimental PEMWE. Based on the FE method, a three-dimensional simulation model of the PEMWE was established to investigate the effects of different bolt torques on the mechanical responses of the cell components. Numerical simulations were performed with a commercial code (ANSYS Mechanical). A pressure-sensitive film was placed instead of the catalyst-coated membrane (CCM) component of the single-cell PEMWE. The experimental results obtained from pressure-sensitive films were compared with the numerical results, and it was observed that the results were in agreement with each other. The maximum equivalent stress on the CCM for a bolt torque of 10 Nm is around 116.3 MPa.

Modeling and optimization study on degradation of organic contaminants using nZVI activated persulfate based on response surface methodology and artificial neural network: a case study of benzene as the model pollutant

Due to the complicated transport and reactive behavior of organic contamination in groundwater, the development of mathematical models to aid field remediation planning and implementation attracts increasing attentions. In this study, the approach coupling response surface methodology (RSM), artificial neural networks (ANN), and kinetic models was implemented to model the degradation effects of nano-zero-valent iron (nZVI) activated persulfate (PS) systems on benzene, a common organic pollutant in groundwater. The proposed model was applied to optimize the process parameters in order to help predict the effects of multiple factors on benzene degradation rate. Meanwhile, the chemical oxidation kinetics was developed based on batch experiments under the optimized reaction conditions to predict the temporal degradation of benzene. The results indicated that benzene (0.25 mmol) would be theoretically completely oxidized in 1.45 mM PS with the PS/nZVI molar ratio of 4:1 at pH 3.9°C and 21.9 C. The RSM model predicted well the effects of the four factors on benzene degradation rate (R2 = 0.948), and the ANN with a hidden layer structure of [8-8] performed better compared to the RSM (R2 = 0.980). In addition, the involved benzene degradation systems fit well with the Type-2 and Type-3 pseudo-second order (PSO) kinetic models with R2 > 0.999. It suggested that the proposed statistical and kinetic-based modeling approach is promising support for predicting the chemical oxidation performance of organic contaminants in groundwater under the influence of multiple factors.

Deep eutectic solvent-based ultrasound-assisted emulsification microextraction for the rapid determination of benzotriazole and benzothiazole derivatives in surface water samples

This work describes a simple and environmental-friendly method for the simultaneous determination of five benzotriazole derivatives (BTRs) and four benzothiazole derivatives (BTs) that are frequently found in surface water. The target analytes were efficiently extracted from water samples using a “green” deep eutectic solvent (DES) as the extraction solvent based- ultrasound-assisted emulsification microextraction (DES-USAEME), and their determination were performed by ultrahigh-performance liquid chromatography and electrospray ionization (+)-quadrupole time-of-flight mass spectrometry (UHPLC-ESI(+)-QToF-MS). The DES was composed of a mixture of choline chloride and phenol (molar ratio 1:2). The DES-USAEME factors were optimized by a Box-Behnken Design coupled response surface methodology. The developed method was validated, providing limits of quantitation (LOQs; 02 μg L−1), high precisions (1–8%), and satisfactory mean spiked recoveries (72–104 %). Relatively high total concentrations of the target analytes were found in samples collected from a reservoir (47.2–101.3 μg L−1), which may have been released from tire-wear particles and scrap tires from buses and old tires that were strung alongside the shuttle boats to prevent the boats from coming into contact with each other or from impacting against the dock during docking. This is the first study on the occurrence of BTRs and BTs in reservoir samples.

Process intensification of 2-ethylhexyl caprylate/caprate synthesis via a pulsed loop reactor: Multi-objective optimization

This work proposes the optimization of 2-ethylhexyl (2-EH) caprylate/caprate synthesis in a pulsed loop reactor, and the energy efficiency of the reactor was assessed. 2-EH caprylate/caprate can be used as a biodegradable base fluid. Only 0.4 wt% catalyst concentration was used in the reaction. The face-centred central composite design (FCCD) experiment coupled response surface methodology (RSM) has been implemented to screen independent parameters. The optimization process showed that vacuum pressure was the most influential factor followed by oscillation speed, temperature and time. The maximum conversion of 98 % and yield of 89 % were achieved under optimal conditions of 78 °C, 10 mbar, 20 min and 155 rpm. Meanwhile, energy balance calculation indicated that the usage of low vacuum pressure not only significantly reduced the total heat duty from 1381 kJ (1 atm) to 823 kJ (10 mbar) but also alleviated the reaction temperature from 172 °C (1 atm) to 78 °C (10 mbar). The calculation also demonstrated that the reflux-stirred tank reactor produced a lower conversion and yield of ester yet 6 kJ higher total heat duty compared to the pulsed loop reactor, indicating a cost-saving with the pulsed loop reactor.

Evaluation of culture medium on poly(3-hydroxybutyrate) production by Cupriavidus necator ATCC 17697: application of the response surface methodology

Polyhydroxyalkanoates (PHA), of which polyhydroxybutyrate (PHB) is the most abundant, are polymers of bacterial origin used for various applications in the medical, industrial and agricultural fields. In the present study we worked on the selection, evaluation and improvement of the significant variables of the medium for the production of PHB by Cupriavidus necator ATCC 17697. In order to address the selection of the main factors and optimize the culture medium, a complete factorial experimental design based on the coupled response surface methodology, was presented. The model with the best adjustment of the variables turned out to be quadratic in fructose (C), linear in ammonium sulphate (N) and pH, with interaction in pH and phosphate solution (P), where the pH was the most significant (p < 0.0001) while the micro-elements solution could be neglected. Thus, optimum carbon concentration, adequate nitrogen limitation and interaction between initial pH and phosphate solution concentration are important factors to ensure a high production of PHB. The optimal values of the selected variables were C = 20 g/l, N = 1.5 g/l, P = 8.75 g/l and pH 7.5. A maximum PHB production of 4.6 g/l, obtained under these conditions, increased almost 2.5 times. The polymer accumulated in the cytoplasm of C. necator ATCC 17697 in the form of granules showed an FTIR spectrum corresponding to that of commercial PHB.

Multi-objective Optimal Design of a 2-DOF Flexure-Based Mechanism Using Hybrid Approach of Grey-Taguchi Coupled Response Surface Methodology and Entropy Measurement

A large displacement and a high first natural frequency are two main concerns for any flexure-based positioning system. A two-degree-of-freedom (DOF) flexure-based mechanism (FBM) with a modified double-lever amplification mechanism is first designed. This study then proposes a multi-objective optimal design of the 2-DOF FBM using the hybrid approach of grey-Taguchi coupled response surface methodology and entropy measurement. The design variables of the 2-DOF FBM include the thickness of the flexure hinges and the length of lever amplification, both of which play vital roles in determining quality responses. The quality responses of the 2-DOF FBM are assessed by measuring the displacement and first natural frequency. The experimental plan is carried out using the Taguchi \({L_{25}}\) orthogonal array. An integrated approach of grey-Taguchi- based response surface methodology and entropy measurement is then applied for the multi-objective optimization of the 2-DOF FBM. To illustrate the relation between the design variables and the output responses, mathematical regression models are developed. The entropy measurement technique is applied to calculate the weight factor corresponding to each response. Then, an analysis of variance (ANOVA) is conducted to determine the significant parameters affecting the responses. In addition, the ANOVA and experimental validations are conducted to validate the statistical adequacy and the prediction accuracy of the developed mathematical models, respectively. The results reveal that the regression models have good statistical adequacy and excellent prediction accuracy. The confirmation results of the grey relational grade fall within 95 % of the confidence interval. It is strongly believed that the proposed approach has great potential for the optimal design of related flexure-based mechanisms.

Analysis of Cutting Forces and Optimization of Cutting Parameters in High Speed Ball-end Milling Using Response Surface Methodology and Genetic Algorithm

The stability of the machine tool mainly depend on the cutting force in machining process. In the present study, the influence of cutting speed, feed per tooth, axial depth of cut and radial depth of cut during high speed ball-end milling of Al2014-T6 under dry condition has been studied. Response surface methodology was used for generating the experimental plan for conducting the experiment. The cutting force components i.e. tangential and radial forces were measured and then analysis of variance (ANOVA) was performed. It has been found that the quadratic model is best fitted for prediction of the force components. The analysis of result shows that the cutting forces increases as increase in feed per tooth and axial depth of cut but decreases with increase in cutting speed. Radial depth of cut has significant effect on the cutting force components. The optimization of cutting force components was done by coupling response surface methodology and genetic algorithm. The optimization process was performed in MATLB.

A coupling method of response surfaces (CRSM) for cutting parameters optimization in machining titanium alloy under minimum quantity lubrication (MQL) condition

This paper presents a new flexible method referring to coupling response surface methodology (CRSM) to acquire optimum cutting parameters in machining of difficult-to-cut titanium alloy under minimum quantity lubrication (MQL) condition. Cutting speed, feed rate and depth of cut are designed as three factors by using the Taguchi method, which are also subject to several constraints including processing efficiency, cutting force and surface quality. In addition, turning experiments of titanium alloy were performed in order to set up the model based on Taguchi experimental design and multiple regression method. ANOVA was carried out to prove the adequacy of the developed mathematical model and the influences of cutting parameters on constraints were also observed. The results indicated that the feed rate was the dominant factor affecting surface roughness and cutting forces, which were minimized when the feed rate and depth of cut were set to the lowest level, while cutting speed was set to the highest level. Moreover, the predicted values obtained by CRSM were in good agreement with the experimental values, which indicated that CRSM was an effective method to determine optimum cutting conditions for machining process under MQL condition.

Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying

In this study, an integrated response surface methodology (RSM) and genetic algorithm (GA) are recommended for developing artificial neural networks (ANNs) with great chances to be an optimal one. A multi-layer feed forward (MLFF) ANN was applied to correlate the outputs (energy and exergy) to the four exogenous inputs (drying time, drying air temperature, carrot cubes size, and bed depth). The RSM was used to build the relationship between the input parameters and output responses, and used as the fitness function to measure the fitness value of the GA approach. In the relationship building, five variables were used (number of neurons, momentum coefficient and step size in the hidden layer, number of epochs and number of training times). A polynomial model was developed from training results to mean square error (MSE) of 50 developed ANNs to generate 3D response surfaces and contour plots. Finally, GA was applied to find the optimal topology of ANN. The ANN topology had minimum MSE when the number of neurons in the hidden layer, momentum coefficient, step size, number of training epochs and training times were 28, 0.66, 0.35, 2877 and 3, respectively. The energy and exergy of carrot cubes during fluidized bed drying were predicted with R 2 values of greater than 0.97 using optimal ANN topology.

Optimization of laccase mediated biodegradation of 2,4-dichlorophenol using genetic algorithm

The present investigation focuses on the development of an effective strategy to determine the optimum environmental conditions leading to the maximum rate of biodegradation of 2,4-DCP by coupling response surface methodology (RSM) with a developed genetic algorithm (GA) thereby ensuring minimum contact time. RSM is utilized to create an efficient analytical model for biodegradation of 2,4-DCP in terms of environmental parameters: pH, temperature, enzyme activity and time of incubation. For this purpose, a number of degradation experiments based on statistical three-level Box Behnken design methods were carried out. An effective response surface (RS) model is developed by carrying out experiments designed using the Box Behnken method. The RS model thus developed is further interfaced with the GA to optimize the degradation conditions for optimum degradation with minimum contact time. The GA increases the biodegradation conditions to >99% within a time period of 8 h within the given range of experimental conditions. The conditions obtained from GA were verified experimentally.

Application of response surface methodology in the optimization of cutting conditions for surface roughness

This paper focuses on the development of an effective methodology to determine the optimum cutting conditions leading to minimum surface roughness in milling of mold surfaces by coupling response surface methodology (RSM) with a developed genetic algorithm (GA). RSM is utilized to create an efficient analytical model for surface roughness in terms of cutting parameters: feed, cutting speed, axial depth of cut, radial depth of cut and machining tolerance. For this purpose, a number of machining experiments based on statistical three-level full factorial design of experiments method are carried out in order to collect surface roughness values. An effective fourth order response surface (RS) model is developed utilizing experimental measurements in the mold cavity. RS model is further interfaced with the GA to optimize the cutting conditions for desired surface roughness. The GA reduces the surface roughness value in the mold cavity from 0.412 μm to 0.375 μm corresponding to about 10% improvement. Optimum cutting condition produced from GA is verified with the experimental measurement.