Use Of Response Surface Method Research Articles

Response surface method-based efficient approach for the load and resistance factors calibration of breakwater foundation considering soil spatial variability

This study aims to propose an efficient approach to calibrate the load and resistance factors (LRFs) for the limit state design of breakwater foundations. The spatial variability of soil was considered by employing the Cholesky decomposition technique. To overcome the low computational efficiency of the Monte Carlo simulation (MCS), a response surface method (RSM) was utilized. Additionally, an efficient algorithm, namely the adaptive boundary-based particle filtering algorithm (ABB-PFA), was proposed with the use of RSM to automatically and quickly search optimal nominal values of random variables in the LRF calibration process. Noticeably, the optimal number of particles and values of the modification factor used in the ABB-PFA were observed to enhance the efficiency of the calibration process. The results showed that the computational efficiency of the calibration process is significantly increased by utilizing the response surface method and the proposed ABB-PFA. The efficiency increases the most if the number of particles is one and the value of the modification factor is 0.5 or 0.6. The influences of the random field element size on the efficiency of the calibration process were also investigated. By using the appropriate size, the efficiency is significantly increased while still obtaining a high accuracy of the LRFs calibration process.

Response Surface Methodology: The Improvement of Tropical Residual Soil Mechanical Properties Utilizing Calcined Seashell Powder and Treated Coir Fibre

Calcined seashell (CSS) powder and treated coir fibre (CF) are well-established additives for reinforcing poor soils. However, the absence of specific mix designs to optimize the mix additives makes it difficult to predict their combined effect on improving the mechanical behaviour of poor soils. This research explores the use of response surface methods to find the optimal proportions of CSS and CF for enhancing the mechanical properties of a tropical residual soil. This study uses a combination of Analysis of Variance (ANOVA) and regression models to examine how the independent variables of the CSS content, CF content, and curing duration influence the responses of the Unconfined Compressive Strength (UCS), Flexural Strength (FS), and Indirect Tensile Strength (ITS). The findings show that the optimal mix of 9.06% CSS, 0.30% CF, and 12 days of curing significantly improved the UCS, FS, and ITS by roughly six, four, and three times, respectively. Microstructural analysis revealed that the formation of calcium-aluminate-hydrate and calcium-silicate-hydrate are the primary components responsible for the enhanced mechanical properties of the treated soil.

Statistical Modeling, Optimization and Sensitivity Analysis of Tool’s Geometrical Parameters on Process Force in Automatic Cortical Bone Drilling Process

One of the most prevalent machining processes in medical treatments is bone drilling process. During bone drilling, excessive process force can cause breakage, crack initiation and severe damage to bone tissue. In this paper, a systematic study with simultaneous use of response surface method, sensitivity analysis based on Sobol method and regression analysis is performed for investigation the effect of helix angle and point angle of the tool as the most important geometrical parameters on imposed force to the bone during drilling process. Initially, using design of experiments and response surface method, imposed force to the bone is modeled and the governing second order linear regression equation is derived and verified. Then, using Sobol sensitivity analysis, with ability to quantify the sensitivity, it is attempted to investigate the effect of input parameters on drilling force. Finally, optimization of the process inputs is followed to find the best combination which yields the desired drilling force. The minimum drilling force, within the range of input parameters, coincides with point angle of 90 and helix angle of 18. This minimal force is lower than the force in surgery and standard tools. The results showed that an increasing in point angle leads to an increase in drilling force. Also, it is concluded that there is an optimum value for using the helix angle in bone drilling process with minimum imposed force.

Optimization of indole-3-acetic acid using response surface methodology and its effect on vegetative growth of chickpea

This study investigates the optimization of various environmental and nutritional factors for the best indole acetic acid (IAA) producing isolate using Box-Behnken Design (BBD) with Response Surface Method (RSM) and examines its effects on early plant growth parameters of Cicer arietinum (chickpea). Firstly, the screening of putative Bacillus isolates based on IAA producing capability and analysis of other plant growth promoting attributes was carried out. The highest IAA producing isolate MRN16 was selected to optimize IAA production based on the BBD and a total of 86 sets of experiments were finalized. The incubation temperature of 35 °C, pH of 7.0, incubation time of 8 days, 1.0 g/L tryptophan, 20 g/L starch, 0.5 g/L KNO3, were found the optimal conditions for the maximum IAA production. The extracted or purified IAA was examined by thin layer chromatography (TLC), showed a red colored spot with an 0.75 Rf value. IAA was determined using high-performance liquid chromatography (HPLC) and peak was observed at 6.66 min retention time, comparable with the standard IAA. The greenhouse assay confirmed that MRN16 was a significant isolate for enhancing chickpea vegetative growth parameters such as root/shoot length and weight (fresh/dry). Finally, isolate MRN16 was identified as Bacillus altitudinis using 16S rRNA sequencing. The present research establishes successful use of RSM, as an easy and useful tool to determine the optimized parameters for maximum IAA production in bacteria and further the utilization of bacteria to upsurge the plant growth.

The nature of flammable cloud volumes in semi-confined environment under the influence of flow of air

In the Explosion Risk Analysis (ERA), ventilation and dispersion calculations using Computational Fluid Dynamics (CFD) are usually used when the level of confinement and congestion cannot be neglected. As far as the dispersion analysis is considered, alternative approaches are sought when a large number of simulations is required. Setting many scenarios and simulate them all is not always suitable within the time-frame of real engineering design. As a result, semi-empirical dispersion models and several procedures based on statistical approaches using CFD have been proposed by other researchers to improve the robustness and the accuracy of prediction of the flammable gas cloud volume. In addition, despite the use of Response Surface Method (RSM) and Frozen Cloud Approach (FCA), it is convenient to address the problem on the basis of the physics underlying the dispersion of a scalar in the chemical process area. In this work we developed a novel mathematical approach for dispersion calculations based on CFD data. We propose a new dimensionless number balancing the transport of the flammable cloud and the accidental leak rate. Numerical experiments have shown that the dimensionless number is related to the angle of the wind direction as the angles in a circle are related to the phase angle in a sine wave Vˆ≈sin(kφπ+β), where Vˆ is the non-dimensional flammable cloud volume and φ the phase angle. The effect of wake wind is investigated and there seems to be no preferable wind direction that leads to the largest cloud volume in the module for the geometry considered in the current research.

Numerical optimization methods comparison based on the CFD conduction-convection heat transfer case

PurposeIn some types of industries, the best possible design, in terms of thermal and flow performance, determines the success or failure of the company. This applies, among others, to the sectional elements that form doors, windows or prefabricated building walls. The purpose of this paper is to show the possibilities and limitations of the different response surface methods for optimization in the case where natural convection phenomenon appears inside the sectional structures.Design/methodology/approachA three-layered wall with air gap is used as a cross-section heat flow model. Response surface algorithms for optimization, which can be found in commercial software, e.g. ANSYS/WORKBENCH, can help to optimize geometrical structure of components to achieve bigger or smaller heat flux value. In this paper, the optimization methodology of the design of experiments (DOE) and different response surface (RS) methods are used.FindingsOptimal results obtained with the use of genetic aggregation, standard RS, Kriging, non-parametric regression and neural network methods are compared with direct CFD and analytical calculations. Different limitations and advantages of the RS methods make individual methods more appropriate for different issues. For a properly defined optimization problem, the heat flux value approximated for the optimal geometry agrees with the direct CFD simulations.Practical implicationsThe presented investigations show how to use response surface optimization methods for thermal optimization of the sectional elements and their applications to obtain reliable results.Originality/valueThis paper presents the value of the use of RS methods in CFD-based geometry optimization. The study also shows that the RS optimization methods can approximate thermal properties under natural convection development conditions.

Optimization of pretreatment condition for ethanol production from Cyperus difformis by response surface methodology.

This study investigated the potential of a new material, so-called small-flowered nutsedge, for bioethanol production. This plant causes the huge loss of rice yield as it competes nutrients, sunlight and other necessary elements with rice plant to grow. The project plans to transform its biomass into valuable product and bring profitable for famers. The powered raw sample was treated with sodium hydroxide (NaOH) and hydrogen peroxide (H2O2) as followed the design experiment. The use of response surface method is helping researcher to save time and effort but still gain meaningful predicted value that closed to the actual value. The highest total sugar was given when the pretreatment condition is solid to liquid ratio of 0.05, 1% NaOH, 1% H2O2 for 72h. The efficiency of hydrolysis can reach 47% after 24h with cellulase enzyme at 50°C, 150rpm and highest ethanol concentration was obtained on the fifth day of fermentation.

The Use of Response Surface Method in Optimization of Levan Production by Heterologous Expressed Levansucrase from Halophilic Bacteria Bacillus licheniformis BK2

Response surface method (RSM) is a recent common method used to identify culture condition for optimal production of particular metabolite. In the present study, RSM is used to optimize a catalytic reaction in levan production by heterologous expression of lsbl-bk2 gene isolated from halophilic bacteria Bacillus licheniformis. Levan is a polyfructose polymer produced from sucrose by the action of extracellular levansucrase secreted by the microorganism. Three factors for levan production, namely sucrose concentration, pH, and temperature of reaction were optimized by full factorial and central composite designs in RSM. The result indicated that the optimum in vitro condition for levan production was achieved when the levansucrase catalytic reaction was performed at 32°C, pH 8, in 12% (w/v) sucrose solution. Levan produced by this procedure was verified by FTIR and NMR spectroscopies.

Optimization of fermentation conditions for an Escherichia coli strain engineered using the response surface method to produce a novel therapeutic DNA vaccine for rheumatoid arthritis

BackgroundFermentation condition optimization and nutrients screening are of equal importance for efficient production of plasmid DNA vaccines. This directly affects the downstream purification and final quality and yield of plasmid DNA vaccines. The present study aimed to optimize the fermentation conditions for high-throughput production of therapeutic DNA vaccine pcDNA-CCOL2A1 by engineered Escherichia coli DH5α, using the response surface method (RSM).ResultsWe hypothesized that optimized fermentation conditions significantly increase the yield of pcDNA-CCOL2A1 therapeutic DNA vaccine, a novel DNA vaccine for treating rheumatoid arthritis (RA). Single-factor analysis was performed to evaluate the optimal basal culture medium from LB, 2 × YT, TB, M9 (Glycerol) and M9 (Glucose), respectively. Thereafter, the Plackett-Burman design (PBD) was used to ascertain the three most significant factors affecting the vaccine yields, followed by the paths of steepest ascent to move to the nearest region of maximum response. Initial screening through the PBD revealed that the most key factors were peptone, mannitol, and inoculum concentration. Subsequent use of RSM was further optimized for the production of therapeutic DNA vaccine pcDNA-CCOL2A1 through Box-Behnken design (BBD). The final optimized fermentation conditions were as follows: peptone, 25.86 g/L; mannitol, 8.08 g/L; inoculum concentration, OD = 0.36. Using this statistical experimental design, the yield of therapeutic DNA vaccine pcDNA-CCOL2A1 markedly increased from 223.37 mg/L to339.32 mg/L under optimal conditions, and a 51.9% increase was observed compared with the original medium.ConclusionsThe present results provide a basis for further production of high-quality and high-yield therapeutic DNA vaccine pcDNA-CCOL2A1 in pilot-scale and even industrial-scale.

The use of response surface method to optimize the extraction of natural dye from winery waste in textile dyeing

Grape pomace, which is a winery waste is studied and experimented to serve as a source of natural dyes that could be utilized in textile dyeing. The present study aims at developing and optimizing an extraction method of natural dye derived from grape pomace in order to dye silk fabrics. The water-acidified ethanol (20–40%) mixture is used mainly for the extraction of anthocyanins at different durations and at different temperatures (20–40 °C) from grape pomace. Effects of these independent variables (percentage of acidified ethanol, temperature, and duration of extraction) and their interactions during the extraction are determined by response surface methodology based on three-level three-factorial Box–Behnken design. Optimized values of percentage of acidified ethanol, temperature, and the duration of extraction for the optimization of monomeric anthocyanins concentration (75 mg L−1) are found as 40%, 32,7 °C, and 38 h, respectively. The extract has been used for silk fabrics dyeing. The effects of some parameters (temperature, pH, and salt addition) on dyeing silk fabrics have been studied and optimized. The use of tannic acid as a natural mordant to improve the dye fastness proprieties has been investigated.

A hybrid approach combining uniform design and support vector machine to probabilistic tunnel stability assessment

Applications of the reliability-based method to stability evaluation of tunnel structures have become an ever-increasing concern over recent years. One critical challenge in conducting such a task is the implicit nature of the limit state function (LSF). To address this issue, the focus of this study is on, among others, the use of response surface method (RSM) by considering both the selection of the sampling method and the choice of the response surface form (as two major factors affecting the RSM’s performance). In this context, the current paper develops for tunnel-reliability analysis a hybrid approach combing an experimental design called uniform design (UD) and a regression device known as support vector machine (SVM). For the proposed hybrid approach, the UD is used to generate sampling points and then the SVM is employed to construct the response surface approximating the original inexplicit LSF. Such an approach integrates the merits of both UD and SVM used for complex nonlinear modelling. Three carefully selected tunnel examples are illustrated: one for a typical tunnel under relatively simplified tunnelling conditions and the other two for real-life tunnels. Comparisons are made to validate the computational accuracy and efficiency of the present approach. In particular, for the tunnel example where the LSF is known only implicitly through the numerical analyses (which is the scenario of many real-world applications in tunnel community), the obtained results further demonstrate the efficiency of this approach: it can be much more economical to achieve reasonable accuracy than the conventional RSMs when a small number of sampling data is used. Such comparisons made in this work verify the application potential of the developed hybrid approach for probabilistic tunnel stability assessment involving the implicit LSF.

Use of response surface method for maximizing the production of arginine deiminase by Pseudomonas putida

Statistically designed experiments were used to optimize the production of arginine deiminase (ADI) by Pseudomonas putida KT2440 in batch culture. A Plackett-Burman design involving eleven factors showed that ADI production was most influenced by the initial pH and the initial concentrations of glucose and yeast extract. A central composite experimental design showed that the optimal values of these factors were 8.0, 10g/L and 12.5g/L, respectively. The other components of the optimal culture medium were bacto peptone 7.5g/L, Triton X–100 0.30% (v/v), and arginine 3g/L, for a culture temperature of 25°C. Compared with the basal medium, the ADI activity in the optimized medium had nearly 4.5-fold increase (4.31U/mL). The optimized medium was then used for a further study of ADI production in a 14L stirred tank bioreactor. The agitation speed and the aeration rates were varied to determine suitable values of these variables.

Prediction of amino nitrogen during ugba (Pentaclethra macrophylla) production under different fermentation variables: A response surface approach

Manipulation of fermentation variables during ugba production (fermented African oil bean (Pentaclethra macrophylla) seed), using traditional method was studied with the view to improving the fermentation process, quality of product and prediction of process. The variables studied were number of leaves used in wrapping, relative humidity (RH) and temperature. Effect of variables on pH and amino nitrogen was determined on 24h basis for 96h. Attempt on prediction and optimization of process using response surface method was made. Amino nitrogen increased with fermentation time and attained its peak at 72h. The highest daily amino nitrogen value (3.4mgN/g) was obtained from sample fermented at 65% RH, temperature 30°C and 5 layers of leaves used for wrapping. pH increased into the alkaline range within the period, the highest value (8.2) being from sample fermented at 65% RH, 40°C and 10 layers of leaves. Statistical analysis and response surface plots associated with analysis showed that the quadratic effect of variables was significant (p<0.05). Effects of number of leaves used in wrapping and relative humidity were significant and accounted for 95.31% of the amino nitrogen variation. Thus use of response surface method predicted the process and can be used to optimize the fermentation process. This ultimately will improve product quality.

Mistuning analysis of bladed disk consisting of directionally solidified blade (Vibration response analysis by use of response surface method and FMM)

Mistuning analysis of bladed disk consisting of directionally solidified blade (Vibration response analysis by use of response surface method and FMM)

Use of response surface methods and path of steepest ascent to optimize ligand-binding assay sensitivity

Response surface methods (RSM) combined with a steepest ascent approach is a powerful technique to optimize assay performance. In this case, a ligand-binding assay (LBA) to quantitate a peptide biotherapeutic was optimized for improved sensitivity using this technique. Conditions were elucidated to enable pg/mL quantitation of the peptide in human plasma using steepest ascent to efficiently optimize assay factors. Instead of relying solely on assay development experience and intuition to improve assay sensitivity, this systematic approach takes advantage of a predictive mathematical model generated through response surface methods that defines a specific path towards greater predicted assay sensitivity. The actual response observed along the steepest ascent path was in good agreement with the model for several steps, until reagent concentrations moved beyond the physical limits of the system, and model breakdown occurred. RSM combined with steepest ascent method proved a useful tool for sensitivity optimization in three ways: (1) The required LBA sensitivity performance (approximately 200pg/mL), measured as a signal-to-noise ratio (SNR) at the targeted lower limit of quantitation (LLOQ), was efficiently achieved in only two optimization experiments; (2) Steepest ascent confirmed that the desired sensitivity was found within the initial RSM design space, and no further gain in sensitivity was found venturing beyond this design space along the steepest ascent path; (3) The desired assay sensitivity was maintained over a reasonable range of reagent concentrations along the steepest ascent path, indicating assay robustness for this parameter.

Optimum Vibration Control Design of a Light Weight Structure in Wide Frequency Domain

Although there are many studies on vibration reduction of a panel by making use of constrained viscoelastic materials, there is almost no research on vibration reduction for light weight paddle throughout the low to high frequency domains whose dynamic properties might be affected by the weight and rigidity of constrained viscoelastic damping materials. There is also little work on optimizing both parameters of constrained viscoelastic materials and light reinforced structural materials simultaneously. In this research, above-mentioned lightweight structure such as a solar paddle of a small satellite is of interest. For a lightweight structure like a solar paddle, the use of vibration control devices is not practical because of its cost and increase of mass, and then large modification of structure is not also practical. Furthermore, large response in high frequency domain is very critical for such structures from the viewpoint of fatigue. The objective of this research is to propose an optimum vibration control design method applicable throughout low to high frequency domains by considering parameters of the thickness, pasting regions for the constrained viscoelastic materials, and the position and cross section shape of stiffener simultaneously by making use of Response Surface Method (RSM) and Genetic Algorithm (GA). Through the design of a solar paddle of a small satellite, the effectiveness of the proposed method was demonstrated in comparison with other four methods including a conventional way.

軽量構造物の広周波数域の受動制振設計法 : 拘束型粘弾性減衰材と軽量補強構造材を同時に考慮した受動最適制振設計法(機械要素,潤滑,工作,生産管理など)

Although there are many researches about vibration reduction of panel by making use of constrained viscoelastic materials, there are almost no research on lightweight panel for the vibration reduction throughout the low to high frequency domain whose dynamic properties might be affected by the weight and rigidity of constrained viscoelastic damping materials. There is also little work to optimize both parameters of constrained viscoelastic materials and light reinforced structural materials simultaneously. In this research, above-mentioned lightweight structure such as a solar paddle of a small satellite is of interest. For a lightweight structure like a paddle, the use of vibration control devices are not practical because of its cost and increase of mass, and then large modification of structure is not also practical. Furthermore, large response in high frequency domain is very critical for such structures from the viewpoint of fatigue. The objective of this research is to propose an optimum vibration control design method throughout the low to high frequency domain by considering parameters of the thickness and pasting regions for the constrained viscoelastic materials and also the position and shape for the stiffener simultaneously by making use of Response Surface Method and Genetic Algorithm. Through the designs of a solar paddle of a small satellite, the effectiveness of the proposed method was demonstrated in comparison with other four methods including the conventional way.

PREDICTION OF TORQUE IN MILLING BY RESPONSE SURFACE METHOD AND NEURAL NETWORK

The present paper discusses the development of the first-order model for predicting the cutting torque in the milling operation of ASSAB 618 stainless steel using coated carbide cutting tools. The first-order equation was developed using response surface method (RSM). The input cutting parameters were the cutting speed, feed rate, radial depth and axial depth of cut. The study found that the predictive model was able to predict torque values close to those readings recorded experimentally with a 95% confident interval. The results obtained from the predictive model were also compared by using multilayer perceptron with back-propagation learning rule artificial neural network. The first-order equation revealed that the feed rate was the most dominant factor which was followed by axial depth, radial depth and cutting speed. The cutting torque value predicted by using Neural Network was in good agreement with that obtained by RSM. This observation indicates the potential use of RSM in predicting cutting parameters thus eliminating the need for exhaustive cutting experiments to obtain the optimum cutting conditions in terms of torque.

Prediction of Torque in Milling By Response Surface Method and Neural Network

The present paper discusses the development of the first-order model for predicting the cutting torque in the milling operation of ASSAB 618 stainless steel using coated carbide cutting tools. The first-order equation was developed using response surface method (RSM). The input cutting parameters were the cutting speed, feed rate, radial depth and axial depth of cut. The study found that the predictive model was able to predict torque values close to those readings recorded experimentally with a 95% confident interval. The results obtained from the predictive model were also compared by using multilayer perceptron with back-propagation learning rule artificial neural network. The first-order equation revealed that the feed rate was the most dominant factor which was followed by axial depth, radial depth and cutting speed. The cutting torque value predicted by using Neural Network was in good agreement with that obtained by RSM. This observation indicates the potential use of RSM in predicting cutting parameters thus eliminating the need for exhaustive cutting experiments to obtain the optimum cutting conditions in terms of torque.

Use of response surface method for the determination of demineralization efficiency in fermented shrimp shells

A Box–Behnken design with three variables (sucrose concentration, initial pH value and soaking time) and three levels were used for studying the demineralization efficiency in fermented shrimp shells by Pediococcus sp. L1/2. First, the bacterial cells were inoculated into the media with various concentrations of sucrose and initial pH values, and fermentation took place under static conditions at 37 °C for 24 h. Significant differences in the levels of total titratable acid were observed. This was followed by adding shrimp shells and soaking them in the fermentation media for 12, 24 and 36 h. The results showed that when the sucrose concentration was 50 g/L, and the initial pH value was 6.00, soaking for 36 h gave a demineralization efficiency of 68.38%. By solving the equation and also analyzing the response surface contour plots, optimum conditions occurred when the sucrose concentration was 50 g/L, the initial pH value was 7.00 and the soaking time was 36 h with a predicted value of demineralization of 83.03% whereas our experiment gave 83.47%.