Response Surface Modeling Approach Research Articles

Sensitivity analysis and optimisation of independent axle suspensions for a heavy-duty mining truck

The kinematic tuning of suspension mechanisms could substantially alter kinematic properties of the suspensions, and therefore the vehicle performance. In this study, positioning of hard points of a composite linkage (CL) and a candle (CA) suspension is investigated using design of experiment (DOE) and response surface model (RSM) approaches together with the effects of optimal designs on handling dynamics of a mining truck. Each kinematic response is optimised using the RSM approach, and the number of optimal cases is reduced considering correlations among different measures. The results suggest that the CL suspension with greater toe-in variation revealed lower under-steer gradient and greater peak lateral acceleration, yaw rate and roll angle responses of the sprung mass, in spite of its superior performance in terms of variations in camber, kingpin inclination and lateral displacement of wheel centre. The proposed method could be employed to achieve improved kinematic properties of current suspension designs.

A Response Surface Modelling Approach for Resonance Driven Reliability Based Optimization of Composite Shells

The composite materials are extensively used in the structures of civil, aerospace, marine, and automobile engineering due to their tailorable capability. The objective of this article is to address the issue of resonance-free lightweight design of such composite structures coupled with the notion of reliability. Laminated composite spherical shell is considered in this study to optimize width and thickness of the structure corresponding to dierent level of reliability of the system to avoid resonance. The present study utilizes genetic algorithm in conjunction to surrogate modelling with D-optimal design for this reliability based optimization problem.

Multi-objective optimisation of stent dilation strategy in a patient-specific coronary artery via computational and surrogate modelling

Although contemporary stents have been shown to improve short and long term clinical outcomes, the optimum dilation protocol is still uncertain in challenging cases characterised by long, highly calcified and tortuous anatomy. Recent clinical studies have revealed that in these cases, sub-optimal delivery can result in stent thrombosis (ST) and/or neointimal thickening as a result of stent malapposition (SM) and/or severe vessel trauma. One of the major contributors to vessel trauma is the damage caused by balloon dilation during stent deployment. In the present work, a Kriging based response surface modelling approach has been implemented to search for optimum stent deployment strategies in a clinically challenging, patient specific diseased coronary artery. In particular, the aims of this study were: (i) to understand the impact of the balloon pressure and unpressurised diameter on stent malapposition, drug distribution and wall stresses via computer simulations and (ii) obtain potentially optimal dilation protocols to simultaneously minimise stent malapposition and tissue wall stresses and maximise drug diffusion in the tissue. The results indicate that SM is inversely proportional to tissue stresses and drug deliverability. After analytical multi-objective optimisation, a set of “non-dominated” dilation scenarios was proposed as a post-optimisation methodology for protocol selection. Using this method, it has been shown that, for a given patient specific model, optimal stent expansion can be predicted. Such a framework could potentially be used by interventional cardiologists to minimise stent malapposition and tissue stresses whilst maximising drug deliverability in any patient-specific case.

An Efficient Optimization Work Flow for Field-Scale In-Situ Upgrading Developments

Summary In-situ upgrading process (IUP) is an attractive technology for developing unconventional extraheavy-oil reserves. Decisions are generally made on field-scale economics evaluated with dedicated commercial tools. However, it is difficult to conduct an automated IUP optimization process because of unavailable interface between the economic evaluator and commercial simulator/optimizer, and because IUP is such a highly complex process that full-field simulations are generally not feasible. In this paper, we developed an efficient optimization work flow by addressing three technical challenges for field-scale IUP developments. The first challenge was deriving an upscaling factor modeled after analytical superposition formulation; proposing an effective method of scaling up simulation results and economic terms generated from a single-pattern IUP reservoir-simulation model to field scale; and validating this approach numerically. The second challenge was proposing a response-surface model (RSM) of field economics to analytically compute key field economical indicators, such as net present value (NPV), by use of only a few single-pattern economic terms together with the upscaling factor, and validating this approach with a commercial tool. The proposed RSM approach is more efficient, accurate, and convenient because it requires only 15–20 simulation cases as training data, compared with thousands of simulation runs required by conventional methods. The third challenge is developing a new optimization method with many attractive features: well-parallelized, highly efficient and robust, and with a much-wider spectrum of applications than gradient-based or derivative-free methods, applicable to problems without any derivative, with derivatives available for some variables, or with derivatives available for all variables. This work flow allows us to perform automated field IUP optimizations by maximizing a full-field economics target while honoring all field-level facility constraints effectively. We have applied the work flow to optimize the IUP development of a carbonate heavy-oil asset. Our results show that the approach is robust and efficient, and leads to development options with a significantly improved field-scale NPV. This work flow can also be applied to other kinds of pattern-based field developments of shale gas and oil, and thermal processes such as steamdrive or steam-assisted gravity drainage.

Multi-objective optimization method of precision forging process parameters to control the forming quality

To control the precision forging product forming quality, a multi-objective optimization method for process parameters design was proposed by applying Latin hypercube design method and response surface model approach. Meanwhile, the deformation homogeneity and material damage of forging product was first presented for evaluating the forming quality. Then, as a case of study, the radial precision forging for a hollow shaft with variable cross section and wall thickness was carried out. The 3D rigid-plastic finite element (FE) model of the radial precision forging was established. The parameters on the forming quality evaluation function study were discussed to investigate the multi-objective optimization model. Non-dominated sorting genetic algorithm-II (NSGA-II) was adopted to obtain the Pareto-optimal solutions. A compromise solution was selected from the Pareto solutions by using the mapping method. Experiments with the same parameter settings were compared with the simulations. After conducting radial forging and mechanical property experimental study on the forging product by multi-objective optimization process parameters, the feasibility of the multi-objective optimization method for the precision forging product forming quality was verified.

Physical Properties of Refabricated Rice as Affected by Extrusion: A Response Surface Analysis

The aim of this study was to optimize the extrusion conditions for production of refabricated rice using a response surface modeling approach. The effects of feed moisture content, screw speed, and barrel temperature on the physical properties of rice grains refabricated using a corotating twin-screw extruder were investigated. Rice flour (variety PR-116) obtained from broken grains (smaller than one-eighth of a whole grain) was used in the study. Screw speeds at five levels between 49 and 150 rpm, barrel temperatures between 59 and 110°C, and feed moisture contents between 31 and 45% were tested. All of the physical properties of the refabricated rice grains that were evaluated (thousand kernel weight, length/breadth ratio, bulk density, and breaking strength) were significantly (P < 0.01) affected by these three process variables. Response surface regression models were established to correlate the physical properties of refabricated rice grains to the process variables. Understanding the effects of pro...

Design optimization of a runflat structure based on multi-objective genetic algorithm

Runflat structure plays an important role in determining the sustainable mileage after the tire is shot. Lightweight, stiffness and strength are highly relevant to the overall performance of the structure. A parameterized model was built based on the full study of the structure, and a new adaptive meshing method is proposed to ensure the quality of the model. The accuracy of the new model was verified by comparing to the traditional finite element model. The parameter study was carried out to investigate the response of the performance and mass. Multi-objective optimization model was established by applying optimal Latin square design method and response surface model approach. Non-dominated sorting genetic algorithm-II (NSGA-II) was applied to obtain the optimization design. The results indicate that the combination of parameterized model and multi-objective genetic algorithms successfully achieve the goal of multi-objective optimization for mass and displacement while ensuring the stress. Meanwhile, the optimal topology, shape and thickness optimization for the runflat structure have been achieved at the same time.

Multiobjective Optimization of Precision Forging Process Parameters Based on Response Surface Method

In order to control the precision forging forming quality and improve the service life of die, a multiobjective optimization method for process parameters design was presented by applying Latin hypercube design method and response surface model approach. Meanwhile the deformation homogeneity and material damage of forging parts were proposed for evaluating the forming quality. The forming load of die was proposed for evaluating the service life of die. Then as a case of study, the radial precision forging for a hollow shaft with variable cross section and wall thickness was carried out. The 3D rigid-plastic finite element (FE) model of the hollow shaft radial precision forging was established. The multiobjective optimization forecast model was established by adopting finite element results and response surface methodology. Nondominated sorting genetic algorithm-II (NSGA-II) was adopted to obtain the Pareto-optimal solutions. A compromise solution was selected from the Pareto solutions by using the mapping method. In the finite element study on the forming quality of forging parts and the service life of dies by multiobjective optimization process parameters, the feasibility of the multiobjective optimization method presented by this work was verified.

Sensitivity analysis frameworks for mechanistic-empirical pavement design of continuously reinforced concrete pavements

The new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) performance predictions for the anticipated climatic and traffic conditions depend on the values of the numerous input parameters that characterize the pavement materials, layers, design features, and condition. This paper proposes comprehensive local sensitivity analyses (LSA) and global sensitivity analyses (GSA) methodologies to evaluate continuously reinforced concrete pavement (CRCP) performance predictions with MEPDG inputs under various climatic and traffic conditions. A design limit normalized sensitivity index (NSI) was implemented in both LSA and GSA to capture quantitative as well as qualitative sensitivity information. The GSA varied all inputs simultaneously across the entire problem domain while the LSA varied each input independently in turn. Correlations among MEPDG inputs were considered where appropriate in GSA. Two response surface modeling (RSM) approaches, multivariate linear regressions (MVLR) and artificial neural networks (ANN or NN), were developed to model the GSA results for evaluation of MEPDG CRCP input sensitivities across the entire problem domain. The ANN-based RSMs not only provide robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs but also capture the variation of sensitivities across the problem domain. The NSI proposed in LSA and GSA provides practical interpretation of sensitivity relating a given percentage change in a MEPDG input to the corresponding percentage change in predicted distress relative to its design limit value. The “mean plus/minus two standard deviations (μ+2σ)” GSA-NSI metric (GSA-NSIμ±2σ) derived from ANN RSM statistics is the best and most robust design input ranking measure since it incorporates both the mean sensitivity and the variability of sensitivity across the problem domain.

Two reduced form air quality modeling techniques for rapidly calculating pollutant mitigation potential across many sources, locations and precursor emission types

Due to the computational cost of running regional-scale numerical air quality models, reduced form models (RFM) have been proposed as computationally efficient simulation tools for characterizing the pollutant response to many different types of emission reductions. The U.S. Environmental Protection Agency has developed two types of reduced form models based upon simulations of the Community Multiscale Air Quality (CMAQ) modeling system. One is based on statistical response surface modeling (RSM) techniques using a multidimensional kriging approach to approximate the nonlinear chemical and physical processes. The second approach is based on using sensitivity coefficients estimated with the Decoupled Direct Method in 3 dimensions (CMAQ-DDM-3D) in a Taylor series approximation for the nonlinear response of the pollutant concentrations to changes in emissions from specific sectors and locations. Both types of reduced form models are used to estimate the changes in O3 and PM2.5 across space associated with emission reductions of NOx and SO2 from power plants and other sectors in the eastern United States. This study provides a direct comparison of the RSM- and DDM-3D-based tools in terms of: computational cost, model performance against brute force runs, and model response to changes in emission inputs. For O3, the DDM-3D RFM had slightly better performance on average for low to moderate emission cuts compared to the kriging-based RSM, but over-predicted O3 disbenefits from cuts to mobile source NOx in very urban areas. The RSM approach required more up-front computational cost and produced some spurious O3 increases in response to reductions in power plant emissions. However the RSM provided more accurate predictions for PM2.5 and for predictions of very large emission cuts (e.g. −60 to −90%). This comparison indicates that there are some important differences in the output of the two approaches that should be taken under consideration when interpreting results for a given application.

Optimizing adsorption of fluoride from water by modified banana peel dust using response surface modelling approach

The present work highlighted the effective application of banana peel dust (BPD) for removal of fluoride (F−) from aqueous solution. The effects of operating parameters such as pH, initial concentration, adsorbent dose, contact time, agitation speed and temperature were analysed using response surface methodology. The significance of independent variables and their interactions were tested by the analysis of variance and t test statistics. Experimental results revealed that BPD has higher F− adsorption capacity (17.43, 26.31 and 39.5 mg/g). Fluoride adsorption kinetics followed pseudo-second-order model with high correlation of coefficient value (0.998). On the other hand, thermodynamic data suggest that adsorption is favoured at lower temperature, exothermic in nature and enthalpy driven. The adsorbents were characterised through scanning electron microscope, Fourier transform infrared spectroscopy and point of zero charges (pHZPC) ranges from pH 6.2–8.2. Finally, error analysis clearly demonstrates that all three adsorbents are well fitted with Langmuir isotherm compared to the other isotherm models. The reusable properties of the material support further development for commercial application purpose.

A Response Surface Model Approach for Continuous Measures of Hypnotic and Analgesic Effect during Sevoflurane–Remifentanil Interaction

The authors studied the interaction between sevoflurane and remifentanil on bispectral index (BIS), state entropy (SE), response entropy (RE), Composite Variability Index, and Surgical Pleth Index, by using a response surface methodology. The authors also studied the influence of stimulation on this interaction. Forty patients received combined concentrations of remifentanil (0 to 12 ng/ml) and sevoflurane (0.5 to 3.5 vol%) according to a crisscross design (160 concentration pairs). During pseudo-steady-state anesthesia, the pharmacodynamic measures were obtained before and after a series of noxious and nonnoxious stimulations. For the "prestimulation" and "poststimulation" BIS, SE, RE, Composite Variability Index, and Surgical Pleth Index, interaction models were applied to find the best fit, by using NONMEM 7.2.0. (Icon Development Solutions, Hanover, MD). The authors found an additive interaction between sevoflurane and remifentanil on BIS, SE, and RE. For Composite Variability Index, a moderate synergism was found. The comparison of pre- and poststimulation data revealed a shift of C50SEVO for BIS, SE, and RE, with a consistent increase of 0.3 vol%. The Surgical Pleth Index data did not result in plausible parameter estimates, neither before nor after stimulation. By combining pre- and poststimulation data, interaction models for BIS, SE, and RE demonstrate a consistent influence of "stimulation" on the pharmacodynamic relationship between sevoflurane and remifentanil. Significant population variability exists for Composite Variability Index and Surgical Pleth Index.

Catalyst-free microwave-assisted conversion of free fatty acids in triglyceride feedstocks with high acid content

The catalyst-free microwave-assisted conversion of free fatty acids (FFA) to alkyl esters in triglyceride feedstocks with a high acid content was studied. To this end, esterification of FFA with higher alcohols was investigated using the response surface modelling approach (RSM). A model was constructed using stearic acid and 1-butanol. The influence of reaction temperature and the FFA content in the triglyceride feedstock were studied by varying both parameters on 5 levels between 175 °C and 275 °C, and 30 mass% and 100 mass% FFA respectively. Afterwards, the general applicability of the model was verified using other FFA, fatty acid distillates (FAD) from industry and other alcohols. When using other FFA, FAD or primary alcohols other than 1-butanol, the model is able to accurately predict the actual conversion with a typical deviation between actual and predicted data of 2%. After 1 h of reaction at temperatures above 250 °C, conversions over 90% were reached.

Response Surface Analysis and Process Optimization of Twin Screw Extrusion Cooking of Potato-Based Snacks

The aim of the present study was to optimize the extrusion process for potato-based snacks using response surface modeling approach. A blend of mashed potatoes, rice and chickpea flour was used in the study. The effects of feed moisture, barrel temperature and screw speed on product responses viz. specific mechanical energy (SME), bulk density (BD), water absorption index (WAI), water solubility index (WSI) and hardness was studied using response surface methodology. The blend of potato-rice and chickpea was extruded at different moisture content (12.6–19.4%), screw speed (349–601 rpm) and barrel temperature (116–184C). Feed moisture had significant effect on all product responses, whereas screw speed and barrel temperature, both independent variables, had significant effect on SME, WSI and hardness of the product. Increase in feed moisture reduces SME and WSI and increases BD, WAI and hardness. Increase in screw speed decreases the BD, WAI and hardness of the snacks, whereas increase in barrel temperature decreases the SME, BD, WAI and hardness but increases the WSI. Optimized extrusion parameters for preparation of snacks were 14% moisture, 550 rpm screw speed, 170C temperature. Practical Applications Potatoes are generally processed into flakes, granules and flour by dehydration for using it as raw material for extrusion. Processing costs are very high, so mixing of mashed potatoes with cereal or pulse flour could be an energy-efficient way to use potatoes in extrusion. The consumers are becoming increasingly health conscious and while many admit that pulses are good for them, they are not sure how to use them in diet. There is also a perception that cooking of pulses is difficult and/or time consuming. There is need to find ways to increase consumption of pulses with little extra efforts. This paper explores the optimization of extrusion condition for development of nutritious snacks from potato-chickpea blends.

Solubility Enhancement of Modafinil by Complexation with &amp;#946;-cyclodextrin and Hydroxypropyl &amp;#946;-cyclodextrin: A Response Surface Modeling Approach

The aim of the study was to enhance solubility or dissolution characteristics of modafinil using its inclusion complexes (ICs) with β-cyclodextrin (β-CD) and hydroxypropyl β-cyclodextrin (HP β-CD). The phase solubility behavior of modafinil in the presence of various concentrations of hydrophilic carriers such as β-CD and HP β-CD (0.25-1.0% w/v) in distilled water was obtained at 37±2 °C. The solubility of modafinil increased with increasing the concentration of carriers. Gibbs free energy (G° tr) values were all negative, indicating the spontaneous nature of modafinil solubilization. Response surface modeling was used for development and data interpretation of the inclusion complexes. The ICs of modafinil with β-CD and HP β-CD were prepared at 1:0.5, 1:1,1:1.5 and 1:2 w/w ratio (Drug: Carrier) by kneading and physical mixing and found enhanced dissolution rate with increasing the carrier concentrations. Mean dissolution time (MDT) of modafinil decreased significantly after preparation of ICs by physical mixing and kneading. Host-guest interactions were characterized in solid state by Fourier-Transform Infrared Spectroscopy (FTIR). It was found that inclusion complex (IC) by kneading with β-CD at a 1:1 and HP β-CD with 1:0.5 weight ratio showed satisfactory enhancement of dissolution compared to pure drug. The XRD studies indicate the amorphous or microcrystalline states of modafinil in ICs. Significant improvement in solubility and dissolution of modafinil was observed with inclusion complex using two grades of cyclodextrins. The study suggested that HP β-CD complex of modafinil may be preferred over β-CD due to its result at half of the concentration when compared. Keywords: Cyclodextrins, inclusion complexation, modafinil, response surface model, solubility and dissolution.

A simple test for synergy for a small number of combinations

A method for detecting deviations from the Loewe additive drug combination reference model for in vitro drug combination experimentation is described. It is often difficult to fit a response surface model to drug combination data, especially in situations where the experimental design contains a sparse set of combinations. The literature does contain good response surface modeling approaches, but they tend to be complex and can be difficult to execute. It is especially difficult to check model quality when fitting to more than two combined agents. A simple method based on sound statistical principles is proposed that examines the mean response deviation of each combination from the predicted response under Loewe additivity. The method can readily handle any number of combined agents, does not require sophisticated modeling, and can even be programmed into Microsoft Excel without the use of macros. Several potential extensions to the method are discussed in detail. Computer-generated simulations demonstrate the statistical capabilities of the approach, and a real-data example is given to illustrate the method.

Global Sensitivity Analysis of Jointed Plain Concrete Pavement Mechanistic–Empirical Performance Predictions

The new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions for various what-if scenarios. MEPDG performance predictions for anticipated climatic and traffic conditions will depend on the values of the input parameters that characterize pavement materials, layers, design features, and condition. A comprehensive global sensitivity analysis methodology is proposed for evaluating performance predictions for jointed plain concrete pavement to MEPDG inputs for five climatic conditions and three traffic levels. MEPDG inputs evaluated in the analysis include traffic volume, layer thicknesses, material properties, groundwater depth, and geometric parameters. Correlations between MEPDG inputs were considered as appropriate. The global sensitivity analysis varied all inputs simultaneously across the problem domain for each of the 15 base cases (five climates x three traffic levels). Two response surface modeling approaches, multivariate linear regressions and artificial neural networks, were developed for evaluation of MEPDG input sensitivities across the problem domain. The response surface modeling approaches based on artificial neural networks not only provided robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs but also captured the variation in sensitivity across the problem domain. The normalized sensitivity index for the design limit proposed in the study provides practical interpretation of sensitivity by relating a given percentage change in an MEPDG design input to the corresponding percentage change in predicted distress relative to its design limit value.

Global Sensitivity Analysis of Mechanistic–Empirical Performance Predictions for Flexible Pavements

The new AASHTO Mechanistic–Empirical Pavement Design Guide (MEPDG) provides pavement analysis and performance predictions for various feasible design scenarios. The MEPDG performance predictions for the anticipated climatic and traffic conditions depends on the values of the input parameters that characterize the pavement materials layers, design features, and condition. This paper focuses on comprehensive global sensitivity analyses of flexible pavement performance predictions to MEPDG design inputs under five climatic conditions and three traffic levels. Design inputs evaluated in the analyses include traffic volume, layer thicknesses, material properties, and groundwater depth. Correlations between design inputs were considered as appropriate. The global sensitivity analysis varied all inputs simultaneously across the problem domain for each of the 15 base cases (five climates and three traffic levels). Two response surface modeling approaches—multivariate linear regressions and artificial neural networks—were developed for modeling the analysis results for evaluation of MEPDG input sensitivities across the problem domain. The response surface modeling approaches based on artificial neural networks were particularly effective in providing robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs. The design limit normalized sensitivity index adopted in the study provided a practical interpretation of sensitivity by relating a given percentage change in an MEPDG input to the corresponding percentage change in predicted distress relative to its design limit value. The design inputs most consistently in the highest sensitivity categories across all distresses were the hot-mix asphalt dynamic modulus master curve, hot-mix asphalt thickness, surface shortwave absorptivity, and Poisson's ratio of hot-mix asphalt. Longitudinal and alligator fatigue cracking were also very sensitive to granular base thickness and resilient modulus and subgrade resilient modulus.

1034 効率的な最適設計に向けたKriging応答曲面手法の拡張について

In this research, a Gradient-Vector Product (GVP) enhanced Kriging response surface model approach has been developed in which the GVP is defined by the gradient vector of objective function with respect to design variables as well as an arbitral unit directional vector in design variable space. When two sample points are given closely in design variable space, the correlation matrix in the Kriging formulation becomes ill-conditioned, which results in the inaccuracy of the response surface model. In this research, therefore, we try to treat the two functional data as one functional datum with one GVP datum in the Kriging formulation. The conventional Kriging formulation is extended to make use of the GVP information, and then a global/efficient design optimization system is proposed with the developed GVP-enhanced Kriging model approach. The validity of the developed approach is investigated in analytical function fitting problems as well as a 2D airfoil shape optimization problem.

Modeling and optimization of reductive degradation of chloramphenicol in aqueous solution by zero-valent bimetallic nanoparticles

The present study aims to investigate the individual and combined effects of temperature, pH, zero-valent bimetallic nanoparticles (ZVBMNPs) dose, and chloramphenicol (CP) concentration on the reductive degradation of CP using ZVBMNPs in aqueous medium. Iron-silver ZVBMNPs were synthesized. Batch experimental data were generated using a four-factor statistical experimental design. CP reduction by ZVBMNPs was optimized using the response surface modeling (RSM) and artificial neural network-genetic algorithm (ANN-GA) approaches. The RSM and ANN methodologies were also compared for their predictive and generalization abilities using the same training and validation data set. Reductive by-products of CP were identified using liquid chromatography-mass spectrometry technique. The optimized process variables (RSM and ANN-GA approaches) yielded CP reduction capacity of 57.37 and 57.10 mg g(-1), respectively, as compared to the experimental value of 54.0 mg g(-1) with un-optimized variables. The ANN-GA and RSM methodologies yielded comparable results and helped to achieve a higher reduction (>6%) of CP by the ZVBMNPs as compared to the experimental value. The root mean squared error, relative standard error of prediction and correlation coefficient between the measured and model-predicted values of response variable were 1.34, 3.79, and 0.964 for RSM and 0.03, 0.07, and 0.999 for ANN models for the training and 1.39, 3.47, and 0.996 for RSM and 1.25, 3.11, and 0.990 for ANN models for the validation set. Predictive and generalization abilities of both the RSM and ANN models were comparable. The synthesized ZVBMNPs may be used for an efficient reductive removal of CP from the water.