The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jianlong Wang - One of the best experts on this subject based on the ideXlab platform.
-
optimization of fermentative hydrogen production process using genetic algorithm based on neural network and Response Surface methodology
International Journal of Hydrogen Energy, 2009Co-Authors: Jianlong WangAbstract:Abstract A central composite design was carried out to investigate the effect of temperature, initial pH and glucose concentration on fermentative hydrogen production by mixed cultures in batch test. The modeling abilities of the Response Surface methodology model and neural network model, as well as the optimizing abilities of Response Surface methodology and the genetic algorithm based on a neural network model were compared. The results showed that the root mean square error and the standard error of prediction for the neural network model were much smaller than those for the Response Surface methodology model, indicting that the neural network model had a much higher modeling ability than the Response Surface methodology model. The maximum hydrogen yield of 289.8 mL/g glucose identified by Response Surface methodology was a little lower than that of 360.5 mL/g glucose identified by the genetic algorithm based on a neural network model, indicating that the genetic algorithm based on a neural network model had a much higher optimizing ability than the Response Surface methodology. Thus, the genetic algorithm based on a neural network model is a better optimization method than Response Surface methodology and is recommended to be used during the optimization of fermentative hydrogen production process.
-
optimization of fermentative hydrogen production process by Response Surface methodology
International Journal of Hydrogen Energy, 2008Co-Authors: Jianlong WangAbstract:The effect of temperature, initial pH and glucose concentration on fermentative hydrogen production by mixed cultures was investigated in batch tests, and the optimization of fermentative hydrogen production process was conducted by Response Surface methodology with a central composite design. Experimental results showed that temperatures, initial pH and glucose concentrations had impact on fermentative hydrogen production individually and interactively. The maximum hydrogen yield of 289.8 mL/g glucose was estimated at the temperature of 38.6 °C, the initial pH of 7.2 and the glucose concentration of 23.9 g/L. The maximum hydrogen production rate of 28.2 mL/h was estimated at the temperature of 37.8 °C, the initial pH of 7.2 and the glucose concentration of 27.6 g/L. The maximum substrate degradation efficiency of 96.9% was estimated at the temperature of 39.3 °C, the initial pH of 7.0 and the glucose concentration of 26.8 g/L. Response Surface methodology was a better method to optimize the fermentative hydrogen production process. Modified logistic model could describe the progress of cumulative hydrogen production in the batch tests of this study successfully.
Sooik Oh - One of the best experts on this subject based on the ideXlab platform.
-
cylindrical tube optimization using Response Surface method based on stochastic process
Journal of Materials Processing Technology, 2002Co-Authors: Sooik OhAbstract:Abstract This paper presents the optimization result in the crashworthiness problem for maximizing absorbing energy of cylindrical tube. To simulate a complicated behavior of this kind of crash problem, a self-developed explicit finite element code is used. The Response Surface method based on stochastic process is used, that is especially good at modeling the non-linear, multi-modal functions that often bring about in engineering. The main characteristics of using Response Surface for global optimization lies in balancing the need to exploit the fitting Surface for improving the approximation. It can be shown that how these approximating functions can be used to construct an efficient global optimization algorithm. Especially, with the comparison of result by classical optimization method, it can be shown that presented optimization method is independent of noise factor and existence of local minimum.
Douglas C Montgomery - One of the best experts on this subject based on the ideXlab platform.
-
optimizing protocol interaction using Response Surface methodology
IEEE Transactions on Mobile Computing, 2006Co-Authors: Kiran K Vadde, Violet R Syrotiuk, Douglas C MontgomeryAbstract:Abstract-Response Surface methodology (RSM) is a collection of statistical design and numerical optimization techniques traditionally used to optimize industrial processes. In this paper, we demonstrate that the methodology can be successfully applied to the domain of networking. Specifically, we obtain increased throughput with a significant decrease in delay in a ns-2 simulation model of a mobile ad hoc network (MANET) by using RSM to optimize protocol interaction found by factor screening. Whether the experimentation is with a stochastic simulation model or a physical system, such as a MANET or a wireless sensor network test-bed, RSM provides a general and practical methodology to screen factors and robustly and jointly optimize Responses.
-
Response Surface methodology a retrospective and literature survey
Journal of Quality Technology, 2004Co-Authors: Raymond H Myers, Douglas C Montgomery, Geoffrey G Vining, Connie M Borror, Scott M KowalskiAbstract:Response Surface methodology (RSM) is a collection of statistical design and numerical optimization techniques used to optimize processes and product designs. The original work in this area dates from the 1950s and has been widely used, especially in the chemical and process industries. The last 15 years have seen the widespread application of RSM and many new developments. In this review paper we focus on RSM activities since 1989. We discuss current areas of research and mention some areas for future research.
-
Response Surface methodology a retrospective and literature survey
Journal of Quality Technology, 2004Co-Authors: Raymond H Myers, Douglas C Montgomery, Geoffrey G Vining, Connie M Borror, Scott M KowalskiAbstract:The original work in Response Surface methodology (RSM) has been widely used in the chemical and process industries. Recent years have seen more widespread use and new developments in RSM. RMS activities since 1989 are reviewed, and areas of current and..
-
Response Surface methodology process and product optimization using designed experiments
1995Co-Authors: Raymond H Myers, Douglas C MontgomeryAbstract:From the Publisher: Using a practical approach, it discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniques, focusing on Response Surface methodology, mixture experiments and robust design techniques. Features numerous authentic application examples and problems. Illustrates how computers can be a useful aid in problem solving. Includes a disk containing computer programs for a Response Surface methodology simulation exercise and concerning mixtures.
Raphael T. Haftka - One of the best experts on this subject based on the ideXlab platform.
-
error amplification in failure probability estimates of small errors in Response Surface approximations
SAE World Congress & Exhibition, 2007Co-Authors: Palaniappan Ramu, Raphael T. HaftkaAbstract:Response Surface methods which approximate the actual performance function using simple algebraic equations are widely used in structural reliability studies. The Response Surface approximations are often used to estimate the reliability of a structure. Errors in the Response Surface approximation affect the results of reliability analysis. This work investigates the error in the failure probability estimated using a Response Surface approximation. It is observed that small errors in the Response Surface may amplify to large errors in the failure probability. It is observed that the amplification occurs when the failure Surface is far away from the Response mean and the DOE has more points near the mean. Another situation is when the failure region is a small island encompassed within the safe region, and the points in the DOE fail to capture the failure region. Analytical and engineering application examples are investigated to understand the amplification of error in the failure probability.
-
Reasonable Design Space Approach to Response Surface Approximation
Journal of Aircraft, 1999Co-Authors: Vladimir Balabanov, Anthony A Giunta, Oleg B. Golovidov, Bernard Grossman, William H. Mason, Layne T. Watson, Raphael T. HaftkaAbstract:Response Surface approximations have become very popular as a tool for multidisciplinary optimization. However, the accuracy of these models is a major concern. One method to increase the accuracy of Response Surface approximations is to construct them only for the portion of the design space that represents reasonable designs. Our paper describes the methodology underlying this reasonable design space approach. This methodology is illustrated using two examples involving multidisciplinary optimization of a high-speed civil transport model. In these examples, low-fidelity analyses are used to estimate the boundary of the reasonable design space. Within the reasonable design space, Response Surface models are constructed, based on results from higher-fidelity analyses. The accuracy of the Response Surface models created with and without using the reasonable design space approach is compared, and the advantages of employing Response Surface models created via the reasonable design space approach are demonstrated.
-
structural optimization of a hat stiffened panel by Response Surface techniques
38th Structures Structural Dynamics and Materials Conference, 1997Co-Authors: Roberta Vitali, Raphael T. Haftka, Oung Park, Bhavani V SankarAbstract:This paper describes a design study for the structural optimization of a typical bay of a blended wing body transport. A hat stiffened laminated composite shell concept is used in the design. The geometry of the design is determined with the PANDA2 program, but due to the presence of varying axial loads, a more accurate analysis procedure is needed. This is obtained by combining the STAGS finite element analysis program with Response Surface approximations for the stresses and the buckling loads. This design procedure results in weight savings of more than 30 percent, albeit at the expense of a more complex design. The Response Surface approximations allow easy coupling of the structural analysis program with the optimization program in the easily accessible Microsoft EXCEL spreadsheet program. The Response Surface procedure also allows the optimization to be carried out with a reasonable number of analyses. In particular, it allows combining a large number of inexpensive beamanalysis stress calculations with a small number of the more accurate STAGS analyses.
-
variable complexity Response Surface approximations for wing structural weight in hsct design
Computational Mechanics, 1996Co-Authors: Matthew Kaufman, Anthony A Giunta, Bernard Grossman, William H. Mason, Vladimir Balabanov, Raphael T. Haftka, Susan L Burgee, Layne T. WatsonAbstract:A procedure for generating and using a polynomial approximation to wing bending material weight of a High Speed Civil Transport (HSCT) is presented. Response Surface methodology is used to fit a quadratic polynomial to data gathered from a series of structural optimizations. Several techniques are employed in order to minimize the number of required structural optimizations and to maintain accuracy. First, another weight function based on statistical data is used to identify a suitable model function for the Response Surface. In a similar manner, geometric and loading parameters that are likely to appear in the Response Surface model are also identified. Next, simple analysis techniques are used to find regions of the design space where reasonable HCST designs could occur. The use of intervening variables along with analysis of variance reduce the number of polynomial terms in the Response Surface model function. Structural optimization is then performed by the program GENESIS on a 28-node Intel Paragon. Finally, optimizations of the HSCT are completed both with and without the Response Surface.
Mikko Makela - One of the best experts on this subject based on the ideXlab platform.
-
experimental design and Response Surface methodology in energy applications a tutorial review
Energy Conversion and Management, 2017Co-Authors: Mikko MakelaAbstract:Abstract Experimental design and Response Surface methodology are useful tools for studying, developing and optimizing a wide range of engineering systems. This tutorial provides a summary and discussion on their use in energy applications. The theory and relevant calculations are clearly presented and discussed along with model diagnostics and interpretation. This is followed by a review of recent reports within the energy field. Overall, this contribution will clarify many aspects of experimental design and Response Surface methodology that are often confusingly discussed in the academic literature and summarizes relevant applications where they have been found useful.
