Response Surface Methodology optimization process

Enzymatic hydrolysis of waste paper process optimization using response surface methodology... 

Garrote, R. L., Coutaz, V. R., Luna, J. A., Silva, E. R., Bertone, R. A. (1993). Optimizing processing conditions for chemical peeling of potatoes using response surface methodology. J. Food Sci., 58, 821-826. 

Myers, R. H., Montgomery, D. C. (1995). Response Surface Methodology Process and Product Optimization Using Designed Experiments. Wiley, New York. 

Guerard, F., Sumaya-Martinez, M. T., Laroque, D., Chabeaud, A., and Dufosse, L. (2007). Optimization of free radical scavenging activity by response surface methodology in the hydrolysis of shrimp processing discards. Process Biochem. 42,1486-1491. 

Ushakumari, S. R., Rastogi, N. K., and Malleshi, N. G. (2007). Optimization of process variables for the preparation of expanded millet using response surface methodology. ]. Food Eng. 82,35-42. 

Cui, W., Mazza, G., Oomah, B.D., and Biliaderis, C.G. 1994b. Optimization of an aqueous extraction process for flaxseed gum by response surface methodology. Lebensm, Wiss. U. Technol. 27, 363-369. 

Sukigara, S., Gandhi, M., Ayutsede, J., Micklus, M., and Ko, F. "Regeneration of Bombyx mori silk by electrospinning. Part 2. Process optimization and empirical modeling using response surface methodology". Polymer 45(11), 3701-3708 (2004). 

In the DoE approach, first process variables are screened to determine which are important to the outcome (excipient type, percentage, mixture time, etc.). The next step is optimization, when the best settings for the important variables are determined. In particular, response surface methodologies have been successfully applied in both drug discovery and development [33]. Advances in supporting software, automated synthesis instrumentation, and high-throughput analytical techniques have led to the broader adoption of this approach in pharmaceutical discovery and chemical development laboratories [34],... 

Adinarayana, K., Ellaiah, P., Srinivasulu, B., Bhavani Devi, R., and Adinarayana, G. (2003b). Response surface methodological approach to optimize the nutritional parameters for neomycin production by Streptomyces marinensis under solid-state fermentation. Process Biochem. 38,1565-1572. 

Myers, R.H. Montgomery, D.C. Response surface methodology process and product optimization using designed experiments. Wiley Series in Probability and Statistics, Wiley Interscience, 1995 248 pp. 

The best method for process optimization is response surface methodology (RSM). This process will not only determine optimum conditions, but also give the information necessary to design a process. 

Response surface methodology (RSM) is a method of optimization using statistical techniques based upon the special factorial designs of Box and Behenkini and Box and Wilson.I It is a scientific approach to determining optimum conditions which combines special experimental designs with Taylor first and second order equations. The RSM process determines the surface of the Taylor expansion curve which describes the response (yield, impurity level, etc.) The Taylor equation, which is the heart of the RSM method, has the form ... 

These principles formed the basis for producing high quality carrots and potatoes by a process of biopolymer infusion followed by high temperature short time fluidized bed dehydration. Infused biopolymers was shown to penetrate intracellular spaces and cell walls and may contribute to reduced cell collapse in the dehydration process. Deposition of infused biopolymer within the cells was elucidated using a convalently bound complex of biopolymer and colored dye which was visible upon histochemical examinations under a microscope. The dehydration process was optimized with response surface methodology. The resulting products have excellent quality, high rehydration ratio and a puffed structure. 

P. Wehrle, Ph. Nobelis, A. Cuine, and A. Stamm, Response surface methodology an interesting tool for process optimization and validation example of wet granulation in a high shear mixer. Drug Dev. Ind. Pharm., 19, 1637-1653 (1993). 

Response surface methodology (RSM) is an optimization technique based on factorial designs introduced by G.E.R Box in the 1950s. Since then, RSM has been used with great success for modeling various industrial processes. In this chapter, we use the concepts introduced in the previous chapters to explain the basic principles of RSM. The interested reader can find more comprehensive treatments in Cornell (1990a), Myers and Montgomery (2002) and in the excellent books and scientific papers of G.E.R Box and his co-workers (Box and Wilson, 1951 Box, 1954 Box and Youle, 1955 Box and Draper, 1987). 

Continuing with the optimization process of Table 8.2 we can note that vertex 18 is maintained in aU of the simplexes constructed after the massive contraction. In other words, the simplex remains anchored at this point, and makes a circular movement aroimd it, indicating that we have arrived in the region of the maximum. At this point, to determine the maximum with more precision it is better to abandon the simplex method and map the region aroimd vertex 18, appljring the response surface methodology we discussed in Chapter 6. 

Chao Yuan [25] has optimized the preparation process of HP-)3-CDs. Reaction time (A) reaction temperature (B), dialysis time (C) were employed in the single factor experiment. Then, a central composite design of response surface methodology was used with the DS and yield response values. 

Yuan, C, BG Liu and CG Chen (2010). Optimization of preparation process of hydroxypropyl-heta-cyclodextrin hy response surface methodology. Proc. 2010 International Conference on Challenges in Environmental Science and Computer Engineering, pp. 26-28. CESCE. 

Response surface methodology (RSM) is utilized to develop mathematical relationships as well as for searching out optimal EMM process parameters setting to achieve the desired level of micro-machining performance criteria. EMM process criteria are measured during linear microchannel generation on copper workpiece, after each set of experimental mn based on experimental planning. The actual values of factors and their levels utilized for the RSM are listed in Table 8.3. 

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