Maximization of lipid production by <i>Rhodotorula gracilis</i> CFR‐1 using response surface methodology

Prapulla, S. G.; Jacob, Z.; Chand, Navin; Rajalakshmi, D.; Karanth, N. G.

doi:10.1002/bit.260400812

Cited by 55 publications

(19 citation statements)

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“…The orthogonal ®rst-order design includes the 2 k factorial and fractions of the 2 k series in which main eects do not interact. This 2 k factorial design, however, does not allow an estimation of the experimental error unless some runs are repeated, which is often achieved by replicating several observations at the center (Draper and Lin, 1990;Hwang and Hansen, 1997;Prapulla et al, 1992). An experimental design for ®tting a second-order model involves at least three levels of each factor.…”

Section: Empirical Models For Biological Processesmentioning

confidence: 99%

Maximization of acetic acid production in partial acidogenesis of swine wastewater

Hwang

Lee²,

Yang

2001

Biotech & Bioengineering

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Swine wastewater was biologically treated to produce short-chain volatile organic acids (VOAs) in laboratory-scale continuously stirred tank reactors. The maximum production rates of acetic and butyric acids associated with simultaneous changes in pH and hydraulic retention time (HRT) were investigated, in which the degree of acidification of swine wastewater to the short-chain VOAs was <25% of influent chemical oxygen demand (COD) concentration. A constant inoculum system was used to minimize the experimental error due to the use of inconsistent inoculum. The inoculum system was operated with synthetic wastewater at 6000 mg soluble chemical oxygen demand per liter (pH 6.0) and 35 degrees C at 0.5 day hydraulic retention time. Response surface methodology was applied successfully to determine the optimum physiological condition for which the maximum rate of acetic acid production occurred, which was pH 5.90 and 0.88 day hydraulic retention time at 35 degrees C. The partial acidification process to manage swine waste should be operated in the optimum condition for acetic acid production because the optimum operating condition for butyric acid production approached the washout point.

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Section: Empirical Models For Biological Processesmentioning

confidence: 99%

Maximization of acetic acid production in partial acidogenesis of swine wastewater

Hwang

Lee²,

Yang

2001

Biotech & Bioengineering

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“…They employed the response surface methodology with a 3-3 incomplete factorial design to optimize experimental conditions. Prapulla et al (1992) carried out an experiment where levels of nitrogen, carbon and inoculum were chosen as factors and used RSM to maximize the lipid production. Agarwal et al (1994) calculated a quadratic response surface equation for economizing fertilizer use for rainfed sesame crop in Doon Valley.…”

Section: Introductionmentioning

confidence: 99%

An Application and Interpretation of the Second Order Response Surface Model

Kocabaş¹

2001

Tarım Bilimleri Dergisi

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In this study, three artificial data sets were used to explain the second order response surface model. It was assumed that the data were collected from a 3x3 experiment with two replications. Fitting the second order response surface model showed that 92.51%, 83.93% and 85.41% of the tatei variation in YIELD1, YIELD2 and YIELD3, respectively, accounted for by the fitted model. When the lack of fit test was applied, the results clarif ı ed that the second order model was adequately described the response surface for all the three sets of data. Application of the canonical analysis confı rmed that there was a stationary point of maximum response for YIELD1, a stationary point of minimum response for YIELD2 and a saddle point for YIELD3. After that, the data on the corrected sugar content of sugar beet were analyzed. The results showed that the fitted second order model accounted for 68.9% of the total variation in the sugar content. When the lack of fit test was applied, the results showed that the second order model adequately described the response surface of sugar content. The results of canonical analysis indicated that there was a saddle point for the sugar content.Key Words: Response surface, second order model, stationary point, saddle point, maximum point, minimum point İ kinci Derece Yan ıt Yüzey Modellerinin Uygulamas ı ve Yorumlanmas ı Özet: İ kinci derece yan ıt yüzey modellerinin uygulamas ı ve sonuçları n yorumlanmas ı için yapay olarak oluşturulan üç veri kümesi kullan ı lm ışt ı r. Verilerin 3x3 düzeninde tertiplenmi ş iki tekerrürlü bir denemeden topland ığı varsay ı lm ıştı r. Yapay olarak olu şturulan veriler için ikinci derece yan ıt yüzey modeli hesapland ığı zaman VERIMI 'deki toplam varyasyonun %95.52'sinin, VER İ M2'deki toplam varyasyonun %83.93'ünün ve VER İ M3'teki toplam varyasyonun %85.41'inin hesaplanan model taraf ı ndan açı kland ığı görülmü ştür. İ kinci derece modelin uygunluk kontrolünden elde edilen sonuçlar hesaplanan modelin yan ıt yüzeyini tan ı mlamak için yeterli oldu ğ unu göstermiştir. Yan ı t yüzeyinin yap ısı n ı incelemek için uygulanan kanonik analiz VERIMI için maksimum, VER İ M2 için minimum oldu ğunu göstermi ştir. Buna karşı n VER İ M3 için bir eyer (saddle) noktas ı hesaplanm ışt ı r. Yapay veriler kullan ıld ı ktan sonra şeker içeri ğ ine ait toplanm ış veriler analiz edilmi ştir. Şeker içeriği verileri için ikinci derece yan ıt yüzeyi modeli hesapland ığı zaman, şeker içeri ğindeki toplam varyasyonun %68.9'unun hesaplanan model taraf ı ndan aç ı kland ığı görülmü ştür. İ kinci derece modelin uygunluk kontrolünden elde edilen sonuçlar hesaplanan modelin şeker içeri ği için yan ıt yüzeyini tan ı mlamak için yeterli oldu ğ unu göstermi ştir.Uygulanan kanonik analizde şeker içeri ği için bir eyer (saddle) nokta hesaplanm ışt ı r.Anahtar Kelimeler: Yan ıt yüzeyleri, ikinci derece model, kritik nokta, eyer noktas ı , maksimum ve minimum nokta

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“…The production of propionic acid, which is another frequently found intermedierating specifications. Almost all RSM problems utilize one or both of these approximating polynomials ate in anaerobic digestion, was not considered because of its toxicity to the system (Cherrington et al, 1991;(Draper and Lin, 1990;Prapulla et al, 1992;Sa and Edwards, 1993). Mawson et al, 1991;Stronach et al, 1986;Van Lier et al, 1993).…”

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“…This 2 k factorial design, however, does not allow an Experimental Materials estimation of the experimental error unless some runs are repeated, which is often achieved by replicating sevOne batch of mixed cheese-processing wastewater was obtained (Gossner's Food, Logan, UT). The waste was eral observations at the center (Draper and Lin, 1990;Prapulla et al, 1992;Sa and Edwards, 1993;Vining and homogeneously mixed and divided into smaller portions and frozen at Ϫ25ЊC for further use. Myers, 1991).…”

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confidence: 99%

“…Two double-head Masterflex pump (Cole-Palmer Selection of Variables Instrument Company) units controlled the flow rate in Microbial growth and enzyme activity have specific opticontinuous experiments and the inoculum system. mum conditions with regard to many variables, including pH, temperature, hydraulic or microbial residence Analytical Methods time in the bioreactor, and presence of inhibitors (Gaudy and Gaudy, 1980). For example, in the acido-A Varian gas chromatograph (Series 3700) with a genic fermentation of lactose, the formation of gaseous Carbopack᭨ column (1-1825 Supelco Inc.) and a flame products (CO 2 and H 2 ) is dependent on pH during diionization detector was used to determine the concengester start-up (Kisaalita et al, 1987).…”

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Modeling and optimization in anaerobic bioconversion of complex substrates to acetic and butyric acids

Hwang

Hansen

1997

Biotechnol. Bioeng.

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Cheese‐processing wastewater was biologically treated to produce short‐chain organic acids in laboratory scale continuously stirred tank reactors. A constant inoculum system was used to mimimize the experimental error due to the use of inconsistent inoculum. The inoculum system was operated with dilute cheese‐processing wastewater with 5000 mg soluble chemical oxygen demand/L at pH 6.5 and 35°C at 0.5 days hydraulic retention time. Response surface methodology was successfully applied to determine the optimum physiological conditions where the maximum rates of acetic and butyric acid production occurred. These were pH 7.01 at 36.2°C and pH 7.26 at 36.2°C, respectively. The lack of overall predictability for butyric acid production meant that the response surface was much more complicated than that of acetic acid; therefore, a small change in pH or temperature could cause large variations in the response of butyric acid production. © 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 54:451–460, 1997.

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Maximization of lipid production by Rhodotorula gracilis CFR‐1 using response surface methodology

Cited by 55 publications

References 8 publications

Maximization of acetic acid production in partial acidogenesis of swine wastewater

Maximization of acetic acid production in partial acidogenesis of swine wastewater

An Application and Interpretation of the Second Order Response Surface Model

Modeling and optimization in anaerobic bioconversion of complex substrates to acetic and butyric acids

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