Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction

Baş, Deniz; Boyaci, İsmail Hakkı

doi:10.1016/j.jfoodeng.2005.11.025

Cited by 253 publications

(94 citation statements)

References 22 publications

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“…The interactive effect among the fermentation parameters can be ascertained through the use of effective optimization tools. Response Surface Methodology (RSM) is a statistical tool used for creating a relationship between a set of defined experimental variables and the observed results [24]. RSM quantifies the effect of the control variables, alone or in combination, on a selected response.…”

Section: Glucose (G/l)mentioning

confidence: 99%

“…It is clear, however, that most of the critical nutrients selected for optimization may be redundant, as they are already contained in adequate quantities in CSL [26]; eliminating them may significantly improve the economics of the process, especially on an industrial scale. While the applicability of RSM to optimization studies has been demonstrated successfully [22,25,27], studies have also shown that its predictive capability may be limited [24,28]. Baş and Boyacı [24] reported that the second-order equation obtained from RSM did not show the interactive effects of pH and substrate concentration on the initial reaction rate of the enzymatic reaction in their study.…”

Section: Glucose (G/l)mentioning

confidence: 99%

“…While the applicability of RSM to optimization studies has been demonstrated successfully [22,25,27], studies have also shown that its predictive capability may be limited [24,28]. Baş and Boyacı [24] reported that the second-order equation obtained from RSM did not show the interactive effects of pH and substrate concentration on the initial reaction rate of the enzymatic reaction in their study. Similar findings were reported in the lipase catalysis and optimization studies of borage oil: that there was no significant interaction among the variables considered using RSM [28].…”

Section: Glucose (G/l)mentioning

confidence: 99%

“…It is made up of a vastly interconnected network structure consisting of many simple processing elements (the artificial neurons, units, or nodes) that are capable of performing parallel computation for data processing. The theoretical basis has been described in detail elsewhere [24,31]. ANN models have been reported to be preferred above RSM in accuracy and data prediction [32][33][34].…”

Section: Glucose (G/l)mentioning

confidence: 99%

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Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast

2018

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Abstract:Bioethanol production has seen an increasing trend in research recently, with a focus on increasing its economic viability. The aim of this study is to develop a low-cost fermentation medium with a minimum of redundant nutritional supplements, thereby minimizing the costs associated with nutritional supplements and seed production. Corn steep liquor (CSL) in glucose fermentation by Saccharomyces Type 1 (ST1) strain and Anchor Instant Yeast (AIY), which are low-cost media, is used as a replacement for yeast extract (YE). The fermentation process parameters were optimized using artificial neural networks (ANN) and the response surface method (RSM). The study shows that for CSL, maximum average ethanol concentrations of 41.92 and 45.16 g/L, representing 82% and 88% of the theoretical yield, were obtained after 36 h of fermentation in a shake flask for ST1 and AIY, respectively. For YE, ethanol concentrations equivalent to 86% and 88% of theoretical yield were obtained with ST1 and AIY, respectively after 48 h. Although ANN better predicted the responses compared to RSM, optimum conditions were better predicted by RSM. This study shows that corn steep liquor is an inexpensive potential nutrient that may have significant cost implications for commercial ethanol production.

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Section: Glucose (G/l)mentioning

confidence: 99%

Section: Glucose (G/l)mentioning

confidence: 99%

Section: Glucose (G/l)mentioning

confidence: 99%

Section: Glucose (G/l)mentioning

confidence: 99%

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Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast

2018

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“…[25] ANN is a simulation of the human brain which is able to use the trained data in the range of an investigation to estimate the response. [26] The focus of this study was on optimization of CGTase biosynthesis by recombinant L. lactis NZ:NSP: CGT using ANN as the main tool. To the best of our knowledge, there is no literature available on either optimization of CGTase production using ANN or CGTase biosynthesis by L. lactis strain.…”

Section: Introductionmentioning

confidence: 99%

Cyclodextrin glycosyltransferase biosynthesis improvement by recombinantLactococcus lactisNZ:NSP:CGT: medium formulation and culture condition optimization

Amiri

Mohamad

Rahim

et al. 2015

Biotechnology & Biotechnological Equipment

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Cyclodextrin glycosyltransferase (CGTase) is a bacterial glycosyltransferase which catalyses the conversion of starch to cyclodextrin. It can be produced industrially through a fermentation process. The optimal design of the fermentation medium and conditions is critical for metabolic production and microbial growth. Optimization of CGTase biosynthesis by recombinant Lactococcus lactis NZ:NSP:CGT was performed using an artificial neural network (ANN) as the main tool in order to improve the CGTase production in the fermentation process. Three parameters, including temperature, carbon source concentration and nitrogen source concentration, were determined as significant factors for improvement of CGTase production by L. lactis NZ:NSP:CGT in the fermentation process. Soluble potato starch and yeast extract were chosen as the best carbon and nitrogen sources for higher production of CGTase by L. lactis NZ:NSP:CGT. The optimum concentration of soluble starch and yeast extract were 3.82% and 5.67% (w/v), respectively, and the optimum temperature was 20C. The final CGTase activity under optimized conditions was 22.09 U/mL, which was close enough to the predicted value of 24.17 U/mL. The obtained results are of particular interest, as CGTase is an industrially important enzyme. In this study, its production through batch fermentation was optimized in order to maximize its production. In future studies, more investigations in various modes of fermentation should be conducted in order to increase the CGTase biosynthesis.

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Thyristor controlled reactor‐based voltage and frequency regulation of a three‐phase self‐excited induction generator feeding unbalanced load

Çalgan

İlten

Demirtaş

2020

Int Trans Electr Energ Syst

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Summary A self‐excited induction generator (SEIG) is one of the cost‐effective ways for power generation in remote areas. Unlike common wind energy systems, SEIG needs an excitation capacitor bank to meet the reactive power demand. The terminal voltage of the SEIG depends on the reactive power delivered from excitation capacitor bank which complicates the voltage and frequency regulation. This article presents an unbalanced loaded three‐phase SEIG system controlled by fixed capacitor and thyristor controlled reactor in order to overcome poor voltage regulation problem. Moreover, the output frequency regulation is maintained thanks to the speed controller adjusting the speed of the generator. This regulation process employs a response surface model (RSM) instead of steady state equation that determines accurately the per‐phase triggering angle of TCR and speed of the generator. Therefore, there is no need to utilize the per‐phase equivalent circuit parameters which is the major advantage of the proposed method. The feasibility and reliability of the RSM have been proven by carrying out test experiments. Experimental results of three‐phase SEIG (5.5 kW) feeding unbalanced loads have been performed to confirm the effectiveness of proposed method. According to experimental results, voltage and frequency regulations of SEIG have been well performed by using RSM within the maximum 3% regulation error.

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Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction

Cited by 253 publications

References 22 publications

Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast

Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast

Cyclodextrin glycosyltransferase biosynthesis improvement by recombinantLactococcus lactisNZ:NSP:CGT: medium formulation and culture condition optimization

Thyristor controlled reactor‐based voltage and frequency regulation of a three‐phase self‐excited induction generator feeding unbalanced load

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