Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling

Vani, Sankar; Sukumaran, Rajeev K.; Savithri, S.

doi:10.1016/j.biortech.2015.01.083

Cited by 86 publications

(38 citation statements)

References 27 publications

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“…A 3 × 2 × 4 × 4 factorial design with four independent variables (fraction type, pretreatment technique, temperature, and time) was used. The choice of this factorial design for determining the optimal conditions was based on previous studies (Idrees et al 2014;Satimanont et al 2012;Vani et al 2015). The effect of the independent variables on glucose concentration was analyzed using ANOVA to assess if differences existed in glucose concentration.…”

Section: Discussionmentioning

confidence: 99%

“…Several attempts are being made to convert lignocellulosic biomass from different plant sources such as corn stover (Karp et al 2014;Li et al 2012), corn cob (Liu et al 2010), switch grass (Payne and Wolfrum 2015), corn leaf (Cai et al 2016), rice straw (Karimi et al 2006;Vani et al 2015), wheat straw (Jin et al 2013;Schmidt Anette and Anne Belinda 1998), soft Pinus densiflora (Lee et al 2007), Micanthus (Obama et al 2012;Payne and Wolfrum 2015), sugar bagasse (Manzoor et al 2012), and some few others for bioethanol production. Among the different lignocellulosic biomass, corn stover is the most promising and widely studied biomass feedstock for bioethanol production and it is one of the abundant agricultural residues which can be used as energy source (Bengtsson et al 2006;Kaar and Holtzapple 2000;Sahare et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…However, the pretreatment and the hydrolysis steps of biomass conversion to ethanol are expensive processes (Oliva and Ballesteros 2008;Vani et al 2015). The use of an efficient pretreatment technique would lead to a decrease in expected enzymatic loading which reduces costs and improves feasibility (Anne 2014).…”

Section: Introductionmentioning

confidence: 99%

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Glucose recovery from different corn stover fractions using dilute acid and alkaline pretreatment techniques

et al. 2017

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Background: Limited availability of corn stover due to the competing uses (organic manure, animal feed, bio-materials, and bioenergy) presents a major concern for its future in the bio-economy. Furthermore, biomass research has exhibited different results due to the differences in the supply of enzymes and dissimilar analytical methods. The effect of the two leading pretreatment techniques (dilute acid and alkaline) on glucose yield from three corn stover fractions (cob, stalk, and leaf) sourced from a single harvest in Uganda were studied at temperatures 100, 120, 140, and 160°C over reaction times of 5, 10, 30, and 60 min. Results: From this study, the highest glucose concentrations obtained from the dilute acid (DA) pretreated cobs, stalks, and leaves were 18.4 g/L (66.8% glucose yield), 16.2 g/L (64.1% glucose yield), and 11.0 g/L (49.5% glucose yield), respectively. The optimal pretreatment settings needed to obtain these yields from the DA pretreated samples were at a temperature of 160°C over an incubation time of 30 min. The highest glucose concentrations obtained from the alkaline (AL) pretreated cobs, stalks, and leaves were 24.7 g/L (81.73% glucose yield), 21.3 g/L (81.23% glucose yield), and 15.0 g/L (51.92% glucose yield), respectively. To be able to achieve these yields, the optimal pretreatment settings for the cobs and stalks were 140°C and for a retention time of 30 min, while the leaves require optimal conditions of 140°C and for a retention time of 60 min. Conclusions: The study recommends that the leaves could be left on the field during harvesting since the recovery of glucose from the pretreated cobs and stalks is higher.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Glucose recovery from different corn stover fractions using dilute acid and alkaline pretreatment techniques

et al. 2017

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“…using an ANN to predict and optimize the glucose yield, combining the effects of the cellulase and β‐glucosidase loads. Vani et al . developed an ANN model to predict with good accuracy the effect of biomass loading, substrate particle size and reaction time on glucose and xylose production during the enzymatic hydrolysis of rice straw.…”

Section: Introductionmentioning

confidence: 99%

Prediction of overall glucose yield in hydrolysis of pretreated sugarcane bagasse using a single artificial neural network: good insight for process development

Tovar

Rivera

Mariano

et al. 2017

J of Chemical Tech & Biotech

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BACKGROUND In this work a single artificial neural network (ANN) was used to model the overall yield of glucose (YGLC) as a function of a wide range of operating conditions of both pretreatment and enzymatic hydrolysis. RESULTS The model was validated experimentally and presented good predictions of YGLC. Sensitivity analysis using the ANN model indicated that most of the operating parameters, except for pretreatment time, were statistically significant (P‐value <0.05). Experiments showed that the processing of sugarcane bagasse (in natura) results in a satisfactory glucose yield of 69.34% when pretreated for 60 min with low initial biomass concentration and acid concentration (10% and 1.0% w/v), and followed by enzymatic hydrolysis for 72 h with 3.0% w/v substrate loading and 60 FPU per gWIS enzyme concentration. CONCLUSION This study demonstrated how pretreatment and enzymatic hydrolysis data can be used to parameterize a single ANN model. Acceptable predictions of YGLC are achieved in terms of RSD, MSE and R2. Supported by the model, this study provided a good insight for process development. © 2017 Society of Chemical Industry

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“…Bioconversion of lignocellulosic material to renewable fuels is currently receiving great interest since it does not impact food security [2]. Several studies on the enhancement of fermentable sugar release from lignocellulosic substrates have been reported [3][4][5]. Microwave-assisted pre-treatment has received increased attention due to its lower energy demand and shorter process times [6].…”

Section: Introductionmentioning

confidence: 99%

Kinetics of Bioethanol Production from Waste Sorghum Leaves Using Saccharomyces cerevisiae BY4743

Rorke

Kana

2017

Fermentation

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Kinetic models for bioethanol production from waste sorghum leaves by Saccharomyces cerevisiae BY4743 are presented. Fermentation processes were carried out at varied initial glucose concentrations (12.5-30.0 g/L). Experimental data on cell growth and substrate utilisation fit the Monod kinetic model with a coefficient of determination (R 2 ) of 0.95. A maximum specific growth rate (µ max ) and Monod constant (K S ) of 0.176 h −1 and 10.11 g/L, respectively, were obtained. The bioethanol production data fit the modified Gompertz model with an R 2 value of 0.98. A maximum bioethanol production rate (r p,m ) of 0.52 g/L/h, maximum potential bioethanol concentration (P m ) of 17.15 g/L, and a bioethanol production lag time (t L ) of 6.31 h were observed. The obtained Monod and modified Gompertz coefficients indicated that waste sorghum leaves can serve as an efficient substrate for bioethanol production. These models with high accuracy are suitable for the scale-up development of bioethanol production from lignocellulosic feedstocks such as sorghum leaves.

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Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling

Cited by 86 publications

References 27 publications

Glucose recovery from different corn stover fractions using dilute acid and alkaline pretreatment techniques

Glucose recovery from different corn stover fractions using dilute acid and alkaline pretreatment techniques

Prediction of overall glucose yield in hydrolysis of pretreated sugarcane bagasse using a single artificial neural network: good insight for process development

Kinetics of Bioethanol Production from Waste Sorghum Leaves Using Saccharomyces cerevisiae BY4743

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