scholarly journals Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

3 Biotech ◽  
2017 ◽  
Vol 7 (2) ◽  
Author(s):  
Repson Gama ◽  
J. Susan Van Dyk ◽  
Mike. H. Burton ◽  
Brett I. Pletschke
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Enzymatic Hydrolysis ◽  
Apple Pomace ◽  
Optimal Conditions ◽  
Artificial Neural ◽  
Hydrolysis Of

scholarly journals Response surface methodology-artificial neural network based optimization and strain improvement of cellulase production by Streptomyces sp.

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Ega Soujanya Lakshmi ◽  
Manda Rama Narasinga Rao ◽  
Muddada Sudhamani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Wild Strain ◽  
Cellulase Activity ◽  
Cellulase Production ◽  
Optimal Conditions ◽  
Zone Formation ◽  
Artificial Neural

ABSTRACT Thirty seven different colonies were isolated from decomposing logs of textile industries. From among these, a thermotolerant, grampositive, filamentous soil bacteria Streptomyces durhamensis vs15 was selected and screened for cellulase production. The strain showed clear zone formation on CMC agar plate after Gram’s iodine staining.  Streptomyces durhamensis vs15 was further confirmed for cellulase production by estimating the reducing sugars through dinitrosalicylic acid (DNS) method. The activity was enhanced by sequential mutagenesis using three mutagens of ultraviolet irradiation (UV), N methyl-N’-nitro-N-nitrosoguanidine (NTG) and Ethyl methane sulphonate (EMS). After mutagenesis, the cellulase activity of GC23 (mutant) was improved to 1.86 fold compared to the wild strain (vs15). Optimal conditions for the production of cellulase by the GC 23 strain were evaluated using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Effect of pH, temperature, duration of incubation, , and substrate concentration on cellulase production were evaluated. Optimal conditions for the production of cellulase enzyme using Carboxy Methyl Cellulase as a substrate are 55 oC of temperature, pH of 5.0 and incubation for 40 h. The cellulase activity of the mutant Streptomyces durhamensis GC23 was further optimised to 2 fold of the activity of the wild type by RSM and ANN.  

scholarly journals Optimization of total flavonoid compound extraction from camellia sinesis using the artificial neural network and response surface methodology

2013 ◽  
Vol 67 (2) ◽  
pp. 249-259 ◽  
Author(s):  
Ivan Savic ◽  
Vesna Nikolic ◽  
Ivana Savic ◽  
Ljubisa Nikolic ◽  
Mihajlo Stankovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Ethanol Concentration ◽  
Total Flavonoid ◽  
Extraction Time ◽  
Optimal Conditions ◽  
Liquid Ratio ◽  
Artificial Neural ◽  
Mlp Model

The aim of this paper was to model and optimize the process of total flavonoid extraction from the green tea using the artificial neural network and response surface methodology, as well as the comparation of these optimization techniques. The extraction time, ethanol concentration and solid-to-liquid ratio were identified as the independent variables, while the yield of total flavonoid was selected as the dependent variable. Central composite design (CCD), using second-order polynomial model and multilayer perceptron (MLP) were used for fitting the obtained experimental data. The values of root mean square error, cross-validated correlation coefficient and normal correlation coefficient for both models indicate that the artificial neural network is better in prediction of total flavonoid yield than CCD. The optimal conditions using the desirability function at CCD model was achieved for the extraction time of 32.5 min, ethanol concentration of 100% (v/v) and solid-to-liquid ratio of 1:32.5 (m/v). The predicted yield at these conditions was 2.11 g/100 g of the dried extract (d.e.), while the experimentally obtained was 2.39 g/100 g d.e. The extraction process was optimized by the use of simplex method at MLP model. The optimal value of total flavonoid yield (2.80 g/100 g d.e.) was achieved after the extraction time of 27.2 min using ethanol concentration of 100% (v/v) at solid-to-liquid ratio of 1:20.7 (m/v). The predicted value of response under optimal conditions for MLP model was also experimentally confirmed (2.71 g/100 g d.e.).

Process Optimization Based on Artificial Neural Network of Potassium Hydroxide to Preparing Biodiesel

2012 ◽  
Vol 263-266 ◽  
pp. 2225-2229 ◽  
Author(s):  
Chang Mei Wang ◽  
Shao Bing Wu ◽  
Wu Di Zhang ◽  
Yu Bao Chen ◽  
Fang Yin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Conversion Rate ◽  
Orthogonal Experiment ◽  
Back Propagation ◽  
Molar Ratio ◽  
Optimal Conditions ◽  
Reaction Conditions ◽  
Network Prediction ◽  
Artificial Neural

In order to obtain the optimal technological conditions of preparing biodiesel, artificial neural network was used to study the biodiesel processing model on transesterification method based on the single factor experiment and orthogonal experiment. The results of experiment indicated that we used the back propagation BP algorithm of artificial neural network to set the network prediction model based on the orthogonal test data can forecast the biodiesel conversion rate under different reaction conditions more accurately.The optimal conditions were obtained from this network model as follows: Molar ratio of methanol to oil was 6:1, the catalyst was 1.0% (w/w, based on oil), reaction temperature and reaction time was 65°Cand 2.5h respectively. Under the optimal conditions, the conversion rate of prediction was 94.93%, the conversion rate of experiment was 95.42% and the relative error was 0.51% compared with the predicted value. Therefore, the network k model could reflect inherent law of sample.

Modeling the tryptic hydrolysis of pea proteins using an artificial neural network

LWT ◽  
2008 ◽  
Vol 41 (5) ◽  
pp. 942-945 ◽  
Author(s):  
Adam Buciński ◽  
Magdalena Karamać ◽  
Ryszard Amarowicz ◽  
Ronald B. Pegg
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pea Proteins ◽  
Artificial Neural ◽  
Hydrolysis Of ◽  
Tryptic Hydrolysis

scholarly journals Prediction of phenolic compounds and glucose content from dilute inorganic acid pretreatment of lignocellulosic biomass using artificial neural network modeling

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hongzhen Luo ◽  
Lei Gao ◽  
Zheng Liu ◽  
Yongjiang Shi ◽  
Fang Xie ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Enzymatic Hydrolysis ◽  
Lignocellulosic Biomass ◽  
Ann Model ◽  
Inorganic Acid ◽  
Acid Pretreatment ◽  
Inorganic Acids ◽  
Artificial Neural ◽  
Input Variables

AbstractDilute inorganic acids hydrolysis is one of the most promising pretreatment strategies with high recovery of fermentable sugars and low cost for sustainable production of biofuels and chemicals from lignocellulosic biomass. The diverse phenolics derived from lignin degradation during pretreatment are the main inhibitors for enzymatic hydrolysis and fermentation. However, the content features of derived phenolics and produced glucose under different conditions are still unclear due to the highly non-linear characteristic of biomass pretreatment. Here, an artificial neural network (ANN) model was developed for simultaneous prediction of the derived phenolic contents (CPhe) and glucose yield (CGlc) in corn stover hydrolysate before microbial fermentation by integrating dilute acid pretreatment and enzymatic hydrolysis. Six processing parameters including inorganic acid concentration (CIA), pretreatment temperature (T), residence time (t), solid-to-liquid ratio (RSL), kinds of inorganic acids (kIA), and enzyme loading dosage (E) were used as input variables. The CPhe and CGlc were set as the two output variables. An optimized topology structure of 6–12-2 in the ANN model was determined by comparing root means square errors, which has a better prediction efficiency for CPhe (R2 = 0.904) and CGlc (R2 = 0.906). Additionally, the relative importance of six input variables on CPhe and CGlc was firstly calculated by the Garson equation with net weight matrixes. The results indicated that CIA had strong effects (22%-23%) on CPhe or CGlc, then followed by E and T. In conclusion, the findings provide new insights into the sustainable development and inverse optimization of biorefinery process from ANN modeling perspectives. Graphical Abstract

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