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

Gama, Repson; Dyk, J. Susan van; Burton, Mike. H.; Pletschke, Brett I.

doi:10.1007/s13205-017-0754-1

Cited by 23 publications

(8 citation statements)

References 43 publications

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“…The neutral network model has been trained and built on the features of 62 corn stover samples through rolling learning-prediction approach. The selection of the most appropriate parameters for ANN modeling is considered of paramount importance for prediction of the hydrolysis process [ 27 ]. In the present work, to test the prediction capabilities of the neural network model, the predicted values obtained from the model are compared with the experimental values.…”

Section: Resultsmentioning

confidence: 99%

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Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features

Gao

Chen

Zhang

2018

BioMed Research International

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The classic assay for a large population biomass is time-consuming, labor intensive, and chemically expensive. This paper would find out a rapid assay for predicting biomass digestibility from biomass structural features without hydrolysis. We examined the 62 representative corn stover accessions that displayed a diverse cell-wall composition and varied biomass digestibility. Correlation analysis was firstly to detect effects of cell-wall compositions and wall polymer features on corn stover digestibility. Based on the dependable relationship of structural features and digestibility, a neural networks model has been developed and successfully predicted the corn stover saccharification based on the features without enzymatic hydrolysis. The actual measured and net-simulated predicted corn stover saccharification had good results as mean square error of 1.80E-05, coefficient of determination of 0.942 and average relative deviation of 3.95. The trained networks satisfactorily predicted the saccharification results based on the features of corn stover. Predicting the corn stover saccharification without hydrolysis will reduce capital and operational costs for corn stover purchasing and storage.

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Section: Resultsmentioning

confidence: 99%

“…The agreement in measured and net-simulated slopes and intercepts indicated the trained networks satisfactorily predicted the saccharification results based on the features of corn stover. It has been reported in literature that ANNs are flexible as new data can be added anytime giving fitting [ 27 ].…”

Section: Resultsmentioning

confidence: 99%

Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features

Gao

Chen

Zhang

2018

BioMed Research International

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“…An optimized ANN model was developed based on hydrolysis conditions to maximize sugar yields. Gama et al (2017) established an ANN applied to predict the best conditions to maximize glucose and reduce sugar yields from enzymatic hydrolysis of apple pomace using as inputs: temperature, pH, enzyme, and substrate loadings (Gama et al 2017). In this case, the training and testing data set contained minimal fluctuations, which showed that enzymatic hydrolysis could benefit significantly from ANN technologies because it does not require specific feedstock composition details as input (Pomeroy et al 2022).…”

Section: Processes Of the Biochemical Biorefinerymentioning

confidence: 99%

Integrating the new age of bioeconomy and Industry 4.0 into biorefinery process design

Clauser

Felissia

Área

et al. 2022

BioRes

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Manufacturing processes and their economy are dramatically evolving due to machinery and digital control improvements. Artificial intelligence, big data analytics, and the Internet of Things are key tools for this new industrial revolution era based on Industry 4.0. Bioeconomy and circular economy concepts have appeared in the forest, agriculture, food, pharmaceutical, pulp and paper, chemical, biotechnological, and energy areas, etc., to achieve sustainable economic growth development via biomass valorization in a biorefinery platform. Biorefinery process development at an industrial scale requires the previous design and assessment of processes and technologies. Therefore, economic, environmental, and social factors should be evaluated to prevent the failure in one of these issues that could affect the performance of the others. With a growing interest in sustainable economic development, there is a need to incorporate new technologies early enough in the process design. This study aims to better understand how Industry 4.0 era tools can bring new solutions to the biorefinery process design, in terms of the technical, economic, environmental, and social factors. Thus, these tools could improve and revolutionize the process selection optimization, provide alternatives for biomass valorization, integration strategies, and the metrics selection for process evaluation, adding the approach toward sustainable economic development.

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“…The initial rise in TRS levels could be attributed to the requirement of an optimum amount of substrate proportional to enzyme levels present in the reaction mixture. The decrease in the hydrolysis yield at high substrate loading might occur due to adsorption of enzymes on to unproductive sites in the biomass, mass transfer limitations, or end-product inhibition of enzyme due to higher sugar levels (Gama et al 2017).…”

Section: Biomass Saccharificationmentioning

confidence: 99%

Saccharification of Parthenium hysterophorus biomass using cellulase from Streptomyces sp. NAA2

Saini

Aggarwal

2019

Ann Microbiol

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Parthenium hysterophorus biomass can be used as a non-conventional renewable feedstock for the production of bioethanol. Therefore, the present work was designed to hydrolyze P. hysterophorus biomass using cellulase enzyme produced from an actinomycete, i.e., Streptomyces sp. NAA2 using P. hysterophorus biomass as a substrate. The isolate NAA2 was identified by molecular characterization of 16SrDNA. The enzyme production by strain NAA2 was enhanced by optimization studies conducted under submerged fermentation conditions using P. hysterophorus as a substrate. The crude enzyme produced under optimized conditions was used to hydrolyze alkali-acid pretreated P. hysterophorus biomass. The highest CMCase production was achieved in 4-5 days when steam-pretreated P. hysterophorus biomass was used at 1% (w/v) concentration, using 2 discs (1 disc = 5 × 10 7 spores/ml) of inoculum, an initial pH 6.5, temperature at 40°C, an agitation speed of 120-150 rpm, and by supplementing fermentation medium with 1.5% (w/v) carboxymethyl cellulose (CMC) as additional carbon source. Under optimized conditions, the actinomycete strain NAA2 showed production of 0.967 ± 0.016 U/ml CMCase, 0.116 ± 0.08 FPU/ ml FPase, and 0.22 ± 0.012 U/ml β-glucosidase enzymes. On utilizing the cellulase enzyme for biomass hydrolysis, maximum 18.2% saccharification yield (of cellulose 0.202 g/g) was achieved in 96 h when enzyme and substrate levels were 30 FPU/ 100 ml and 2% (w/v) respectively. Parthenium hysterophorus biomass can be hydrolyzed enzymatically yielding considerable amounts of total reducing sugars. It can, therefore, be used as a feedstock for the production of bioethanol. Also, it has the potential to act as a substrate for the production of cellulases. Furthermore, the improved cellulolytic potential of Streptomyces sp. NAA2 can be exploited in various industrial applications.

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

Cited by 23 publications

References 43 publications

Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features

Neural Network Prediction of Corn Stover Saccharification Based on Its Structural Features

Integrating the new age of bioeconomy and Industry 4.0 into biorefinery process design

Saccharification of Parthenium hysterophorus biomass using cellulase from Streptomyces sp. NAA2

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