Improved biobleaching of mixed hardwood pulp and process optimization using novel GA-ANN and GA-ANFIS hybrid statistical tools

Kumar, Vishal; Kumar, Ashwani; Chhabra, Deepak; Shukla, Pratyoosh

doi:10.1016/j.biortech.2018.09.115

Cited by 80 publications

(25 citation statements)

References 32 publications

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“…Table 1). The kappa number is an indication of the residual lignin content in pulp ber which can be used as an indicator of the e ciency of bleaching process (Kumar et al 2019). A reduction of kappa number and an increase in the percentage of brightness in our experiment was higher than those reported in previous studies.…”

Section: Discussioncontrasting

confidence: 71%

“…A reduction of kappa number and an increase in the percentage of brightness in our experiment was higher than those reported in previous studies. Kumar et al (2019) reported that the brightness of hardwood pulp pre-treated with xylanase increased 21.56% while the kappa number decreased 20% compared to the untreated samples. According to Wu et al (2018) who reported that the brightness of eucalyptus kraft was signi cantly increased about 14.5%, while 24.5% of kappa number was reduced after bleaching by endoxylanase from Streptomyces griseorubens LH-3.…”

Section: Discussionmentioning

confidence: 99%

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The Effect of Endo-1,4-β-Xylanase From Thermomyces Dupontii KKU−CLD−E2−3 on Eucalyptus Pulp Bleaching and Effluent Treatment

Seemakram¹,

Harada²,

Ekprasert³

et al. 2020

Preprint

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Endo-1,4-β-xylanase-chlorine dioxide bleaching of eucalyptus pulp and analysis of effluent was investigated. The eucalyptus pulp bleaching in D0 stage was prepared with enzyme dose 100 Unit of 10% consistency and incubated at 70 °C for 30 min. The brightness level was achieved up to 72.50 (% ISO). The kappa number and viscosity of eucalyptus pulp was found to be 1.70 and 8.90 (cp), respectively. The paper from pulp bleaching with enzyme has shown an increase in brightness, breaking length, bursting strength, and tearing resistance to be 89.60% IOS, 37.27 Nm/g, 138.81 kPa and 218.02 mN, respectively. The SEM and FTIR analysis of pulp fibers revealed a significance of morphological and structural changes. The analysis of the effluent also showed statistically-significant differences in TOC, BOD, TDS and TSS. The FTIR analysis of the effluent showed the organic compounds and chloride dioxin in cases of those treated by chemicals.

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Section: Discussioncontrasting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

The Effect of Endo-1,4-β-Xylanase From Thermomyces Dupontii KKU−CLD−E2−3 on Eucalyptus Pulp Bleaching and Effluent Treatment

Seemakram¹,

Harada²,

Ekprasert³

et al. 2020

Preprint

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“…An enzyme's optimum temperature is the indicator of its thermal activity (Liu et al, 2012). On the one hand, high temperature increases substrates' solubility and bioavailability, accelerates molecular dynamics, and decreases the probability of microbial contamination significantly (Kumar et al, 2013, 2019). On the other hand, numerous biological processes are carried out at mild or cold temperatures (Collins et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…In another research, Ariaeenejad et al applied a regression model based on a pseudo amino acid composition (PAAC; Chou, 2009) to predict the optimum temperature and pH of xylanase in strains of Bacillus subtilis (Ariaeenejad et al, 2018). Genetic algorithm‐artificial neural network has been employed for the optimization of xylanase production for industrial purposes (Kumar, Chhabra, et al, 2017; Kumar et al, 2019). In a 2010 study, to find features with the highest correlation with the thermostability of proteins, a K‐means clustering algorithm, and a decision tree have been employed (Ebrahimi & Ebrahimie, 2010).…”

Section: Introductionmentioning

confidence: 99%

A generalized machine‐learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study

Shahraki

Farhadyar

Kavousi

et al. 2020

Biotech & Bioengineering

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Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time‐effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence‐based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non‐thermophilic, thermophilic, and hyper‐thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross‐validations as well as holdout tests, and it is freely accessible as a web‐service at arimees.com.

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“…In another research, Ariaeenejad et al applied a regression model based on a pseudo amino acid composition (PAAC) (29) to predict the optimum temperature and pH of xylanase in strains of Bacillus subtilis enzymes (30). Genetic Algorithm-Artificial Neural Network 5 (GA-ANN) have been employed for the optimization of xylanase production for industrial purposes (31,32) . In order to find features with the highest correlation with the thermostability of proteins, Kmean algorithm clustering method and a decision tree have been employed (33).…”

Section: Introductionmentioning

confidence: 99%

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study

Shahraki

Farhadyar

Kavousi

et al. 2019

Preprint

View full text Add to dashboard Cite

Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose polysaccharide, which is present in lignocellulosic biomass. The optimum temperature of enzymes is the indicator of their thermal activity and is an essential factor to be considered when choosing an appropriate biocatalyst for a particular purpose. Therefore, insilico prediction of this enzymatic attribute is a significant cost and time-effective step in the effort to identify and characterize novel enzymes. The objective of this study was to develop an accurate computational method to predict the thermal activity status of xylanases from glycoside hydrolases families 10 and 11, the most prevalent known xylanase families. Here we present TAXyl (Thermal Activity Prediction for Xylanase), a new sequence-based machine learning method that has been trained using a selected combination of various physicochemical protein features. This ensemble of four supervised learning algorithms discriminates mesophilic, thermophilic, and hyper-thermophilic xylanases based on their optimum temperature with the process of soft-voting. TAXyl's performance was ultimately evaluated through multiple iterations of six-fold cross-validations, and it exhibited a mean accuracy of ~0.94, F1-score of ~0.91, and MCC of ~0.9. Additionally, the model was tested on previously unseen data and depicted relatively similar performance. To the best of our knowledge, this tool is the most accurate and practical prediction tool currently available and operating on this class of enzymes.TAXyl is freely accessible as a web-service at http://arimees.com/ and provides users with several features to facilitate the characterization of GH10 and GH11 xylanases.

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Improved biobleaching of mixed hardwood pulp and process optimization using novel GA-ANN and GA-ANFIS hybrid statistical tools

Cited by 80 publications

References 32 publications

The Effect of Endo-1,4-β-Xylanase From Thermomyces Dupontii KKU−CLD−E2−3 on Eucalyptus Pulp Bleaching and Effluent Treatment

The Effect of Endo-1,4-β-Xylanase From Thermomyces Dupontii KKU−CLD−E2−3 on Eucalyptus Pulp Bleaching and Effluent Treatment

A generalized machine‐learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study

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