Rheological behavior of the sludge in a long-running anaerobic digestor: Essential factors to optimize the operation

This study investigates the use of pretreatment and enzymatic hydrolysis side streams and conversion to lignocellulose nanofibers. We used a steam-exploded and partial enzymatic hydrolyzed hardwood pulp and an organosolv pretreated softwood pulp to prepare lignocellulose nanofibers (LCNF) via microfluidization. The energies applied on fibrillation were estimated to examine the energy consumption levels of LCNF production. The energy consumptions of the fibrillation processes of the hardwood LCNF production and the softwood LCNF production were about 7040-14080 kWh/ton and 4640 kWh/ton on a dry material basis, respectively. The morphology and dimension of developed hardwood and softwood LCNFs and the stability and rheological behavior of their suspensions were investigated and are discussed.

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Temperature Dependent Rheological Behavior of Zinc Oxide Based Water Nanofluid

Proceeding of Proceedings of the 25th National and 3rd International ISHMT-ASTFE Heat and Mass Transfer Conference (IHMTC-2019) ◽

10.1615/ihmtc-2019.540 ◽

2019 ◽

Author(s):

Ashutosh Pare ◽

Subrata Kumar Ghosh

Keyword(s):

Zinc Oxide ◽

Rheological Behavior ◽

Temperature Dependent

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Investigation of Rheological Behavior of Modified Polychloroprene Adhesive Compositions

Leather and Footwear Journal ◽

10.24264/lfj.14.4.3 ◽

2014 ◽

Vol 14 (4) ◽

pp. 227-242

Author(s):

Darina Ivanova Zheleva ◽

Vassil Ivanov Samichkov

Keyword(s):

Rheological Behavior ◽

Adhesive Compositions

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Rheological Behavior of Coating Color and Effects of Rheology Modifier

JAPAN TAPPI JOURNAL ◽

10.2524/jtappij.71.850 ◽

2017 ◽

Vol 71 (8) ◽

pp. 850-854

Author(s):

Kazutaka Kasuga ◽

Koichi Tadaki ◽

Kaori Sasaki

Keyword(s):

Rheological Behavior ◽

Coating Color ◽

Rheology Modifier

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Determination of rheological parameters of giezekus and Vinogradov-Malkin models for description of the rheological behavior of polyethylene melts

Transactions of Academenergo ◽

10.34129/2070-4755-2020-58-1-7-17 ◽

2020 ◽

Vol 58 (1) ◽

pp. 7-17

Author(s):

E.K. Vachagina ◽

◽

A.I. Kadyirov ◽

J.V. Karaeva ◽

R.M. Garipov ◽

...

Keyword(s):

Rheological Behavior ◽

Rheological Parameters ◽

Polyethylene Melts

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MODELING FEEDBACKS BETWEEN TRANSIENT THERMAL EVOLUTION, RHEOLOGICAL BEHAVIOR, AND STRAIN LOCALIZATION IN COLLISIONAL THRUST BELTS

10.1130/abs/2017am-304469 ◽

2017 ◽

Author(s):

J. Ryan Thigpen ◽

◽

Kyle T. Ashley ◽

Richard D. Law

Keyword(s):

Rheological Behavior ◽

Strain Localization ◽

Thermal Evolution ◽

Thrust Belts ◽

Transient Thermal

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Correlation between large amplitude oscillatory shear (LAOS) and steady shear of soft solids at the onset of the fluid rheological behavior

Polymer ◽

10.1016/j.polymer.2016.06.003 ◽

2016 ◽

Vol 104 ◽

pp. 215-226 ◽

Cited By ~ 9

Author(s):

Daniela Coblas ◽

Diana Broboana ◽

Corneliu Balan

Keyword(s):

Rheological Behavior ◽

Large Amplitude ◽

Oscillatory Shear ◽

Steady Shear ◽

Large Amplitude Oscillatory Shear ◽

Soft Solids

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Rheological behavior and morphology of poly(lactic acid)/low‐density polyethylene blends based on virgin and recycled polymers: Compatibilization with natural surfactants

Journal of Applied Polymer Science ◽

10.1002/app.50590 ◽

2021 ◽

pp. 50590

Author(s):

Francesco Casamento ◽

Alessandra D'Anna ◽

Rossella Arrigo ◽

Alberto Frache

Keyword(s):

Lactic Acid ◽

Rheological Behavior ◽

Low Density Polyethylene ◽

Poly Lactic Acid ◽

Low Density ◽

Polyethylene Blends ◽

Natural Surfactants

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Rheological behavior of scCO2-Foam for improved hydrocarbon recovery: Experimental and deep learning approach

Journal of Petroleum Science and Engineering ◽

10.1016/j.petrol.2021.108646 ◽

2021 ◽

Vol 203 ◽

pp. 108646

Author(s):

S. Ahmed ◽

W. Alameri ◽

W.W. Ahmed ◽

S.A. Khan

Keyword(s):

Deep Learning ◽

Rheological Behavior ◽

Learning Approach ◽

Hydrocarbon Recovery

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STRONG: metagenomics strain resolution on assembly graphs

Genome Biology ◽

10.1186/s13059-021-02419-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Christopher Quince ◽

Sergey Nurk ◽

Sebastien Raguideau ◽

Robert James ◽

Orkun S. Soyer ◽

...

Keyword(s):

Time Series ◽

De Novo ◽

Single Copy ◽

Bayesian Algorithm ◽

Anaerobic Digestor ◽

Core Genes

AbstractWe introduce STrain Resolution ON assembly Graphs (STRONG), which identifies strains de novo, from multiple metagenome samples. STRONG performs coassembly, and binning into metagenome assembled genomes (MAGs), and stores the coassembly graph prior to variant simplification. This enables the subgraphs and their unitig per-sample coverages, for individual single-copy core genes (SCGs) in each MAG, to be extracted. A Bayesian algorithm, BayesPaths, determines the number of strains present, their haplotypes or sequences on the SCGs, and abundances. STRONG is validated using synthetic communities and for a real anaerobic digestor time series generates haplotypes that match those observed from long Nanopore reads.

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