Methane Production from the Pyrolysis–Catalytic Hydrogenation of Waste Biomass: Influence of Process Conditions and Catalyst Type

A review of the production of activated carbons from waste biomass has been presented. The effects of various process parameters on the pyrolysis stage have been reviewed. Influences of activating conditions, physical and chemical, on the active carbon properties have been discussed. Under certain process conditions several active carbons with BET surface areas, ranging between 250 and 2410 m2/g and pore volumes of 0.022 and 91.4 cm3/g, have been produced. A comparison in characteristics and uses of activated carbons from waste biomass with those of commercial carbons has been made. Waste biomass being highly efficient, low cost and renewable sources of activated carbon production. Bangladesh J. Sci. Ind. Res. 47(4), 347-364, 2012 DOI: http://dx.doi.org/10.3329/bjsir.v47i4.14064

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Synthetic natural gas production from the three stage (i) pyrolysis (ii) catalytic steam reforming (iii) catalytic hydrogenation of waste biomass

Fuel Processing Technology ◽

10.1016/j.fuproc.2020.106515 ◽

2020 ◽

Vol 208 ◽

pp. 106515

Author(s):

Mohammad M. Jaffar ◽

Mohamad A. Nahil ◽

Paul T. Williams

Keyword(s):

Natural Gas ◽

Steam Reforming ◽

Catalytic Hydrogenation ◽

Gas Production ◽

Stage I ◽

Waste Biomass ◽

Natural Gas Production ◽

Synthetic Natural Gas ◽

Catalytic Steam Reforming

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Pyrolysis of waste biomass: investigation of fast pyrolysis and slow pyrolysis process conditions on product yield and gas composition

Journal of the Energy Institute ◽

10.1179/1743967113z.00000000067 ◽

2013 ◽

Vol 86 (4) ◽

pp. 233-241 ◽

Cited By ~ 14

Author(s):

Q M K Waheed ◽

M A Nahil ◽

P T Williams

Keyword(s):

Fast Pyrolysis ◽

Product Yield ◽

Process Conditions ◽

Gas Composition ◽

Waste Biomass ◽

Pyrolysis Process ◽

Slow Pyrolysis

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Influence of process conditions on the kinetics of the liquid-phase catalytic hydrogenation of aromatic nitro compounds

Bulletin of the Academy of Sciences of the USSR Division of Chemical Science ◽

10.1007/bf00953458 ◽

1983 ◽

Vol 32 (4) ◽

pp. 692-695 ◽

Cited By ~ 4

Author(s):

I. A. Makaryan ◽

V. I. Savchenko ◽

Kh. A. Brikenshtein

Keyword(s):

Liquid Phase ◽

Catalytic Hydrogenation ◽

Nitro Compounds ◽

Process Conditions ◽

Aromatic Nitro Compounds ◽

Aromatic Nitro ◽

Kinetics Of

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Chemical reactions in the hydrothermal liquefaction of biomass and in the catalytic hydrogenation upgrading of biocrude

Green Chemistry ◽

10.1039/d0gc02893b ◽

2021 ◽

Author(s):

Botian Hao ◽

Donghai Xu ◽

Guanyu Jiang ◽

Tanveer Ahmed Sabri ◽

Zefeng Jing ◽

...

Keyword(s):

Chemical Reactions ◽

Residence Time ◽

Reaction Temperature ◽

Catalytic Hydrogenation ◽

Hydrothermal Liquefaction ◽

Operating Parameters ◽

Catalyst Type

This article systematically describes chemical reactions in biomass HTL and the catalytic hydrogenation upgrading of the obtained biocrude and analyze the effects of operating parameters on these two processes, such as reaction temperature, residence time and catalyst type.

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Chryseobacterium aquifrigidense FANN1 Produced Detergent-Stable Metallokeratinase and Amino Acids Through the Abasement of Chicken Feathers

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.720176 ◽

2021 ◽

Vol 9 ◽

Author(s):

Amahle Bokveld ◽

Nonso E. Nnolim ◽

Uchechukwu U. Nwodo

Keyword(s):

Amino Acids ◽

Free Amino ◽

Incubation Temperature ◽

Residual Activity ◽

Green Technology ◽

Process Conditions ◽

Good Prospect ◽

Potential Candidate ◽

Waste Biomass ◽

Repressive Effect

Microbial keratinases’ versatility in the beneficiation of keratinous waste biomass into high-value products prompts their application in diverse spheres hence, advancing green technology and the bioeconomy. Consequently, a feather-degrading Chryseobacterium aquifrigidense FANN1 (NCBI: MW169027) was used to produce keratinase, and its biochemical properties were determined. The optimization of physicochemical parameters and analysis of the free amino acid constituents of the feather hydrolysate were also carried out. FANN1 showed a maximum keratinase yield of 1,664.55 ± 42.43 U/mL after 72 h, at optimal process conditions that included initial medium pH, incubation temperature, inoculum size, and chicken feather concentration of 8, 30°C, 4% (v/v), and 15 (g/L), respectively. Analysis of degradation product showed 50.32% and 23.25% as the protein value and total free amino acids, respectively, with a relatively high abundance of arginine (2.25%) and serine (2.03%). FANN1 keratinase was optimally active at pH 8.0 and relatively moderate to high temperature (40–50°C). EDTA and 1,10-phenanthroline inhibited the keratinase activity, and that suggests a metallo-keratinase. The enzyme showed remarkable stability in the presence of chemical agents, with residual activity 141 ± 10.38%, 98 ± 0.43%, 111 ± 1.73%, 124 ± 0.87%, 104 ± 3.89%, 107 ± 7.79%, and 112 ± 0.86% against DTT, H2O2, DMSO, acetonitrile, triton X-100, tween-80, and SDS, respectively. The residual activity of FANN1 keratinase was enhanced by Sunlight (129%), Ariel (116%), MAQ (151%), and Surf (143%) compared to the control after 60 min preincubation. Likewise, the enzyme was remarkably stable in the presence Fe3+ (120 ± 5.06%), Ca2+ (100 ± 10.33%), Na+ (122 ± 2.95%), Al3+ (106 ± 10.33%); while Co2+ (68 ± 8.22%) and Fe2+ (51 ± 8.43%) elicited the most repressive effect on keratinase activity. The findings suggest that C. aquifrigidense FANN1 is a potential candidate for keratinous wastes bio-recycling, and the associated keratinase has a good prospect for application in detergent formulation.

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A Review on Machine Learning Application in Biodiesel Production Studies

International Journal of Chemical Engineering ◽

10.1155/2021/2154258 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yuanzhi Xing ◽

Zile Zheng ◽

Yike Sun ◽

Masoome Agha Alikhani

Keyword(s):

Machine Learning ◽

Fossil Fuels ◽

Environmental Benefits ◽

Biodiesel Production ◽

Process Conditions ◽

Transportation Fuels ◽

Production Studies ◽

Model Control ◽

Catalyst Type ◽

System Input

The consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce various energy sorts like electricity, power, and heating or to sustain transportation fuels. Biodiesel production is an intricate process that requires identifying unknown nonlinear relationships between the system input and output data; therefore, accurate and swift modeling instruments like machine learning (ML) or artificial intelligence (AI) are necessary to design, handle, control, optimize, and monitor the system. Among the biodiesel production modeling methods, machine learning provides better predictions with the highest accuracy, inspired by the brain’s autolearning and self-improving capability to solve the study’s complicated questions; therefore, it is beneficial for modeling (trans) esterification processes, physicochemical properties, and monitoring biodiesel systems in real-time. Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. Emissions composition and temperature estimation and motor performance analysis investigate in the consumption phase. Fatty methyl acid ester stands as the output parameter, and the input parameters include oil and catalyst type, methanol-to-oil ratio, catalyst concentration, reaction time, domain, and frequency. This paper will present a review and discuss various ML technology advantages, disadvantages, and applications in biodiesel production, mainly focused on recently published articles from 2010 to 2021, to make decisions and optimize, model, control, monitor, and forecast biodiesel production.

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Optimization of Remazol Black B Removal Using Biochar Produced from Caulerpa scalpelliformis Using Response Surface Methodology

Advances in Materials Science and Engineering ◽

10.1155/2021/1535823 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

R. Gokulan ◽

S. Balaji ◽

P. Sivaprakasam

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Dye Removal ◽

Regression Coefficient ◽

Process Conditions ◽

Waste Biomass ◽

Solution Ph ◽

Box Behnken Design ◽

Remazol Black B ◽

Remazol Black

Optimization of process conditions for the removal of Remazol Black B was investigated using response surface methodology (Box–Behnken design). The biodecolorization of dye was studied using biochar produced from waste biomass of Caulerpa scalpelliformis (marine seaweeds). The reactions were optimized by varying sorbent dosage, solution pH, temperature, and initial dye concentration. The results indicated that dye removal efficiency of 80.30% was attained at an operating condition of 4 g/L (sorbent dosage), 2.0 (solution pH), 35°C (temperature), and 0.25 mmol/L (initial dye concentration). The regression coefficient of the developed model was calculated to be 97% which shows good fit of the model.

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The anaerobic digestion microbiome: a collection of 1600 metagenome-assembled genomes shows high species diversity related to methane production

10.1101/680553 ◽

2019 ◽

Cited By ~ 8

Author(s):

Stefano Campanaro ◽

Laura Treu ◽

Luis M Rodriguez-R ◽

Adam Kovalovszki ◽

Ryan M Ziels ◽

...

Keyword(s):

Organic Matter ◽

Anaerobic Digestion ◽

Methane Production ◽

Metabolic Reconstruction ◽

Waste Biomass ◽

Dynamic Composition ◽

Extensive Evaluation ◽

Wide Range ◽

Biogas Reactors ◽

High Flexibility

AbstractBackgroundMicroorganisms in biogas reactors are essential for degradation of organic matter and methane production through anaerobic digestion process. However, a comprehensive genome-centric comparison, including relevant metadata for each sample, is still needed to identify the globally distributed biogas community members and serve as a reliable repository.ResultsHere, 134 publicly available datasets derived from different biogas reactors were used to recover 1,635 metagenome-assembled genomes (MAGs) representing different bacterial and archaeal species. All genomes were estimated to be >50% complete and nearly half were ≥90% complete with ≤5% contamination. In most samples, specialized microbial communities were established, while only a few taxa were widespread among the different reactor systems. Metabolic reconstruction of the MAGs enabled the prediction of functional traits related to biomass degradation and methane production from waste biomass. An extensive evaluation of the replication index provided an estimation of the growth rate for microbes involved in different steps of the food chain. The recovery of many MAGs belonging to Candidate Phyla Radiation and other underexplored taxa suggests their specific involvement in the anaerobic degradation of organic matter.ConclusionsThe outcome of this study highlights a high flexibility of the biogas microbiome. The dynamic composition and adaptability to the environmental conditions, including temperatures and a wide range of substrates, were demonstrated. Our findings enhance the mechanistic understanding of anaerobic digestion microbiome and substantially extend the existing repository of genomes. The established database represents a relevant resource for future studies related to this engineered ecosystem.

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