Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bioreactor Controls-III01:22

Bioreactor Controls-III

Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
Biofuels01:25

Biofuels

The microbial conversion of organic matter into biofuels holds potential as a renewable energy source. Among biofuel sources, microalgae are recognized as a highly efficient and adaptable feedstock for biodiesel production, owing to their rapid biomass accumulation, elevated lipid productivity, and capacity to proliferate in diverse aquatic systems, including freshwater, marine, and wastewater habitats. Unlike terrestrial crops, microalgae do not compete for land and can achieve significantly...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Graph-based pan-genome reveals structural variations associated with agronomic traits in mung bean.

Nature genetics·2026
Same author

Glucagon-like peptide-1 receptor agonist prevents pulmonary fibrosis following acute COVID-19 infection associated with type 2 diabetes.

Journal of virology·2026
Same author

Cattle and human organoids reveal 2.3.4.4b H5N1 cross-species transmission potential and neuraminidase-specific neutralizing antibodies in humans.

Nature communications·2026
Same author

Sialic acid-anchored haemagglutinin stalk neutralizing antibody M-SiaB enhances protection against highly pathogenic influenza H5N1/Texas/2024.

Nature communications·2026
Same author

Plastrum Testudinis Extract Ameliorates Intervertebral Disc Degeneration by Suppressing NF-κB Mediated Senescence and Inflammation.

Journal of immunology research·2026
Same author

8-Chloroadenosine suppresses hepatocellular carcinoma progression via ADAR1/PPARγ axis-mediated lipid metabolism.

Genes & diseases·2026

Related Experiment Video

Updated: May 8, 2026

High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release
11:31

High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release

Published on: September 15, 2015

10.0K

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems.

Rupeng Wang1, Zixiang He1, Honglin Chen1

  • 1State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.

The Science of the Total Environment
|April 10, 2024
PubMed
Summary

Machine learning (ML) offers new ways to manage bioenergy systems, addressing sustainability and energy security. This review explores ML applications in bioenergy production, highlighting challenges and solutions for better forecasting and optimization.

Keywords:
Bioenergy productionBiomass conversionFeedstockFull-scale applicationMachine learning

More Related Videos

Pretreatment of Lignocellulosic Biomass with Low-cost Ionic Liquids
10:42

Pretreatment of Lignocellulosic Biomass with Low-cost Ionic Liquids

Published on: August 10, 2016

18.0K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

Related Experiment Videos

Last Updated: May 8, 2026

High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release
11:31

High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release

Published on: September 15, 2015

10.0K
Pretreatment of Lignocellulosic Biomass with Low-cost Ionic Liquids
10:42

Pretreatment of Lignocellulosic Biomass with Low-cost Ionic Liquids

Published on: August 10, 2016

18.0K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

Area of Science:

  • Bioenergy
  • Machine Learning
  • Sustainable Energy Systems

Background:

  • Growing concerns over fossil fuel depletion and carbon footprint drive interest in bioenergy.
  • Effective management, modeling, and forecasting of bioenergy systems are critical challenges.
  • Machine learning (ML) presents opportunities to optimize bioenergy production and consumption.

Purpose of the Study:

  • To review current ML techniques applied to bioenergy production.
  • To identify challenges in integrating ML with bioenergy research.
  • To propose solutions and discuss ML application scenarios across the bioenergy value chain.

Main Methods:

  • Comparative review of existing literature on ML in bioenergy.
  • Analysis of common issues in ML integration.
  • Discussion of ML application scenarios in bioenergy production, consumption, and environmental impact.

Main Results:

  • ML techniques are underutilized in bioenergy research.
  • Integration of ML faces challenges related to data, expertise, and methodology.
  • ML can enhance process-level understanding, improving techno-economic and socio-ecological aspects.

Conclusions:

  • Modernized bioenergy conversion processes supported by ML are essential for sustainability.
  • ML offers significant potential to improve the resilience and integrity of bioenergy production.
  • Further research and adoption of ML are crucial for advancing sustainable bioenergy solutions.