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

Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...
Determination of Molar Masses of Polymers I01:24

Determination of Molar Masses of Polymers I

Polymerization produces macromolecules with a range of chain lengths due to the random nature of molecular growth processes. As chains form and terminate at different stages, a single polymer sample contains molecules of varying sizes rather than a uniform structure. This variability is described using average molar masses and distribution-related parameters, which together provide a comprehensive understanding of polymer characteristics.The distribution of molar masses plays a critical role in...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Determination of Molar Masses of Polymers II01:27

Determination of Molar Masses of Polymers II

Polymer samples typically consist of macromolecular chains with a distribution of lengths, resulting in a range of molar masses rather than a single discrete value. Conventional descriptors such as the number-average molar mass and weight-average molar mass quantify this distribution but do not fully capture polymer behavior in solution..The viscosity-average molar mass provides a more realistic description of polymer behavior in solution because it accounts for the enhanced contribution of...

You might also read

Related Articles

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

Sort by
Same author

Porous Organic Polymers: From Molecular Design to Scalable Technologies.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Geological and Technical Foundations of Offshore CO<sub>2</sub> Storage in Depleted Reservoirs.

ACS omega·2026
Same author

Retraction notice to "Modification of SBA-15 mesoporous silica as an active heterogeneous catalyst for the hydroisomerization and hydrocracking of n-heptane" [Heliyon 8 (2022) e09737].

Heliyon·2025
Same author

Estimating advancing and receding contact angles for pure and mixed liquids on smooth solid surfaces using the PCP-SAFT equation of state.

Physical chemistry chemical physics : PCCP·2025
Same author

Improving the anaerobic digestion of sewage sludge by adding cobalt nanoparticles.

Environmental technology·2024
Same author

A review on development and modification strategies of MOFs Z-scheme heterojunction for photocatalytic wastewater treatment, water splitting, and DFT calculations.

Heliyon·2024

Related Experiment Video

Updated: Jun 19, 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

9.9K

Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction.

Abbas Azarpour1, Sohrab Zendehboudi2, Noori M Cata Saady3

  • 1Department of Engineering and Physics, Southern Arkansas University, Magnolia, Arkansas 71753, United States.

ACS Omega
|March 3, 2025
PubMed
Summary

This study optimizes lignocellulosic biomass torrefaction using advanced hybrid AI models. The coupled simulated annealing-least-squares support vector machine (CSA-LSSVM) achieved the highest accuracy, identifying temperature as the key factor for efficient bioenergy production.

More Related Videos

Method to Produce Durable Pellets at Lower Energy Consumption Using High Moisture Corn Stover and a Corn Starch Binder in a Flat Die Pellet Mill
08:52

Method to Produce Durable Pellets at Lower Energy Consumption Using High Moisture Corn Stover and a Corn Starch Binder in a Flat Die Pellet Mill

Published on: June 15, 2016

20.9K
Fractionation of Lignocellulosic Biomass using the OrganoCat Process
06:19

Fractionation of Lignocellulosic Biomass using the OrganoCat Process

Published on: June 5, 2021

3.9K

Related Experiment Videos

Last Updated: Jun 19, 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

9.9K
Method to Produce Durable Pellets at Lower Energy Consumption Using High Moisture Corn Stover and a Corn Starch Binder in a Flat Die Pellet Mill
08:52

Method to Produce Durable Pellets at Lower Energy Consumption Using High Moisture Corn Stover and a Corn Starch Binder in a Flat Die Pellet Mill

Published on: June 15, 2016

20.9K
Fractionation of Lignocellulosic Biomass using the OrganoCat Process
06:19

Fractionation of Lignocellulosic Biomass using the OrganoCat Process

Published on: June 5, 2021

3.9K

Area of Science:

  • Renewable Energy and Biofuels
  • Chemical Engineering and Process Optimization
  • Artificial Intelligence in Energy

Background:

  • Growing global energy demand necessitates sustainable alternatives to fossil fuels.
  • Renewable energy sources, particularly biomass, offer a promising solution to mitigate climate change.
  • Torrefaction is an effective thermochemical process for enhancing biomass properties for energy applications.

Purpose of the Study:

  • To analyze and model the torrefaction process of lignocellulosic biomass.
  • To develop predictive models for solid yield based on biomass properties and operating conditions.
  • To identify key parameters influencing the torrefaction efficiency for bioenergy optimization.

Main Methods:

  • Hybrid machine learning models: Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Coupled Simulated Annealing-Least-Squares Support Vector Machine (CSA-LSSVM).
  • Gene Expression Programming (GEP) for developing correlations between biomass characteristics, operating conditions, and solid yield.
  • Parametric sensitivity analysis to determine the influence of various factors on the torrefaction process.

Main Results:

  • The CSA-LSSVM model demonstrated superior accuracy with R² = 0.98, MSE = 0.00082, and AARE% = 2.61%.
  • Key influential variables identified include residence time, temperature, and moisture content.
  • Temperature was found to be the most critical parameter in the lignocellulosic biomass torrefaction process.

Conclusions:

  • The developed hybrid AI models accurately predict biomass torrefaction yield, aiding process optimization.
  • Findings provide crucial insights for the bioenergy industry to achieve cost-effective and energy-efficient operations.
  • Optimized torrefaction can significantly contribute to reducing CO₂ emissions and advancing sustainable energy solutions.