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

Response Surface Methodology01:16

Response Surface Methodology

647
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
647
Genetics of Speciation02:16

Genetics of Speciation

21.0K
Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
21.0K
Hybrid Zones02:29

Hybrid Zones

21.8K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
21.8K
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

48.8K
sp3d and sp3d 2 Hybridization
48.8K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

404
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
404
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

66.9K
The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
66.9K

You might also read

Related Articles

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

Sort by
Same author

An "In-Situ Binding" Approach to Produce Torrefied Biomass Briquettes.

Bioengineering (Basel, Switzerland)·2019
Same author

Effect of Deep Drying and Torrefaction Temperature on Proximate, Ultimate Composition, and Heating Value of 2-mm Lodgepole Pine (Pinus contorta) Grind.

Bioengineering (Basel, Switzerland)·2017
See all related articles

Related Experiment Video

Updated: Jan 30, 2026

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
09:16

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

1.6K

Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm.

Jaya Shankar Tumuluru1, Dean J Heikkila2

  • 1Idaho National Laboratory, 750 MK Simpson Blvd., Energy Systems Laboratory, P.O. Box: 1625, Idaho Falls, ID 83415-3570, USA. jayashankar.tumuluru@inl.gov.

Bioengineering (Basel, Switzerland)
|January 30, 2019
PubMed
Summary

Optimizing corn stover grinding is crucial for renewable energy. Higher moisture and grinder speed increase density, while specific energy use is minimized at lower speeds and moisture.

Keywords:
corn stovergrinding processhybrid genetic algorithmoptimizationrenewable energyresponse surface methodology

More Related Videos

GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization
11:38

GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization

Published on: October 24, 2011

15.9K
Fractionation of Lignocellulosic Biomass using the OrganoCat Process
06:19

Fractionation of Lignocellulosic Biomass using the OrganoCat Process

Published on: June 5, 2021

4.6K

Related Experiment Videos

Last Updated: Jan 30, 2026

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
09:16

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

1.6K
GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization
11:38

GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization

Published on: October 24, 2011

15.9K
Fractionation of Lignocellulosic Biomass using the OrganoCat Process
06:19

Fractionation of Lignocellulosic Biomass using the OrganoCat Process

Published on: June 5, 2021

4.6K

Area of Science:

  • Agricultural Engineering
  • Renewable Energy Sources
  • Biomass Processing

Background:

  • Agricultural waste, like corn stover, presents a significant renewable energy feedstock.
  • Utilizing corn stover can displace fossil fuels, contributing to energy sustainability.
  • Efficient processing of corn stover is key to unlocking its energy potential.

Purpose of the Study:

  • To investigate how corn stover moisture content and grinder speed affect grind physical properties.
  • To develop and optimize response surface models for predicting grind characteristics.
  • To identify optimal processing parameters for maximizing desired physical properties and minimizing energy consumption.

Main Methods:

  • Development of response surface models to analyze the impact of moisture content and grinder speed.
  • Utilizing surface plots to visualize interactions between processing parameters and grind properties.
  • Employing a hybrid genetic algorithm for optimizing the developed response surface models.

Main Results:

  • Increased corn stover moisture content and grinder speed positively influenced bulk and tapped density.
  • Final grind moisture content was strongly dependent on the initial moisture levels.
  • Optimal conditions for maximizing density and minimizing particle size were identified (17-19% moisture, 47-49 Hz speed).

Conclusions:

  • Corn stover processing parameters significantly impact grind physical properties and energy consumption.
  • Response surface modeling and genetic algorithms provide effective tools for process optimization.
  • Specific processing windows can maximize biomass density and minimize energy requirements for renewable energy applications.