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Related Experiment Video

Updated: Jul 3, 2026

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure

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Published on: July 19, 2018

Mathematical modelling of a composting process, and validation with experimental data.

Ana P Gomes¹, F Antunes Pereira

¹Department of Environment and Planning, University of Aveiro, 3810 Aveiro, Portugal. pgomes@dao.ua.pt

Waste Management & Research : the Journal of the International Solid Wastes and Public Cleansing Association, ISWA

|July 25, 2008

Summary

This study presents a mathematical model for composting rice and rice husks. The model identifies hydrolysis as the rate-limiting step, suggesting enzymes, not microbes, accelerate composting.

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Area of Science:

Biochemical Engineering
Environmental Science
Applied Mathematics

Background:

Composting is a vital biological process for organic waste management.
Developing practical mathematical models for composting is crucial for optimizing efficiency.
Previous models often overlook key chemical and microbiological interactions.

Purpose of the Study:

To develop a practical mathematical model for composting rice and rice husks.
To validate the model using experimental data on biological aerobic decomposition.
To identify the rate-limiting steps and kinetic parameters of the composting process.

Main Methods:

Utilized a parameter aggregation model based on pseudo-first-order reactions in series.
Modeled the aerobic decomposition of rice and rice husks mixture.
Determined kinetic parameters and their temperature dependence.

Main Results:

The biological aerobic decomposition followed a hydrolysis step and a biochemical oxidative step.
Hydrolysis was identified as the slowest, rate-determining step in the composting process.
Overall reaction rate was independent of oxygen concentration.

Conclusions:

The developed mathematical model accurately explains the experimental composting data.
Adding hydrolytic enzymes, rather than microbial cultures, is recommended to accelerate composting.
The findings provide insights for optimizing composting strategies for agricultural waste.