This document summarizes work to model biomass piles to help manage them. A mathematical model is being developed using concepts of heat and mass transfer to predict temperature rise within piles. The model accounts for biological growth of bacteria, oxygen consumption during decomposition, and heat generation from chemical reactions. Measured data from wireless sensors will be used to monitor piles and improve the model. The goals are to determine best practices for pile management and develop industry standards.
2. Team
Dr. Suzanne Wetzel,
NRCan, Canadian Wood Fibre Center,
suzanne.wetzel@canada.ca
Prof. Sally Krigstin, UofT,
Department of Forestry
sally.krigstin@utoronto.ca
Janet Damianopoulos,
NRCan, Canadian Wood Fibre Center
janet.damianopoulos@mail.utoronto.ca
Wasim Faizal,
NRCan, Canadian Wood Fibre Center
wasim.mohamedfaizal@gmail.com
4. Why Do We Need It?
Build up of heat within a pile
Can lead to localized fires
Want to understand how much CO2 a pile releases
Want to know if better storage practices can
improve quality of feed
8. Current Practices
Turning the piles to dissipate heat at fixed periods
Random temperature measurements
Managing pile geometry
Compost Piles: controlling oxygen levels
10. Factors Influencing Heat build up
Biological - bacteria/living woody tissue
Chemical - oxidation reactions
Physical - evaporation / condensation of water
11. Can we predict temperature rise within a
pile?
Why?
To understand when a new pile might get too hot
To know when to release heat
How?
Requires knowledge of specific wood properties
Requires mathematical models
12. Application Heat and Mass Transfer
concepts
Mass Build up = Mass Flow in – Mass Flow out +/-
Reaction
Heat Build up = Heat Flow in – Heat Flow out +/-
Reaction
Dispersion of mass through diffusion (Fick’s Law)
Dispersion of heat through conduction (Fourier
Law of Heat Conduction)
14. Modeling Biological Growth
Biological growth = Bacteria in – Bacteria out + Rate of Growth
𝑟𝑥 = 𝜇 𝑚
𝑀𝐵
𝐾 𝑏+𝑀𝐵
𝑋 − 𝑏𝑋 (
𝑘𝑔
𝑚3)
𝜇 𝑚-growth factor
𝑏 − 𝑑𝑒𝑎𝑡ℎ 𝑓𝑎𝑐𝑡𝑜𝑟
𝑀𝐵 − 𝑤𝑜𝑜𝑑𝑦 𝑏𝑖𝑜𝑚𝑎𝑠𝑠 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 (sugars)
𝐾𝑏 − 𝑏𝑖𝑜𝑚𝑎𝑠𝑠 𝑠𝑎𝑡𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡
𝑋-bacterial concentration
Equation Reference: F. Ferrero et al. Journal of Loss Prevention in the Process Industries 22 (2009) 439-448
15. Modeling Oxygen Consumption
𝐶6 𝐻12 𝑂6 + 6𝑂2 → 6𝐶𝑂2 + 6𝐻2 𝑂 −− −Δ𝐻𝑐.𝑟.
Heat released per mole of oxygen consumed
𝑞 𝑂2
= (Δ𝐻𝑐.𝑟./6)(1-efficiency)
Equation Reference: F. Ferrero et al. Journal of Loss Prevention in the Process Industries 22 (2009) 439-448
16. Modeling Oxygen Consumption (cont.)
−𝑟𝑂2
=
1−𝑌
𝑌
𝜇 𝑚
𝑀𝐵
𝐾 𝑏+𝑀𝐵
𝑋 − 𝑏(1 − 𝑓)𝑋
Oxygen consumption is used to predict heat
released by bacteria.
𝑄 = (𝑟𝑂2
)𝑞 𝑂2
(
𝑊
𝑚3 )
𝑞 𝑂2
- oxycalorific coefficient (heat released per
molecule of oxygen consumed)
Equation Reference: F. Ferrero et al. Journal of Loss Prevention in the Process Industries 22 (2009) 439-448
18. Modeling Other Heat Sources
Model the decomposition of wood as a first order
chemical reaction
Use the rate of decomposition with the enthalpy of
decomposition to determine heat released
23. Model Improvement
Add growth limiting factors for bacteria
Moisture content
Temperature limits
Oxygen content
24. Collaboration to Monitor Data
Data Expertise:
NRCan
UofT
Equipment Expertise:
Braingrid
25. Monitoring Temperatures
Previous temperature monitoring failed
Temperature loggers caught fire
Braingrid provides a wireless sensor monitoring
tool
Monitor and log data to a remote server
Data is accessible from any location
26.
27. Sentroller
The Sentroller acts as a data hub.
It is capable of capturing
information from any sensor
Relays that information to a remote
location off-site
28.
29. Goals
Determine accuracy of the model (other data sets)
Work being conducted at PAMI to prepare new
biomass piles
Use model to determine best practices for various
biomass types
Develop a CSA standard for managing a pile
30. Summary
It is possible to model biomass conditions
Currently working on improving and verifying the
model
Enables us to determine practices to increase
efficiency and reduce cost
Control moisture content, effect on heating value
Conserve dry matter
To preserve or improve quality of fuel
Knowledge of techniques to influence properties , pile geometry, etc
Once the model has been tested against multiple data sets, it can be used to predict the temperature and CO2 releases for any biomass pile.
If we can predict the CO2 and temperature releases, we will know the long term effects different types of biomass has on CO2 levels and we will know when the pile needs to be “cooled”.
This allows us to develop a CSA standard, specifying the lengths of storage (to minimize CO2 release) and the time periods at which they need to be “cooled”.
Developing this standard will allow smaller companies to enter the bioenergy market, without the need for expensive equipment to constantly monitor their biomass storage.
Once the model has been tested against multiple data sets, it can be used to predict the temperature and CO2 releases for any biomass pile.
If we can predict the CO2 and temperature releases, we will know the long term effects different types of biomass has on CO2 levels and we will know when the pile needs to be “cooled”.
This allows us to develop a CSA standard, specifying the lengths of storage (to minimize CO2 release) and the time periods at which they need to be “cooled”.
Developing this standard will allow smaller companies to enter the bioenergy market, without the need for expensive equipment to constantly monitor their biomass storage.
