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Modeling Biomass Pile Management
- 1. Modeling Biomass Piles for Pile Management Wasim Faizal, E.I.T., M.Eng
- 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
- 3. What is pile management? Nobody Really Knows
- 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
- 7. -30 C
- 8. Current Practices Turning the piles to dissipate heat at fixed periods Random temperature measurements Managing pile geometry Compost Piles: controlling oxygen levels
- 9. Why does a pile heat up?
- 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)
- 13. Reactor Analogy Mass in Mass out Heat in Heat out
- 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
- 17. Why use Oxygen consumption to estimate heat?
- 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
- 20. Temperature Modeling 𝜌𝐶 𝑝 𝑑𝑇 𝑑𝑡 = 𝐻𝑒𝑎𝑡 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑
- 21. Modeling Impossible to solve analytically Multi-dimensional problem Use COMSOL or MATLAB to determine a numerical solution set
- 22. Current Results
- 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
- 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
- 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
- 31. Questions?
Editor's Notes
- 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.