Details

Autor(en) / Beteiligte

• Lim, Chun Hsion
• Mohammed, Isah Yakub
• Abakr, Yousif Abdalla
• Kazi, Feroz Kabir
• Yusup, Suzana
• Lam, Hon Loong

Titel

Novel input-output prediction approach for biomass pyrolysis

Ist Teil von

• Journal of cleaner production, 2016-11, Vol.136, p.51-61

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2016

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Biomass pyrolysis to bio-oil is one of the promising sustainable fuels. In this work, relation between biomass feedstock element characteristic and pyrolysis process outputs was explored. The element characteristics considered in this study include moisture, ash, fix carbon, volatile matter, carbon, hydrogen, nitrogen, oxygen, and sulphur. A semi-batch fixed bed reactor was used for biomass pyrolysis with heating rate of 30 °C/min from room temperature to 600 °C and the reactor was held at 600 °C for 1 h before cooling down. Constant nitrogen flow rate of 5 L/min was provided for anaerobic condition. Rice husk, Sago biomass and Napier grass were used in the study to form different element characteristic of feedstock by altering mixing ratio. Comparison between each element characteristic to total produced bio-oil yield, aqueous phase bio-oil yield, organic phase bio-oil yield, higher heating value of organic phase bio-oil, and organic bio-oil compounds was conducted. The results demonstrate that process performance is associated with feedstock properties, which can be used as a platform to access the process feedstock element acceptance range to estimate the process outputs. Ultimately, this work evaluated the element acceptance range for proposed biomass pyrolysis technology to integrate alternative biomass species feedstock based on element characteristic to enhance the flexibility of feedstock selection. •To study impact of feedstock element characteristics to pyrolysis process outputs.•Construction of element acceptance range for fixed bed pyrolysis.•Integrate alternative biomass as feedstock without major process modification.•Estimation of process outputs in the event of feedstock properties uncertainty.