Torrefaction performance prediction approached by torrefaction severity factor

Wei Hsin Chen, Ching Lin Cheng, Pau Loke Show, Hwai Chyuan Ong

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


Torrefaction is a crucial biomass upgrading technology to produce biochar for fuel, soil amendment, and bio-absorbent. A number of indicators such as weight loss (WL), torrefaction severity index (TSI), and severity factor (SF) have been conducted to describe the torrefaction degree. However, operating conditions such as torrefaction temperature and duration are not considered in weight loss and torrefaction severity index, while biomass nature is not taken into account in the severity factor. To overcome these drawbacks, an indicator termed torrefaction severity factor (TSF) is proposed by introducing a time exponent in the severity factor. Four different biomass materials of Chinese medicine residue, Arthrospira platensis residue, C. sp. JSC4, and spent coffee grounds are examined. After the optimization of the time exponent, TSF can accurately correlate weight loss and thereby torrefaction severity, and improve the prediction up to 13% when compared to severity factor. In addition, the results suggest that TSF is able to appropriately predict the enhancement factor of HHV and energy yield where the coefficient of determination (R2) is beyond 0.83. Overall, TSF has successfully combined the operating conditions (temperature and duration) and biomass species, can be utilized for predicting torrefaction performance. This gives a simple and fast way for torrefaction operation and reactor design, thereby achieving time-saving and efficient predictions.

Original languageBritish English
Pages (from-to)126-135
Number of pages10
StatePublished - 1 Sep 2019


  • Biochar
  • Linear regression
  • Torrefaction
  • Torrefaction severity factor (TSF)
  • Torrefaction severity index (TSI)
  • Weight loss


Dive into the research topics of 'Torrefaction performance prediction approached by torrefaction severity factor'. Together they form a unique fingerprint.

Cite this