Mathematical modelling of extraction of oil from Aquilaria malacenssis wood employing subcritical conditions - Zainal Abidin, Zurina; Awang Biak, Dayang Radiah; Yunus, Robiah; Samadi, Mahtab (Tamkang University, 2024)
Subcritical water extraction (SWE) is gaining popularity due to its ability to manipulate water properties at high pressure and temperature. Here, the essential oil from Aquilaria Malacenssis wood was recovered using the subcritical technique. The aim is to elucidate SWE mechanism or behaviour and mathematically define the process kinetics for future scaling up, designing unit operation and improving efficiency. The extraction processes were carried out at various subcritical conditions such as varying temperature and time. These concentration-time profile data were analyzed using four models namely partitioning coefficient, one-site desorption, two-site desorption and thermodynamic partition with external mass transfer. Concentration (yield)-time profile indicated that the subcritical water extraction occurred in two simultaneous processes of fast and slow desorption. The fast process persisted during the initial 10 minutes with large changes in the essential oil yield. From this onwards until the end of extraction time, a gradual slow increment was seen till a plateau was reached. Three models which are second order, two-site kinetic and partitioning coefficient with external mass transfer were found to give reasonable SSR and R2 values. However, the two-site kinetic/second order model emerged to best fit the experimental data with low SSR of less than 0.002 and high R2 of greater than 0.99. Using the two rate constants of k1 and k2 from the two-site kinetic model, activation energies were calculated and found to be 16.5 KJ/mol and 28 KJ/mol for fast desorption and slow diffusion respectively. In short, SWE has demonstrated good enhancement of the extraction yield and temperature being the critical parameter. These findings on kinetics and modelling can facilitate the reduction of energy and time for scaling up and optimization.