Mathematical modelling of stingless bee honey dewatering using lowtemperature vacuum drying with induced nucleation bubbling

• Main Authors: Ahmad Syazwan, Ramli, Nur Faeeza, Abdul Halim, Luqman, Abdul Halim, Firdaus, Basrawi, Mohd Azwan, Bakri
• Format: Conference or Workshop Item
• Language: English
• Published: Institute of Physics 2024
• Subjects: T Technology (General); TA Engineering (General). Civil engineering (General); TJ Mechanical engineering and machinery; TL Motor vehicles. Aeronautics. Astronautics
• Online Access: http://umpir.ump.edu.my/id/eprint/41241/1/Mathematical%20modelling%20of%20stingless%20bee%20honey%20dewatering%20using%20low-temperature%20vacuum.pdf; http://umpir.ump.edu.my/id/eprint/41241/; https://doi.org/10.1088/1742-6596/2688/1/012011

Low-temperature vacuum drying with induced nucleation boiling (LTVD-NB) was developed to dewater heat-sensitive materials such as stingless bee honey (SBH). However, its performance can be further optimised to achieve an efficient LTVD-NB operation. The objective of this paper is to investigate the most fitting drying model for dewatering SBH and to develop a suitable mathematical drying model that can be used to predict and optimise dewatering SBH using LTVD-NB. Established experimental data was used to develop the mathematical model. The data result showed that the logarithmic model had the best fit for drying SBH using LTVD-NB as compared to other models based on the highest value of R 2 and the lowest Root mean square, RMSE and reduced chi-square, χ 2 values which are 0.999988, 7.87E-05, and 1.41E-08, respectively. The model was further regressed to obtain an optimised mathematical model to better predict an LTVD-NB operation to dewater SBH. In conclusion, an optimised drying model to describe the dewatering process of SBH using the LTVD-NB method was able to be developed based on the multiple regression analysis of the obtained experimental data. Therefore, the drying model can predict the efficiency of this process just by giving the temperature and surface roughness values as input information.