Parameter study on low temperature vacuum drying with induced nucleation boiling for dewatering stingless bees honey

One of the issues faced by stingless bees beekeepers is related to the storing of Stingless Bees Honey (SBH) at room temperature. This is because SBH contains high water content which may cause fermentation. Hence, the dewatering process is necessary to reduce the water content and prevent fermentation. The conventional methods that are used to lower the water content of honey are not efficient and have the potential to degrade the nutritious content of honey. Thus, the Low Temperature Vacuum Drying with Induced Nucleation Boiling (LTVD-NB) method has been developed to solve the problem of honey dewatering efficiency while maintaining honey quality. As LTVD-NB method involve with nucleate boiling, therefore surface roughness (SR) and dewatering temperature could affect the performance of dewatering honey. However, research on the effect of SR and dewatering temperature on nucleate boiling is limited. It is known that SR determines cavity and affects boiling heat transfer process, whereas temperature determines heat flux (q”). It is believed that boiling heat transfer process is directly related to bubble nucleate on heated surface. However, there is a limited amount of research available on the effect of SR and dewatering temperature towards the characteristics of bubble nucleation in honey. Thus, the objective of this study was to investigate the effects of temperature and SR of stainless steel heater pipes on dewatering rate, nucleate boiling heat transfer (NBHT), and bubble nucleate characteristic during dewatering of SBH. 200 g of SBH sample was heated for five minutes at three different temperatures, which were 40, 45, and 50 °C using stainless steel heater pipes with SR of 0.80, 3.39, 8.82, and 11.33 μm at the pressure of 5 kPa. A digital camera was used to capture and record the formation of bubble nucleate on the heater surface during the experiment. The formation of bubble characteristic has been observed and analysed base on the number of bubble nucleated and the bubble departure frequency. Each experimental setting was performed three times. It was found that SR and temperature significantly affected the dewatering rate of SBH. The highest dewatering rate of 0.42 %/min was obtained at the roughest surface of 11.33 μm and highest temperature of 50 °C. It was five times faster compared to dewatering rate at 0.80 μm SR of 40 °C, which was only 0.08 %/min. The highest dewatering rate was obtained at the highest SR due to more nucleation sites, thus increased the number of bubbles depart. By roughening the heater surface, the heat transfer coefficient (HTC) was enhanced as more cavity and nucleation site were present on the surface. The 11.33 μm surface produced 143 % higher HTC than the 0.80 μm. In addition, the highest dewatering rate was obtained at higher temperature as the heat dispersion and transfer were better at higher temperature compared with lower temperature. This is because as temperature increased q”, bubble frequency departure from heater surface and HTC also increased. The maximum HTC obtained was around 10.11 kW/m2K, corresponding to the highest temperature tested at 50 °C. Thus, higher SR and temperature resulted in higher dewatering rate, and this correlated with the increment of nucleation site, bubble frequency depart from heater surface, q”, and HTC

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

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