## 3 Results and Discussion
### Approximation of Experimental Data  
The numerical analysis involved fitting moisture ratio curves with four thin-layer drying models. Table 3 shows the parameters determining the goodness of fit of each model. As can be seen, the best fit was obtained for the Midilli-Kucuk model (adj. R\({}^{2}\) of 0.999 and reduced chi-square of 10\({}^{-5}\)-10\({}^{-6}\)). The worst fit was observed for the simplest models, i.e., Newton and Page. The Henderson and Pabis model reproduced the drying curves better, thanks to the additional term a. A comparison of the experimental curves with those approximated by the Midilli-Kucuk model is shown in Figure 6. The model curves coincided with the experimental curves for the entire drying period, as supported by the significantly high coefficient of determination. Table 4 presents the Midilli-Kucuk model parameter values and the effective diffusion coefficient values for each process. The collected data aligns with the analysis of the kinetic data. The CVMW2 program exhibited the highest diffusion coefficient, along with the fastest average drying rate and shortest drying time. The coefficient had a minimum value during the convection (CV) process, indicating it to be slow and energy-intensive. The aid of ultrasound in CVUS marginally improved drying conditions, producing a relatively small (not statistically significant) surge in the effective diffusion coefficient. The CVMWUS \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Symbol**} & \multicolumn{2}{c}{**[PERSON]**} & \multicolumn{2}{c}{**Page**} & \multicolumn{2}{c}{**[PERSON]–Pabis**} & \multicolumn{2}{c}{**[PERSON]–[PERSON]**} \\ \cline{3-10}  & & **red x\({}^{2}\)** & **adj R\({}^{2}\)** & **red x\({}^{2}\)** & **adj R\({}^{2}\)** & **red x\({}^{2}\)** & **adj R\({}^{2}\)** & **red x\({}^{2}\)** & **adj R\({}^{2}\)** \\ \hline
1 & CV & 3.01\(\cdot\)10\({}^{-3}\) & 0.9689 & 1.50\(\cdot\)10\({}^{-4}\) & 0.9984 & 1.51\(\cdot\)10\({}^{-3}\) & 0.9844 & 3.09\(\cdot\)10\({}^{-5}\) & 0.9997 \\
2 & CVUS & 2.18\(\cdot\)10\({}^{-3}\) & 0.9775 & 2.81\(\cdot\)10\({}^{-4}\) & 0.9971 & 9.92\(\cdot\)10\({}^{-4}\) & 0.9898 & 4.26\(\cdot\)10\({}^{-5}\) & 0.9996 \\
3 & CVMW1 & 4.48\(\cdot\)10\({}^{-3}\) & 0.9582 & 4.48\(\cdot\)10\({}^{-3}\) & 0.9582 & 1.92\(\cdot\)10\({}^{-3}\) & 0.9821 & 3.62\(\cdot\)10\({}^{-5}\) & 0.9997 \\
4 & CVMW2 & 3.65\(\cdot\)10\({}^{-3}\) & 0.9679 & 3.76\(\cdot\)10\({}^{-4}\) & 0.9967 & 1.42\(\cdot\)10\({}^{-3}\) & 0.9876 & 6.82\(\cdot\)10\({}^{-6}\) & 0.9999 \\
5 & CVMWUS & 3.84\(\cdot\)10\({}^{-3}\) & 0.9647 & 2.98\(\cdot\)10\({}^{-4}\) & 0.9973 & 1.53\(\cdot\)10\({}^{-3}\) & 0.9859 & 1.54\(\cdot\)10\({}^{-5}\) & 0.9999 \\ \hline \hline \end{tabular} red x\({}^{2}\)—reduced chi squared, adj R\({}^{2}\)—adjusted R squared.  
\end{table} 
Table 3: Reduced Chi-square and adjusted R-squared were calculated for the models used in approximation. Figure 6: Evolution of experimental (Exp) and numerical (F) moisture ratio (MR) over time, according to the Midilli–Kucuk model. process, which utilized all drying techniques, displayed a higher diffusion coefficient than that of convection or ultrasonic-assisted convection, yet lower than that of the convection-microwave process executed at 250 W. A high positive correlation (r = 0.79) was observed between the value of the drying rate k and the diffusion coefficient. The effective diffusion coefficient during drying is influenced by both microwave and ultrasound. Microwave power significantly affects the total heating time and energy efficiency of the drying processing, and the effective moisture diffusivities follow an Arrhenius-type relationship with microwave power [33]. Ultrasonic power also has an impact on drying kinetics, with a significant linear correlation between the identified effective diffusivity and the applied ultrasonic power for various products [34].