Adaptive neuro fuzzy inference system modeling of Synsepalum dulcificum L. drying characteristics and sensitivity analysis of the drying factors

Drying kinetics and thermo-environmental analysis of a PV-operated tracking indirect solar dryer for tomato slices

The purpose of this study is to investigate how a tracking indirect solar dryer (SD) powered by photovoltaic cells affected the drying kinetics (DK) and thermo-environmental conditions of tomato slices. In this current investigation, three air speeds (1, 1.5, and 2 m/s) are used, as well as three slice thicknesses (ST) (4, 6, and 8 mm) and two SD, one of which is integrated with fixed collector motion (FCM) and another with SD tracking collector motion (TCM). The obtained results showed that the drying time (DT) isn't significantly change with increasing air speeds from 1 to 2 m/s, this may be due to many reasons such as short DT, high temperature inside drying room, and little difference between the exanimated air speeds. When the ST is changed from 4 to 8 mm and maintaining constant air speeds, the DT for FCM and TCM rose by roughly 1.667 and 1.6 times, respectively. In addition, the drying coefficient of the TCM is higher than the FCM due to higher temperature. At 1.5 m/s air speed and 8 mm ST, the maximum values of moisture diffusivity (MD) are 7.15×10-10 and 9.30×10-10 m2/s for both FCM and TCM systems, respectively. During the study of DK, nine drying models and chose the best based on higher R2 and lower χ2 and RMSE are used. The findings of the DK analysis revealed that the modified two term II model fit the experimental data of various air speeds well when TF was dried using TCM and FCM systems at varying ST. These findings are based on recorded observations. Where the models' R2 values varied from 0.98005 to 0.99942 for FCM system and varied from 0.99386 to 0.99976 for TCM system. Regarding environmental analysis, it is found that the CO2 mitigation per lifetime is ranged between 5334.9-6795.4 tons for FCM and 6305.7-6323.3 tons for TCM.

Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

In this study, the focus was to produce xanthan gum from pineapple waste using Xanthomonas campestris. Six machine learning models were employed to optimize fermentation time and key metabolic stimulants (KH2PO4 and NH4NO3). The production of xanthan gum was optimized using two evolutionary optimization algorithms, particle swarm optimization, and genetic algorithm while the importance of input features was ranked using global sensitivity analysis. KH2PO4 was the most important input and was found to be beneficial for xanthan gum production, while a limited amount of nitrogen was needed. The extreme learning machine model was the most adequate for modeling xanthan gum production, predicting a maximum xanthan yield of 10.34 g/l (an 11.9 % increase over the control) at a fermentation time of 3 days, KH2PO4 of 15 g/l, and NH4NO3 of 2 g/l. This study has provided important insights into the intelligent modeling of a biostimulated process for valorizing pineapple waste.