Influence of product geometry and process variables on drying energy demand of vegetables: An experimental study

Abstract

The changing aspects of energy and its high demand during convective drying of food products prompted this present study, which aims at investigating the impact of product geometric shapes and process parameters on the energy demand of vegetables during convective drying. It considered three distinctive shapes (sphere, S g ; cylinder, C g ; and slab, R g ) of vegetables (cucumber, garden egg, and white carrot) at constant sample volume, dried in a laboratory convective dryer. The Box-Behnken Design tool was used to design the experiment and explored the effects of material geometries at varying drying conditions (air temperature: 50, 60, and 70 C; air velocity: 1.0, 1.5, and 2.0 ms −1 ) on the total and specific drying energy demand, drying efficiency, percent product shrinkage, and drying time of the fresh vegetables. Results obtained revealed that the spherical-shaped samples exhibited high moisture diffusion and gross reduction in drying time (120 min), thus greater potential for energy and drying system improvement. The values of the effective moisture diffusion, D e (0.72 × 10 −10 ≤D e ≤ 2.13 × 10 −9 m 2 s −1 ) increased with drying temperature, and the maximum D e -value was obtained for the S g -carrot sample. The percent product shrinkage ranged between 78.66-94.73% for the C g -garden egg and S g -cucumber samples, respectively. The specific energy demand of the fresh cucumber, eggplant, and white carrot samples varied significantly (p >.005) with sample geometry. The maximum specific energy demand (16.38 ± 0.41 MJ/kgH 2 O, respectively) was obtained for the cylindrical shaped samples, whereas the minimum specific energy demand of 9.06 ± 0.24 MJ/kgH 2 O was yielded by the spherical shaped samples. The mean energy and drying efficiencies of the system ranged from 3.25 to 12.26% and 7.22 to 28.24%, respectively; whilst drying time ranged between 470 and 840 min.At the optimum process variables of 69.85 C, 1.22 ms −1 , 0.999 geometric shape, and −0.9977 crop sample, the specific energy demand, drying time, drying efficiency, and percent shrinkage were found to be 14.21 ± 0.84 MJkg −1 , 820 ± 9.00 min, 22.76 ± 0.74%, and 92.104 ± 1.00%, respectively. Prospects for future application and recommendations for further studies were suggested.

Strength properties of the Bambara kernel (Vigna subterranean) as influenced by the moisture content and kernel size

“…The obtained results were tested for the degrees of variation (P < 0.01, P < 0.05) with the use of the SPSS 20.0 Statistical software ® . Comparison of the least significant differences (LSD) in the kernels' strength parameters and interaction effects were tested and the treatment means were separated using Duncan's new multiple range test at a 0.95 level of confidence (Nwakuba et al , 2021.…”

Drying kinetics and shrinkage rate of thin‐sliced pears in different drying stages

“…Image J software was used to extract shrinkage‐related information such as image surface. The percentage of shrinkage at each time of drying was calculated by the following equation (Nwakuba et al, 2021): where sh is the shrinkage, is surface area of the sample before drying, and is the instant sample area during drying at the time of t .…”

“…Image J software was used to extract shrinkage-related information such as image surface. The percentage of shrinkage at each time of drying was calculated by the following equation (Nwakuba et al, 2021):…”

A Systematic Review of Energy Demand, Technology, and Efficiency Nexus: Implications for Bangladeshi Food Processing Industry

“…South Asian countries like Bangladesh enjoy a strategic advantage in the food processing industry as it relies on its adequate agricultural inputs (World Bank, 2012). However, the perishability and seasonality feature of agricultural raw materials makes the activities of the agro-food processing industry challenging (Nwakuba, Ndukwe, & Paul, 2021). In any type of food processing industry, energy is generally needed for machinery, technological devices, and equipment handling operations such as freezing, drying, heating, refrigeration and cooling (Murali, Krishnan, Amulya, Alfiya, Delfiya, & Samuel, 2021).…”

“…Through efficiency measurement, the performance and quality of transforming input into output can be assessed (Lu, Chiu, Chiu, & Chang, 2022). Despite advancements in different areas, the food processing industry faces enormous hurdles of high energy consumption and low operational efficiency (Nwakuba et al, 2021). In this case, technological innovation is one of the leading measures to shrink energy consumption, confirming energy efficiency (Sohag, Begum, Abdullah & Jaafar, 2015).…”