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Diffusion modeling and optimization of drying dynamics of ogbono seed (Irvingea gabonensis): empirical insights into energy indices and process conditions

  • Nnaemeka R. Nwakuba ORCID logo EMAIL logo , Ikechukwu I. Iroegbu and Chioma Anunobi
Published/Copyright: March 10, 2025
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 20 Issue 3

Abstract

The drying of agro-seeds is essential for enhancing product quality and extending shelf-life. This paper explores the diffusion modeling and drying dynamics of ogbono seeds (Irvingia gabonensis), providing insights to improve drying processes and energy efficiency. The experiment employed Central Composite Design to assess the impacts of temperatures (40, 50, 60 °C) and seed sizes (small, medium, and large) at a constant air velocity of 1.5 m/s on specific energy utilization, energy efficiency, seed shrinkage, and drying duration. The kinetics of water desorption were examined using the superimposition technique under different conditions. The drying behaviour was best represented by the Avhad and Marchetti model (R 2 = 0.9933, ε r m s   = 0.0120, χ 2 = 0.0004), revealing that drying time dropped from 240 to 180 min as the temperature increased. Shrinkage tests indicated considerable morphological defects in larger seeds due to greater porosity, whilst smaller seeds demonstrated higher structural resilience. Effective moisture diffusivity dropped by 42.5 %, from 1.21 to 3.52 × 10−10 m2/s. The activation energy ranged from 55.91 to 80.68 kJ/mol. Specific energy consumption (1.05–1.60 kWh) and energy efficiency (60–85 %) exhibited a perfect balance between product quality and energy input. Under the optimal conditions of 60 °C and small seed size, the specific energy of 1.075 kWh, 82.52 % energy efficiency, 8.65 % seed shrinkage, and 46.25 % drying time reduced to 129 mins was achieved. These outcomes established a yardstick for sustainable and energy-efficient ogbono seed drying. The study provides potential pathways to work in sensor-based hybrid power crop dryers and their applications to other commercially valuable seeds.

Keywords: agro-seeds; energy; moisture desorption; numerical optimization; shrinkage; sustainability
Corresponding author: Nnaemeka R. Nwakuba, Department of Agricultural Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Owerri, Nigeria; and Departamento de Engenharia Agrícola, Universidade Federal de Viçosa, MG, Brasil, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

Appendix: General statistical analysis of the drying models for ogbono seeds at varying temperatures and seed sizes.

Model No. Seed size (g) Air temp (°C) Model constants R 2 ε r m s χ 2
k n a b g k 1 k 2
1 S 40 0.3252 0.211 0.9591 0.0313 0.0027
2 0.2324 0.9234 0.0541 0.0071
3 0.4729 1.02 0.11 0.21 0.9881 0.0334 0.0008
4 0.5404 1.064 0.9739 0.0500 0.0059
5 0.190 0.049 0.062 0.9169 0.0570 0.0053
1 M 40 0.1469 1.011 0.9813 0.0430 0.0017
2 0.1372 0.9788 0.0635 0.0031
3 0.1738 1.031 0.124 0.183 0.9879 0.1129 0.0007
4 0.2377 1.11 0.9555 0.0357 0.0042
5 0.314 0.0922 0.123 0.9361 0.0410 0.0055
1 L 40 0.2141 1.307 0.9882 0.0260 0.0017
2 0.1425 0.9700 0.0490 0.0188
3 0.2107 0.1135 0.1311 0.1522 0.9933 0.0120 0.0004
4 0.1337 1.01 0.9899 0.0131 0.0027
5 0.3381 0.0432 0.038 0.9614 0.0599 0.0062
1 S 40 0.2127 0.931 1.017 0.9846 0.0390 0.0039
2 0.1251 1.02 0.9829 0.0528 0.0036
3 0.2131 0.4192 0.211 0.198 0.8949 0.0365 0.0005
4 0.1322 0.8221 0.9829 0.0412 0.0011
5 0.311 0.281 0.9875 0.0397 0.0031
1 M 50 0.1257 1.001 0.9764 0.0442 0.0019
2 0.2027 0.9764 0.0176 0.0036
3 0.1412 1.00 0.19 0.88 0.9899 0.0316 0.0009
4 0.1305 1.12 0.9764 0.0442 0.0075
5 0.294 0.126 0.132 0.9734 0.0470 0.0061
1 L 50 0.1347 1.232 0.9852 0.0372 0.0053
2 0.2116 0.9721 0.0481 0.0029
3 0.1275 0.931 0.152 0.19 0.9106 0.1467 0.0073
4 0.1157 1.00 0.9721 0.0481 0.0041
5 0.991 0.024 0.2204 0.216 0.9720 0.0510 0.0012
1 S 60 0.1399 0.9813 0.0430 0.0057
2 0.2106 0.9786 0.0435 0.0047
3 0.2214 1.023 0.201 0.188 0.9654 0.1350 0.0006
4 0.2141 0.014 0.9813 0.0430 0.0072
5 1.03 0.224 0.216 0.9921 0.0300 0.0018
1 M 60 0.1565 0.1153 0.9558 0.0642 0.0011
2 0.1372 0.9488 0.0650 0.0025
3 0.1134 1.031 0.91 0.17 0.9872 0.0700 0.0056
4 0.1322 0.209 0.9488 0.0690 0.0072
5 0.340 0.015 0.018 0.9614 0.0599 0.0012
1 B 60 0.1302 1.012 0.9519 0.0670 0.0017
2 0.2113 0.9519 0.0628 0.0038
3 0.1622 1.03 0.13 0.22 0.9701 0.0649 0.0007
4 0.122 0.186 0.0127 0.1604 0.9791 0.0497 0.0025
5 0.3102 0.0221 0.027 0.9643 0.0574 0.0061

  1. The statistical values in “bold” are significant at P < 0.05. 1. Page model, 2. Lewis model, 3. Avhad & Marchetti model, 4. Henderson & Pabis model, 5. The two-term exponential models, S, M, and L, denote small, medium, and large seed samples.

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Received: 2025-01-08
Accepted: 2025-02-22
Published Online: 2025-03-10

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
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  4. Numerical investigation of inlet pressure effects on condensation flow regime in a supersonic nozzle
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https://doi.org/10.1515/cppm-2025-0003
Keywords for this article
agro-seeds; energy; moisture desorption; numerical optimization; shrinkage; sustainability

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. An improvement of level control of non-linear horizontal tank process using sliding mode controller
  4. Numerical investigation of inlet pressure effects on condensation flow regime in a supersonic nozzle
  5. Effects of tapered helical obstacles on heat transfer in tubes
  6. Gasification process prediction using a novel and reliable metaheuristic algorithm coupled with the K-nearest neighbors
  7. Evaluating the ionic liquids, commercial solvents, and pressure-swing for efficient azeotropic separation
  8. A synergistic approach to CO2 sequestration: evaluating trapping mechanisms in saline aquifers
  9. Diffusion modeling and optimization of drying dynamics of ogbono seed (Irvingea gabonensis): empirical insights into energy indices and process conditions
  10. Production of polyphenol extracts with antioxidant activity from olive pomace: process modeling and optimization
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