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Modeling the kinetics, energy consumption and shrinkage of avocado pear pulp during drying in a microwave assisted dryer

  • James Chinaka Ehiem , Okechukwu Oduma , Austin O. Igbozulike , Vijayan G. S. Raghavan and Ndubisi A. Aviara EMAIL logo
Published/Copyright: November 25, 2024
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Chemical Product and Process Modeling
From the journal Chemical Product and Process Modeling Volume 19 Issue 6

Abstract

Drying kinetics, energy utilization (EU) and shrinkage level of avocado pear pulp during drying were investigated and modeled to determine the condition that enhances the quality of the dried product. Drying was carried out using a microwave assisted dryer with data lodger. The system was set at a constant power of 200 W, air velocity of 1.4 m/s, and temperatures of 50, 60 and 70 °C with pulp thickness being 5 mm. Fifteen thin-layer drying models, five non-linear shrinkage models and ANN methods were tested for describing the drying behaviour of avocado pulp using statistical parameters. The results revealed that drying took place in the falling rate period with the above temperatures reducing the moisture content of the pulp from 64.12 to 2.16 % wet basis within 15,360, 11,520 and 5,130 s, respectively. The drying rate and effective diffusivity increased with increase in temperature and ranged from 6.05 × 10−3 to 1.70 × 10−2 kg/kgs and 3.11 to 9.34 × 10−9 m2/s, respectively. The activation energy of the pulp was 50.34 kJ/mol. Among the drying models tested, Page and Aghashilo models provided the best statistical parameters for describing the drying behaviour of the pulp, while ANN demonstrated great ability to predict MR and SR more accurately with high and low R2 and RMSE. A non-linear shrinkage model developed also had the best fit qualities for describing the shrinkage behaviour of the pulp. The energy utilized (EU), specific energy utilized (S EU ), heat transfer coefficient (h tc ) and mass transfer coefficient (M tc ) of the pulp ranged from 7.36 to 3.19 kWh, 11.21 to 5.76 × 10−2 Wh/kg, 0.1054 to 7.98 × 10−7 W/mK and 2.06 to 4.28 × 10−6 m/s respectively and were statistically (5 %) influenced by temperature. The EU model developed had the best description behaviour of the energy relationship with other factors, having high R2 and low RMSE and SSE values.

Keywords: ANN; coefficient; diffusivity; heat transfer; moisture transfer; moisture ratio
Corresponding author: Ndubisi A. Aviara, Department of Agricultural and Bio-Resources Engineering, Michael Okpara University of Agriculture, Umudike, P.M.B. 7267, Umuahia, Abia State, Nigeria, E-mail:

Funding source: None declared

  1. Research ethics: Not applicable

  2. Informed consent: Not applicable

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Ehiem, James C.: conceived and designed the analysis and wrote the paper. Oduma, Okechukwu: collected data and perform analysis. Igbozulike, Austin O.: collected data and perform analysis. Raghavan, Vijayan G. S.: Provided analysis tools and designed analysis. Aviara, Ndubisi A.: designed and perform analysis.

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

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

  6. Research funding: None declared.

  7. Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Nomenclatures

Mx

Moisture content at a given time

Mi

Initial moisture content

Me

Equilibrium moisture content

MR

Moisture ratio

Deff

Effective diffusivity

Z

Thickness

S

Slope

A

Constant

Ea

Activation energy

Do

Arrhenius constant

R

Universal gas constant

T

Absolute temperature

Ʀ t

Dry rate

w t + 1

Moisture content at the time t + 1

w t

Moisture content at time t (kg)

SEU

Specific energy utilized

Mwr

Mass of moisture removed

S MRR

Specific moisture removal rate

htc

Heat transfer coefficient

mtc

Moisture transfer coefficient

ρ

Density

PMW

Microwave power

Cp

Specific heat capacity

G Lewis

Lewis dimensionless number

Sc

Schmidt number

Pr

Prandtl number

μ

Dynamic viscosity

Re

Renold number.

Vt

Volume of the product at the time (t)

Ve

Volume of the product at constant mass

Vo

Initial volume of the product

n

Number of product piece in the drying tray

Vdry

Volume of dry matter

VH2O

Volume of water in the product

mH2O

Mass of water removed (kg)

ρ H 2 O

Density of water removed

SR

Shrinkage ratio

a, b, c, d, e, f, g

Constants

ANN

Artificial neural network

RMSE

Root mean square error

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Received: 2024-07-08
Accepted: 2024-10-12
Published Online: 2024-11-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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  2. Research Articles
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https://doi.org/10.1515/cppm-2024-0062
Keywords for this article
ANN; coefficient; diffusivity; heat transfer; moisture transfer; moisture ratio

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Comparative study of deterministic and stochastic optimization algorithms applied to the absorption of CO2 by alkanolamine solution
  4. Modeling the kinetics, energy consumption and shrinkage of avocado pear pulp during drying in a microwave assisted dryer
  5. Study of municipal solid waste treatment using plasma gasification by application of Aspen Plus
  6. Numerical analysis of segregation of microcrystalline cellulose powders from a flat bottom silo with various orifice positions
  7. Prediction of syngas production in the gasification process of biomass employing adaptive neuro-fuzzy inference system along with meta-heuristic algorithms
  8. Industrial high saline water desalination by activated carbon in a packed column- an experimental and CFD study
  9. Dual-loop PID control strategy for ramp tracking and ramp disturbance handling for unstable CSTRs
  10. A control perspective on hybrid membrane/distillation propane/propylene separation process
  11. Prediction of surface heating effect on non-equilibrium homogeneous condensation in supersonic nozzle using CFD method
  12. Modeling the emitted carbon dioxide and monoxide gases in the gasification process using optimized hybrid machine learning models
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