Neural networks applications in dehydration.pptx

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This document discusses using an artificial neural network (ANN) model to predict drying parameters for bitter leaf. It aims to develop a suitable ANN model to describe bitter leaf's moisture content removal during drying. The ANN will be trained to predict drying parameters and validated using experimental data. This could help understand and better control the drying process. Materials and methods describe collecting and preparing bitter leaf samples, drying them via sun drying, and using MATLAB to design and test ANN models with moisture content and drying time as inputs and outputs.

Engineering

PREDICTING OF DRYING PARAMETERS FOR
BITTER LEAF USING ARTIFICIAL NEURAL
NETWORK (ANN)

 Background of the study
 Problem statement
 Aim and objectives
 Significance of the study
 Literature review
 Materials and methods

BACKGROUND OF THE STUDY
Definition of drying
Drying is defined as the process of moisture removal
due to simultaneous heat and mass transfer.
Drying of vegetables
Moisture is an integral part of all leafy vegetables. For
the purpose of preservations, it is necessary to reduce if
not completely eliminate the moisture content of these
vegetables.

Problem statement
A research study on the use of Artificial Neural Network for the
prediction of drying parameters for bitter leaf.
Aim and objectives:
 Development of a suitable ANN model describing moisture
content removal behaviour during drying of bitter leaf.
 Training of the Artificial Neural Network to predict drying
parameters for bitter leaf.
 Validation of the model using experimentally generated data.
 Comparison of the developed model with an existing model in
terms of moisture content removal efficiency.

SIGNIFICANCE OF STUDY
The description of the drying process using an ANN model is
going to help in the understanding of the drying operation. ANN
models will make it possible to predict the drying behaviours of
bitter leaf over a range of parameters and help to better control
the drying process.

LITERATURE REVIEW
Health benefits of bitter leaf
 Bitter leaf is rich in vitamin C which maintains bones and teeth
 Bitter leaf contains flavonoids which has powerful antioxidant
effects.
 It’s antifungal and antibacterial properties treats skin infections.
Previous works on drying using ANN
Author Work Area of study Findings
Tarafdar et al Predicted water activity of
button mushrooms during
freeze-drying using a one
hidden layer MLP ANN
model.
Prediction of drying
parameter using ANN
The developed model was
found to reduce energy
consumption of the
freeze-drying process.
Freire et al Estimation of the
interphase coupling term
of spouted bed drying of
three different pastes
using a hybrid lumped
element/ANN model.
Prediction of drying
parameters using ANN
The proposed hybrid
model accurately
computed the outlet
drying air temperature and
relative humidity as well
as the powder moisture
content.

MATERIALS AND METHODS
Materials:
The materials and equipment used for this work include:
• Bitter leaf
• Water
• Knife
• Tray
• Chopping board
• Weighing balance
• Plastic container

METHODS
Sample preparation
This was done by the following steps:
• Collection of fresh bitter leaf bunch
• Plucking of the leaves
• Washing
• Cutting
• Sun- drying
Drying process
Sun drying method will be used in this work. The leaf sample
will be weighed at daily intervals and dried between 8am to 5pm
daily for five days until no further changes in weight are
observed.

ANN MODELLING
MATLAB software is to be used for the design and testing of
various ANN models. The ANN configuration to be used in this
work is a multilayer “feed-forward,” consisting of one input
layer, one hidden layer, and one output layer.
Moisture content
Drying time

In this study, input-output data sets in the experiment will be collected
for moisture ratio, drying time and moisture content. For ANN model
building, the available data will be randomly divided into 3 subsets:
training (70%), validation (15%), and testing (15%) subsets. The
networks performance will be evaluated by correlation coefficient (𝑅2)
and mean square error (RMSE).

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