Modeling of Shrimp Chitosan Polymer Adsorption Usi

Journal of Earth Energy Science, Engineering, and Technology, Vol. 7, No. 2, 2024
URL: https://e-journal.trisakti.ac.id/index.php/jeeset, DOI: https://dx.doi.org/10.25105/jvk2gg02
Received: 12 Agustus 2024, Accepted: 5 October 2024, Published: 7 October 2024
eISSN: 2614-0268 37 pISSN: 2615-3653
Modeling of Shrimp Chitosan Polymer Adsorption Using Artificial Neural
Network
(Pemodelan Adsorpsi Polimer Kitosan Udang Menggunakan Jaringan Syaraf
Tiruan)
Muhammad Taufiq Fathaddin1*, Dwi Atty Mardiana1, Andrian Sutiadi1, Fajri Maulida1, Baiq Maulinda Ulfah2
1Department of Petroleum Engineering, Universitas Trisakti, Jakarta, Indonesia
2Department of Petroleum Engineering, STT Migas, Balikpapan, Indonesia
Abstract
One phenomenon that can occur when a polymer solution is injected into an oil reservoir is adsorption. Adsorption occurs
due to interactions between polymer molecules and the reservoir pore surface. Adsorption causes some polymer molecules to
be removed from solution. So, this process results in a reduction in the polymer concentration in the solution. In this study, an
artificial neural network (ANN) model is used to estimate the adsorption of shrimp chitosan polymer on the surface of 40 mesh
and 60 mesh sand grains. The ANN model can estimate adsorption more accurately than previous models. This is because
previous models only predicted certain adsorption patterns, while the ANN model is able to predict adsorption with complex
relationships. The comparison of the mean absolute relative errors (MAREs) of the ANN, Langmuir, Freundlich, Henry, and
Harkins-Jura models is 5.7%, 15.9%, 14.6%, 15.2%, and 14.5%, respectively.
Keywords: Artificial Neural Network; Adsorption; Chitosan; Polymer; Shrimp
Sari
Salah satu fenomena yang dapat terjadi ketika larutan polimer diinjeksikan ke dalam reservoir minyak adalah adsorpsi.
Adsorpsi terjadi karena adanya interaksi antara molekul polimer dengan permukaan pori reservoir. Adsorpsi menyebabkan
sebagian molekul polimer terlepas dari larutan. Jadi, proses ini mengakibatkan berkurangnya konsentrasi polimer dalam
larutan. Dalam penelitian ini, model jaringan syaraf tiruan (JST) digunakan untuk menaksir adsorpsi polimer kitosan udang
pada permukaan butiran pasir 40 mesh dan 60 mesh. Model JST dapat menaksir adsorpsi lebih akurat dibandingkan model-
model sebelumnya. Hal ini dikarenakan model-model sebelumnya hanya memprediksi pola-pola adsorpsi tertentu, sedangkan
model JST mampu memprediksi adsorpsi dengan hubungan yang kompleks. Perbandingan mean absolute relative error
(MAREs) model JST, Langmuir, Freundlich, Henry, dan Harkins-Jura berturut-turut adalah 5,7%, 15,9%, 14,6%, 15,2%, dan
14,5%.
Kata-kata kunci: Jaringan Syaraf Tiruan, Adsorpsi, Kitosan, Polimer, Udang
*Corresponding author
E-mail: [email protected]
I. INTRODUCTION
Polymer injection is one of the Enhanced Oil
Recovery (EOR) methods that can increase oil
production by around 5% -30% [1]. The function of
the polymer is to increase the viscosity of the injection
water which causes a decrease in its mobility [2].
Changes in the properties of the injection fluid cause
a reduction in the fingering phenomenon where the
injection fluid bypasses the displaced oil, so that the
efficiency of oil sweeping can be increased [3].
Polymers used in polymer flooding are generally
divided into two main groups, namely natural
polymers and synthetic polymers. Natural polymers
that have been used include xanthan gum and guar
gum. While synthetic polymers that have been used
include polyacrylamide (PAM) and Polyethylene
oxide. These polymers are types of polysaccharides
and polyacrylamide polymers [4]. Since they do not
require chemicals and are readily available,
renewable, biodegradable, and environmentally kind,
natural polymers are a more appealing option for use
than synthetic ones.
The use of biopolymers to increase oil yields in
laboratory research continues to grow. Some of the
polymers that have been used include porang, green
grass jelly, black grass jelly, seaweed, crab shells and
shrimp [5,6]. The use of shrimp and crab shells as
biopolymers is one step to reduce the waste of shrimp
and crab shells which are very large in number [7].
The use of chitosan biopolymers made from shrimp as
injection materials has begun to be developed [8].
Chitosan polymers produced from the chitin
deacetylation process are a type of polysaccharide
polymer [9].
One common phenomenon that occurs when
polymers are injected into oil reservoirs is adsorption.
This event has an impact on various fluid and rock
properties. The challenge of polymer injection
applications is to limit this adsorption. According to
Manichand and Seright (2014), polymer adsorption is

Journal of Earth Energy Science, Engineering, and Technology, Vol. 7, No. 2, 2024
URL: https://e-journal.trisakti.ac.id/index.php/jeeset, DOI: https://dx.doi.org/10.25105/jvk2gg02
Received: 12 Agustus 2024, Accepted: 5 October 2024, Published: 7 October 2024
eISSN: 2614-0268 38 pISSN: 2615-3653
the process of polymer molecules attaching to the rock
surface [10]. Adsorption can result in reduced rock
pore space. According to Dong et al. (2006) and
Tobing and Eni (2013), this indicates a decrease in
rock permeability which results in low fluid flow rates
[11,12]. In addition, the presence of several polymers
adsorbed on the rock surface causes a decrease in the
polymer concentration in the solution. A decrease in
polymer concentration will reduce the viscosity of the
injection fluid and reduce the efficiency of oil
sweeping [13-15].
Adsorption is the surface reaction that occurs in
the liquid or gas phase between a contaminant
(adsorbate) and a solid (adsorbent). At the interfaces
between solid–gas, solid–liquid, or gas–liquid,
adsorption can take place. If there is more adhesion
force between two molecules than there is cohesion
force, adsorption may occur [16]. The two most well-
known adsorption types are chemical and physical
adsorption [17,18]. The process of chemical
adsorption arises from the creation of chemical bonds
between the adsorbate and the adsorbent surface. Van
der Waals forces are what cause physical adsorption
to occur in the interim [13,18,19]. The kind and
concentration of the adsorbate dissolved in the liquid
greatly influences the adsorption effect. Adsorption
models are derived from traditional adsorption
isotherm equations and are considered to be less
trustworthy empirical approaches, especially when
simulating multicomponent adsorption systems with
several adsorbates. consequently, there is a need to
create and enhance the modeling tools available for
adsorption analysis [20].
The Langmuir isotherm equation is suggested for
mono-layer adsorption, [13,21-23], while the Harkins
Jura and Freundlich isotherm equations are suggested
for multi-layer adsorption [24,25]. Furthermore, the
heterogeneous nature of adsorption sites is assumed
by the Freundlich adsorption isotherm [26].
Concurrently, the Henry isotherm equation is put out
to simulate adsorption at comparatively low
concentrations of the adsorbate [27-28]. Predicting the
type and surface area of the adsorbent as well as the
rate of adsorption at different adsorbate
concentrations is significantly affected by the limits of
the conventional adsorption isotherm equations.
An intelligent model that is used to improve
adsorption process simulation is one that is powered
by artificial intelligence. The usefulness of these
models in enhancing correlations and assessments of
the adsorption of both organic and inorganic
adsorbates in single-component and multi-component
solutions has been recognized by many authors [29-
32]. In order to solve problems associated with
simulating adsorption systems, especially those
containing fluids with multiple adsorbates under
different operating conditions, artificial neural
networks (ANN) have been proposed as a dependable
and effective solution [29,33-37].
In this study, an artificial neural network model
will be used to get around this. It is anticipated that the
neural network model will be able to estimate
adsorption more accurately than the traditional
approaches due to its capacity to produce complicated
correlations between important factors [38-40].
II. METHODOLOGY
In this study, the concentration of shrimp chitosan
in the solution used with three variations, namely
5,000, 1,000, and 15,000 mg/L. While the salinity of
the formation water was determined with two
variations, namely 10,000 and 20,000 mg/L. The
study was conducted with the types of salt, namely
NaCl and MgCl2. The UV-visible spectrophotometer
apparatus was applied to measure the absorbance of
polymer solutions onto sand grains as an adsorbent.
The adsorption test was carried out by setting a
wavelength of 600 nm to obtain an accurate
absorbance value and determine the amount of
polymer absorbed by the rock surface (core). In the
test, the fluid was left to stand after being stirred
evenly. The steps to be taken are as follows. 10 grams
of sand grains that have been filtered with 40 mesh or
60 mesh. Then the sand is put into a schott duran
bottle, 10 grams each for each fluid sample with
variations in concentration and salinity that have been
determined. Prepare 15 ml of polymer solution each
for different salinity and concentration in a chemical
measuring cup then pour it into a schott duran bottle
that has been filled with core samples and an empty
schott duran bottle then close it tightly. Shake the
schott duran bottle manually for 2 hours. Store the
schott duran bottle at room temperature for 5 days.
Insert the Cuvette containing the polymer solution
into the UV-Vis Spectrophotometer to obtain the
absorbance value. The absorbance parameter is used
to calculate adsorption (Ulfah, 2023).
The materials used in this research were Sapindus
rarak surfactant, brine with a salinity of 6000 ppm and
10000 ppm, light crude oil of 43 API, berea core.
Meanwhile, the equipment used included beakers,
chemical glasses, analytical balances, hot plate
magnetic stirrers, Schott Duran bottles, pipettes,
Erlenmeyer flasks, UV-Vis Spectrophotometers, core
flooding apparatus, and syringe pumps [41].
The parameters observed from the batch
adsorption experiments were salinity of brine, initial
and final concentrations of shrimp chitosan, size of
sand grains, and adsorption. The last parameter was
used as output, while other parameters were used as
input. The ANN modeling included 36 experimental
data sets including for training, validation and testing.
Meanwhile, the rest data sets were used for
comparison of ANN model correlations and previous
methods such as Langmuir, Harkins-Jura, Freundlich,
and Henry isotherm equations. Figure 1 shows the
general procedure of the research.
The accuracy of the model in estimating

Journal of Earth Energy Science, Engineering, and Technology, Vol. 7, No. 2, 2024
URL: https://e-journal.trisakti.ac.id/index.php/jeeset, DOI: https://dx.doi.org/10.25105/jvk2gg02
Received: 12 Agustus 2024, Accepted: 5 October 2024, Published: 7 October 2024
eISSN: 2614-0268 39 pISSN: 2615-3653
adsorption is assessed using the statistical parameters,
mean absolute relative error (MARE) and correlation
coefficient (R) [42].
Figure 1. Procedure of the Research
Mean absolute relative error is defined as
Pearson correlation coefficient is defined as
where xi, xi’, and  are measured data, predicted
data, and average value.
III. RESULTS AND DISCUSSION
Artificial neural network modelling was
performed by identifying four inputs that influenced
output. The inputs were sand grain size, initial and
final concentrations of chitosan polymer, and salinity
while the output was adsorption. Feed forward back
propagation network type was applied for the model
to learn and plot the relationships between inputs and
output. In addition, the network learning rule was used
to adjust a system's weight values and bias values. The
values were optimized using Levenberg-Marquardt
method to achieve the minimum error. The model
applied 16 neurons with one hidden layer. The tangent
sigmoid was used as a transfer function to calculate a
layer's output from its net inputs. Figure 2 shows
Schematic diagram of the model architecture of
artificial neural network.
Figure 2. The Artificial Neural Network Model Schematic
Diagram
Figure 3. Artificial neural network training result
Figures 3 to 5 shows the outcomes of the
validation, testing, and training procedures. The
values of correlation coefficient (r) for the training,
validation, and testing phases were 0.99733, 0.96760,
and 0.96964, respectively. The entire process had a
correlation coefficient of 0.98551. The model
indicates that correlation coefficient (r) values
approached one. This means that there is a good link
between the output (prediction) and the target (data).
0
5
10
15
20
25
30
010 20 30
Output = 0.97*Target + 0.28
Target
Training: R=0.99733
Data
Fit
Y = T
Preparation of polymer solutions with
various salinities and concentrations
Static adsorption test
Adsorption measurements using UV vis
spectrophotometer
Making ANN model
ANN model validation
Adsorption prediction using ANN model
and previous methods
Observation and Analysis
Initial Con-
centration
Final Con-
centration
Salinity
Sand grain
size
Adsorption

Journal of Earth Energy Science, Engineering, and Technology, Vol. 7, No. 2, 2024
URL: https://e-journal.trisakti.ac.id/index.php/jeeset, DOI: https://dx.doi.org/10.25105/jvk2gg02
Received: 12 Agustus 2024, Accepted: 5 October 2024, Published: 7 October 2024
eISSN: 2614-0268 40 pISSN: 2615-3653
Figure 4. Artificial neural network validation result Figure 5. Artificial neural network testing result
Table 1. Comparison of Adsorption Predictions
No.
1
2
3
4
5
6
Experiment, ppm
5.3
12.8
16.1
6.9
11.3
20.0
ANN, ppm
6.1
12.8
15.5
6.7
11.3
22.3
% ANN
16.0
0.3
3.4
2.8
0.3
11.7
Langmuir, ppm
5.5
10.0
20.4
6.8
13.5
15.9
% Langmuir
4.5
21.6
27.1
1.3
20.2
20.7
Freundlich, ppm
5.9
10.1
18.0
6.6
13.9
17.0
% Freundlich
12.6
20.6
11.8
4.4
23.3
15.2
Henry, ppm
5.8
9.8
17.1
5.9
14.3
18.2
% Henry
10.5
23.1
6.5
14.9
27.1
9.0
Harkins-Jura, ppm
5.4
7.2
19.7
6.8
11.8
17.4
%Harkins-Jura
2.5
43.2
22.7
0.7
5.1
13.0
The accuracy of artificial neural network model
was carried out by comparing adsorption estimates
using ANN and six experimental data as given in
Table 2. The table shows that the absolute relative
error (ARE) between data and ANN estimations
ranged between 0.3% to 16%. The table also indicates
adsorption predictions using adsorption isotherm
equations such as Henry, Langmuir, Harkins-Jura, and
Freundlich. The table shows the maximum absolute
relative error between predictions using the Langmuir,
Freundlich, Henry, and Harkins-Jura equations with
data are 27.1%, 23.3%, 27.1%, and 43.2%,
respectively. These results indicate that the deviation
from adsorption estimations using the ANN model is
lower than the predictions from conventional
adsorption equations. However, the accuracy of
adsorption estimates for the six cases fluctuated for all
models used in this research. Determining the average
accuracy of estimations for the six cases can be
denoted by the mean absolute relative error (MARE).
Based on Table 2, the MARE for the six cases given
the ANN, Langmuir, Freundlich, Henry, and Harkins-
Jura adsorption models with data is 5.7%, 15.9%,
14.6%, 15.2%, and 14.5%, respectively. This also
indicates that the ANN model is the most accurate
compared to other models.
IV. CONCLUSIONS
Based on the results and analysis mentioned
above, several statements are made as follows:
1. The ANN model was developed by including
initial and final concentrations, salinity, and sand
grain size as input parameters, while adsorption
was set as output parameter.
2. The ANN model can predict the adsorption of
shrimp chitosan polymer with an absolute relative
error varying between 0.3% to 16%.
3. The accuracy of the ANN model is higher
compared to previous models. This is indicated by
the MARE value of the ANN model which is
lower than the other models. The MARE values of
the ANN, Langmuir, Freundlich, Henry, and
0
5
10
15
20
25
30
010 20 30
Output = 1*Target + 0.71
Target
Validation: R=0.96760
Data
Fit
Y = T
0
5
10
15
20
25
30
010 20 30
Output = 0.91*Target + 0.27
Target
Testing: R=0.96964
Data
Fit
Y = T

Journal of Earth Energy Science, Engineering, and Technology, Vol. 7, No. 2, 2024
URL: https://e-journal.trisakti.ac.id/index.php/jeeset, DOI: https://dx.doi.org/10.25105/jvk2gg02
Received: 12 Agustus 2024, Accepted: 5 October 2024, Published: 7 October 2024
eISSN: 2614-0268 41 pISSN: 2615-3653
Harkins-Jura models are 5.7%, 15.9%, 14.6%,
15.2%, and 14.5%, respectively.
ACKNOWLEDGEMENT
This work was supported by Bima Research Grant
from the Ministry of Education, Culture, Research,
and Technology of the Republic of Indonesia (no.
832/LL3/AL.04/2024) and Research Grant no.
174/A/LPPM-P/USAKTI/VI/2024 and 175/A/LPPM-
P/USAKTI/VI/2024 from Universitas Trisakti.
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