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All Journal JOURNAL OF EARTH ENERGY ENGINEERING Jurnal Offshore: Oil, Production Facilities, and Renewable Energy Journal of Earth Energy Science, Engineering, and Technology
Sutiadi, Andrian
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Earth & Planetary Sciences Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Modeling of Shrimp Chitosan Polymer Adsorption Using Artificial Neural Network Fathaddin, Muhammad Taufiq; Mardiana, Dwi Atty; Sutiadi, Andrian; Maulida, Fajri; Ulfah, Baiq Maulinda
Journal of Earth Energy Science, Engineering, and Technology Vol. 7 No. 2 (2024): JEESET VOL. 7 NO. 2 2024
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/jeeset.v7i2.21134

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.
Adsorption Modeling of Amorphophallus oncophyllus Prain Using Artificial Neural Network Sutiadi, Andrian; Mardiana, Dwi Atty; Fathaddin, Muhammad Taufiq
Journal of Earth Energy Science, Engineering, and Technology Vol. 7 No. 3 (2024): JEESET VOL. 7 NO. 3 2024
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/qagty424

Abstract

Adsorption is the process of interaction between a liquid and a solid surface. It happens because of physical forces or chemical bonds, which moves substance molecules dissolved in a liquid to the solid surface. As a result, the concentration of the substance in the solution drops. In this study, an artificial neural network (ANN) was applied to model the adsorption of Amorphophallus oncophyllus Prain and xanthan gum on sand grains with sizes of 40 mesh and 60 mesh. Two ANN models were developed. The first ANN model was used to predict the final concentration of the polymer solution after the adsorption process. This model had a correlation coefficient for the training, validation, and testing phases of 0.9968, 0.9982, and 0.9990, respectively. Meanwhile the second ANN model was used to predict the adsorbed polymer. This model had a correlation coefficient for the training, validation, and testing phases of 0.9984, 0.9996, and 0.9985, respectively. These models were capable of accurately predicting the final concentration and adsorbed polymer when compared to laboratory data.
Characterization of Porang and Xanthan Gum Solutions for Polymer Flooding Sutiadi, Andrian; Siahaya, Jacob; Maulida, Fajri; Mardiana, Dwi Atty; Fathaddin, Muhammad Taufiq; Setiati, Rini; Rakhmanto, Pri Agung; Irawan, Sonny
Journal of Earth Energy Engineering Vol. 13 No. 2 (2024)
Publisher : Universitas Islam Riau (UIR) Press

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Abstract

Porang tubers contain glucomannan which is used in various industries. Porang is a biopolymer that has the potential to be applied in polymer flooding in oil reservoirs. In this research, a combination of porang and Xanthan gum was used for displacing oil in the laboratory. The samples analyzed varied with polymer concentrations of 2000, 4000, and 6000 ppm respectively for porang solution, Xanthan gum, and a mixture of porang and Xanthan gum. The salinity of the formation water used in this research was 6000, 12000 and 18000 ppm. The experiment aimed to observe the characteristics and performance of porang and Xanthan gum including testing for compatibility, viscosity, adsorption and sandpack flooding. Based on the test results, all samples were compatible. The application of a mixture of porang and Xanthan gum provided lower adsorption compared to the application of only Xanthan gum. The highest reduction in adsorption value given was 3.545 mg/gr. The highest viscosity and additional recovery factor were given by a mixture of porang and Xanthan gum with a concentration of 6,000 ppm and a salinity of 18,000 ppm, namely 284.72 cP and 16.1%, respectively.
Pemodelan dan Prediksi Densitas Larutan Porang dan Xanthan Gum dengan Menggunakan Model-Model Machine Learning Sutiadi, Andrian; Dardjat, Izumi Wicaksono; Arkaan, Muhammad Dzaki; Mardiana, Dwi Atty; Fathaddin, Muhammad Taufiq; Rakhmanto, Pri Agung; Pramadika, Havidh; Ristawati, Arinda
Jurnal Offshore: Oil, Production Facilities and Renewable Energy Vol. 8 No. 2 (2024): Jurnal Offshore: Oil, Production Facilities and Renewable Energy
Publisher : Proklamasi 45 University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30588/jo.v8i2.2085

Abstract

One physical characteristic that is helpful in comprehending the physical and chemical characteristics of a solution is the density of the polymer solution. Its primary function is to ascertain the polymer's concentration in solution. The density value can be used to estimate the polymer concentration in solution. The study of the flow and viscosity of polymer solutions also makes use of the interaction between the polymer and solvent. This study aims to establish a relationship between the density of porang and xanthan gum solutions and the percentage of porang, polymer content, and salinity. Machine learning models, like the Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), are used for modeling. The creation of these machine learning models used 471 digitized data of density curves of porang solution, xanthan gum solution, and porang-xanthan gum mixture solution. The training, validation, and testing processes of the ANN and ANFIS models provided average correlation coefficients of 0.99955 and 0.99999, respectively. Comparison between the estimates of the ANN and ANFIS models and the measurement results of 27 porang and xanthan gum solutions provided accurate results with correlation coefficients of 0.99893 and 0.99996, respectively.    
Co-Authors Arkaan, Muhammad Dzaki Dardjat, Izumi Wicaksono Havidh Pramadika Mardiana, Dwi Atty Maulida, Fajri Muhammad Taufiq Fathaddin, Muhammad Taufiq Rakhmanto, Pri Agung Rini Setiati Ristawati, Arinda Siahaya, Jacob Sonny Irawan Ulfah, Baiq Maulinda