Prediksi Kendali pada Furnace dengan Menggunakan Neural Network Model Predictive Control (NNMPC) di PPSDM Migas Cepu

Danandyo, Dimas (2022) Prediksi Kendali pada Furnace dengan Menggunakan Neural Network Model Predictive Control (NNMPC) di PPSDM Migas Cepu. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Crude oil atau minyak mentah merupakan bahan dasar untuk olahan produk minyak
bumi yang diproses melalui proses pemisahan fraksi distilasi di kolom distilasi pada
kilang sehingga didapatkan minyak bumi olahan sesuai dengan spesifikasi yang
dibutuhkan, di mana proses pengolahannya sendiri terbagi menjadi beberapa bagian
antara lain pemanasan, pemisahan, penguapan, pengembunan, dan pendinginan.
Crude oil dipanaskan di dalam furnace dengan temperatur sebagai variabel yang
paling penting yang mana akan sangat menentukan kualitas dari olahan hasil
produksi. Jaringan syaraf tiruan sendiri digunakan untuk merancang neural network
model predictive control (NNMPC) pada penelitian tugas akhir ini untuk
memprediksi kinerja dan performansi sistem kendali yang ideal karena
kelebihannya dalam melakukan pembelajaran dari data input dan output suatu
proses. Pada penelitian yang dilakukan, NNMPC menunjukkan performa yang
lebih baik ditinjau dari karena rise time, settling time, peak time yang lebih cepat;
serta error steady state (ESS) yang lebih rendah dibandingkan dengan pengendali
PI yang digunakan sebagai pembanding langsung.
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Crude oil is the raw material for the processing of petroleum products which is
processed through a distillation separation process at the refinery so that refined
petroleum is obtained according to the required specifications, where the
processing process itself is divided into several parts including heating, separation,
evaporation, condensation, and cooling. Crude oil in the furnace run the process
with temperature as the most important variable which will greatly determine the
quality of the processed products. The artificial neural network itself is used to
design a neural network model predictive control (NNMPC) in this undergraduate
thesis research to predict the performance of an ideal control system because of its
advantages in learning from input and output data of a process. In the research
conducted, NNMPC showed better performance quickly in terms of rising time,
settling time, and peak time; also, lower steady state error (ESS) than the PI
controller which is used as a direct comparison.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Furnace, Artificial Neural Network, NNMPC
Subjects:	Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) T Technology > TJ Mechanical engineering and machinery > TJ217.6 Predictive Control
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Dimas Danandyo
Date Deposited:	22 Feb 2022 01:28
Last Modified:	22 Feb 2022 01:28
URI:	http://repository.its.ac.id/id/eprint/94694

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