Amir Akbar, Ian Haikal (2020) Optimisasi Konsumsi Energi Pada Proses Kolom Depropanizer Berbasis Neural Network - Particle Swarm Optimization (NN-PSO). Other thesis, Institut Teknologi Sepuluh Nopember.
Preview |
Text
02311640000021-Undergraduate_Thesis.pdf Download (4MB) | Preview |
Abstract
Kolom Distilasi merupakan plant yang digunakan untuk memisahkan komponen
senyawa hydrocarbon pada crude oil dan natural gas berdasarkan nilai
volatilitasnya. Penggunaan energi yang tinggi pada equipment Condenser dan
Reboiler menuntut adanya optimisasi konsumsi energi kolom distilasi untuk
menekan biaya utilitas cooling water dan steam. Metode optimisasi dilakukan
dengan cara mencari nilai variabel operasional pada kolom distilasi tersebut yang
menghasilkan konsumsi energi minimum. Proses pencarian nilai variabel
operasional kolom distilasi dilakukan dengan menggunakan metode Stochastic
Algorithm yaitu Particle Swarm Optimization (PSO). Kolom distilasi yang bersifat
kompleks dan non linier dimodelkan dengan Neural Network (NN), dan keluaran
dari model NN digunakan untuk penentuan fungsi obyektif pada algoritma PSO.
Variabel operasional dari kolom depropanizer yang akan dilakukan optimisasi yaitu
Feed – Molar Flow (F), Feed – Temperature (Tf), Condenser – Pressure (Pc),
Reboiler – Pressure (Pr). Performansi arsitektur Neural Network Feed Forward -
Backpropagation dengan algoritma training Lavenberg Marquart pada jumlah
neuron 19 menghasilkan nilai RMSE Qc, RSME Qr, dan RMSE total berturut –
turut sebesar 5,541 x 10-4 ; 6,310 x 10-4 ; 8,398 x 10-4. Metode optimisasi konsumsi
energi menggunakan algoritma PSO menghasilkan penghematan konsumsi energi
pada Reboiler dengan nilai efisiensi Er sebesar 29,27 %, sedangkan pada equipment
Condenser terjadi peningkatan konsumsi energi dibandingkan dengan kondisi
eksisting sehingga nilai efisiensi Ec sebesar -33,75 %, sehingga secara keseluruhan
dapat memberikan penghematan biaya utilitas sebesar 16,92 %.
========================================================================================================================
Distillation column is a plant that is used to separate components of hydrocarbon
compounds in crude oil and natural gas based on their volatility values. The high
energy usage of Condenser and Reboiler equipment demands the optimization of
the distillation column energy consumption to reduce the utility cost of cooling
water and steam. Optimization methods performed by searching operational
condition variable values distillation column to produce minimum energy
consumption, the process of finding the value of distillation column operational
variables using Stochastic Algorithm, that is Particle Swarm Optimization (PSO).
Distillation columns that are complex and non-linear can be modeled using Neural
Network (NN), and the output from the NN model is used to determine the objective
function in the PSO algorithm. The operational variables of the Depropanizer
Column to be optimized are Feed - Molar Flow (F), Feed - Temperature (Tf),
Condenser - Pressure (Pc), Reboiler - Pressure (Pr). Architecture performance of
Neural Network Feed Forward - Backpropagation with Lavenberg Marquart's
training algorithm on the number of neurons 19 produces RMSEQc, RSMEQr, and
RMSEtotal values respectively 5,541 x 10-4; 6,310 x 10-4; 8,398 x 10-4. Energy
consumption optimization method using the PSO algorithm result in savings of
energy consumption in the reboiler with Er efficiency value of 29.27%, while in
Condenser equipment there is an increase in energy consumption compared to
existing conditions, so the Ec efficiency value of -33.75%, so that as a whole can
provide utility cost savings amounting 16.92%.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Depropanizer Column, Distillation Column, Neural Network, Optimization, Particle Swarm Optimization, Kolom Depropanizer, Kolom Distilasi, Neural Network, Optimisasi, Particle Swarm Optimization. |
| Subjects: | Q Science > QC Physics > QC320 Heat transfer |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Ian Haikal Amir Akbar |
| Date Deposited: | 06 Aug 2020 05:33 |
| Last Modified: | 27 May 2023 04:36 |
| URI: | http://repository.its.ac.id/id/eprint/77077 |
Actions (login required)
 |
View Item |