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Abstrak :
Penlitian ini membahas tentang eliminasi componen metallic dan saturasi componen olefin bersama dengan eliminasi componen lain yang juga tidak di inginkan yang akan mempengaruhi laju reaksi dan kualitas produk. Solusi dari masalah ini adalah dengan mendesain sebuah trickle bed reactor berisikan Cobalt Molybdenum sebagai katalis yang dilengkapi dengan pelindung alumina dan mengandung HCL anhidrat. Reaktor ini mempunyai tinggi 3.33 m dan lebar diameter 1.5 berisikan 3080 kg katalis. Reactor yang mempunyai volume 5.874 m3 ini di desain untuk memproses 1 kg/s naphtha yang mengandung 0.03wt% sulfur, 40 ppb lead, dan 2vol% olefin. ...... This research discusses on removal of metallic compound and saturation of olefin with addition to other impurities removal from selected naphtha composition since it affects overall reaction rate and product quality. The solution is to design a trickle bed reactor loaded with Cobalt Molybdenum catalyst supported with alumina guard and potentially impregnated with anhydrous HCL. The reactor dimension is found to be 3.33 m in height and 1.5 in diameter packed with 3080 kg of catalyst. This 5.874 m3 reactor is designed to treat 1 kg/s naphtha which contains 0.03wt% sulphur, 40 ppb of lead and 2vol% of olefin.

Abstrak :
In this research, a layered-recurrent artificial neural network (ANN) using the back-propagation method was developed for simulation of a fixed-bed industrial catalytic reforming unit called Platformer. Ninety-seven data points were gathered from the industrial catalytic naphtha reforming plant during the complete life cycle of the catalytic bed (about 919 days). Ultimately, 80% of them were selected as past horizontal data sets, and the others were selected as future horizontal ones. After training, testing, and validating the model with past horizontal data, the developed network was applied to predict the volume flow rate and research octane number (RON) of the future horizontal data versus days on stream. Results show that the developed ANN was capable of predicting the volume flow rate and RON of the gasoline for the future horizontal data sets with AAD% (average absolute deviation) of 0.238% and 0.813%, respectively. Moreover, the AAD% of the predicted octane barrel levels against the actual values was 1.447%, which shows the excellent capability of the model to simulate the behavior of the target catalytic reforming plant.

Abstrak :
ABSTRAK
Fasilitas blending mogas berfungsi untuk mencampur komponen High Octane Mogas Component HOMC dan Naphtha sehingga menghasilkan produk dalam bentuk gasoline 88 atau lebih dikenal premium. Analisa kelayakan investasi dilakukan dengan metode Net Present Value NPV yang dilakukan dalam dua cara yaitu secara konvensional dan fuzzy.Penelitian ini menggunakan software MATLAB R2016a untuk melakukan perhitungan Fuzzy NPV berbasis Distribusi Triangular. Rentang nilai yang digunakan untuk variabel yang difuzzikan adalah rendah L , medium M , dan tinggi H .Hasil perhitungan dengan pendekatan fuzzy menunjukkan nilai yang berbeda dibandingkan dengan pendekatan konvensional. Hasil perhitungan NPV menggunakan metode konvensional menghasilkan nilai 10.6995 juta USD, sedangkan berbasis Fuzzy Distribusi Triangular menghasilkan 8.8129 juta USD. Adanya perbedaan tersebut dikarenakan variasi input terhadap tingkat suku bunga, pendapatan, dan total biaya blending.

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ABSTRACT
The blending mogas facility serves to mix High Octane Mogas Component HOMC and Naphtha components to produce gasoline 88 or known premium. Investment feasibility analysis is done by Net Present Value NPV method which is done in two ways, conventionally and fuzzy.This research uses MATLAB R2016a software to perform Fuzzy NPV calculation based on Triangular Distribution. The range of values used for the dif fered variables is low L , medium M , and high H .The results of calculations with the fuzzy approach show different values compared with the conventional approach. The NPV calculation results using conventional methods is 10.6995 million USD, while Fuzzy based Triangular Distribution is 8.8129 million USD. The difference is due because there are input variation to the interest rate, revenue, and total cost of blending.