Activating additives for intensification of primary crude oil distillation

1986 ◽  
Vol 22 (7) ◽  
pp. 319-321
Author(s):  
I. M. Saidakhmedov ◽  
Z. I. Syunyaev ◽  
O. F. Glagoleva
Keyword(s):  
Crude Oil ◽  
Activating Additives ◽  
Crude Oil Distillation

ANALYTICAL APPROACH FOR THE DETERMINATION OF MOLECULAR WEIGHTS OF n-ALKANES IN CRUDE OIL DISTILLATION CUTS

1997 ◽  
Vol 15 (3-4) ◽  
pp. 273-282
Author(s):  
Marco Antonio G. Teixeira ◽  
Artur L. Scofield ◽  
Marcos Vinicius R. Cabral
Keyword(s):  
Crude Oil ◽  
Analytical Approach ◽  
Molecular Weights ◽  
Crude Oil Distillation

Increasing the distillate yields in atmospheric distillation of crude oil by means of activating additives

1986 ◽  
Vol 22 (1) ◽  
pp. 6-7
Author(s):  
I. M. Saidakhmedov ◽  
O. F. Glagoleva ◽  
A. �. Grushevenko
Keyword(s):  
Crude Oil ◽  
Atmospheric Distillation ◽  
Activating Additives

scholarly journals Simulation–Optimization-Based Design of Crude Oil Distillation Systems with Preflash Units

2018 ◽  
Vol 57 (30) ◽  
pp. 9821-9830 ◽  
Author(s):  
Minerva Ledezma-Martínez ◽  
Megan Jobson ◽  
Robin Smith
Keyword(s):  
Crude Oil ◽  
Simulation Optimization ◽  
Crude Oil Distillation ◽  
Optimization Based Design

Development of Neural Network Models for a Crude Oil Distillation Column

2012 ◽  
Author(s):  
Khairiyah Mohd. Yusof ◽  
Fakhri Karray ◽  
Peter L. Douglas
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Crude Oil ◽  
Distillation Column ◽  
Network Models ◽  
Ann Model ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

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