Gas Oil Color (ASTM) Inference with Neural Network in an Oil Refinery Distillation Column

2001 ◽  
Author(s):  
C. Villamil ◽  
G. De Vincenti ◽  
N. Tumini ◽  
F. Masson ◽  
O. Agamennoni
Keyword(s):  
Neural Network ◽  
Distillation Column ◽  
Oil Refinery ◽  
Gas Oil ◽  
Oil Color

THERMAL NEUTRALIZATION OF CARBON-CONTAINING SLUDGE

2021 ◽  
Vol 3 (1) ◽  
pp. 118-123
Author(s):  
D. YU. UMANSKY ◽  
◽  
M. V. VASINA ◽  
Keyword(s):  
Sulfuric Acid ◽  
Aromatic Hydrocarbons ◽  
Petroleum Products ◽  
Oil Refinery ◽  
Gas Oil ◽  
Hazard Class ◽  
Resinous Substance ◽  
Oil Fractions ◽  
Petroleum Distillates

Acid tar is a resinous substance, which in most cases has a viscous structure. They are obtained as a result of sulfuric acid purification of petroleum distillates, oil residues, in the production of sulfonate additives, in the sulfonation and purification of oils, paraffins, kerosene and gas oil fractions and other petroleum products from aromatic hydrocarbons. Until recently, this type of waste was temporarily accumulated in specially designated areas - acid tar storage ponds, which were located near the oil refinery, which had a significant impact on the environment. The paper considers the process of formation of acid tar on the example of the production of sulfonate additives. The composition of the mixture of acid tar and sulfonate sludge was evaluated, and the hazard class of this type of waste was determined. Methods of utilization of acid tar are studied and a method of utilization of acid tar for the considered production is proposed.

First Principle Modeling and Neural Network–Based Empirical Modeling with Experimental Validation of Binary Distillation Column

2013 ◽  
Vol 8 (1) ◽  
pp. 53-70 ◽  
Author(s):  
Amit Kumar Singh ◽  
Barjeev Tyagi ◽  
Vishal Kumar
Keyword(s):  
Neural Network ◽  
Flow Rate ◽  
Distillation Column ◽  
Empirical Modeling ◽  
Experimental Setup ◽  
Ann Model ◽  
State Behavior ◽  
Relationship Model ◽  
Artificial Neural Network Ann ◽  
Model Temperature

Abstract To get the better product quality and to decrease the energy consumption of the distillation column, an accurate and suitable nonlinear model is crucial important. In this work, two types of model have been developed for an existing experimental setup of continuous binary distillation column (BDC). First model is a theoretical tray-to-tray binary distillation model for describing the steady-state behavior of composition in response to changes in reflux flows and in reboiler duty. Another model is an artificial neural network (ANN)–based input/output data relationship model. In ANN-based model, temperature of first tray, feed flow rate, and column pressures have been taken in addition to reflux flow rate and reboiler heat duty as inputs to give the more accurate I/O relationship. The comparison of output of ANN model and the equation-based model with the real-time output of the experimental setup of BDC has been given for the validation of developed models.

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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