Abstract
A neural networks controller is developed and used to regulate the temperatures in a crude oil distillation unit. Two types of neural networks are used; neural networks predictive and nonlinear autoregressive moving average (NARMA-L2) controllers. The neural networks controller that is implemented in the neural network toolbox software uses a neural network model of a nonlinear plant to predict future plant performance. Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. A comparison has been made between two methods to test the effectiveness and performance of the responses. The results show that a good improvement is achieved when the NARMA-L2 controller is used with maximum mean square error of 103.1 while the MSE of neural predictive is 182.7 respectively. Also shown priority of neural networks NARMA-L2 controller which gives less offset value and the temperature response reach the steady state value in less time with lower over-shoot compared with neural networks predictive controller.
Highlights
The crude oil distillation unit (CDU) fractionation column separates the feed crude into different cuts suitable for the different refinery processing units
A step change in feed temperature from 340 to 350°C is carried out using neural network predictive and neural network nonlinear autoregressive-moving average (NARMA)-L2 controller to control the temperatures of naphtha, kerosene, light gas oil (LGO) D86 95%, top, intermediate and bottom pumparounds
The results presented in this paper have clearly shown the ability of neural networks to act as process controllers
Summary
The crude oil distillation unit (CDU) fractionation column separates the feed crude into different cuts suitable for the different refinery processing units. The temperature control is based on the assumption that the product composition can satisfy its specification when an appropriate tray temperature is kept constant at its set-point [2]. In the control of crude oil distillation columns is usually difficulty to get accurate and reliable product composition measurements without time delay. Many composition analyzers such as gas chromatography, NIR (NearInfrared) analyzers, suffer from large measurement delays and high investment and maintenance costs and usually possess significant time lags. In inferential control of product composition is used by estimation from other measured variables [3]
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