Development of neural network based models to control temperature and estimate composition of a debutaniser column / Nasser Mohamed Ramli

Nasser, Mohamed Ramli (2015) Development of neural network based models to control temperature and estimate composition of a debutaniser column / Nasser Mohamed Ramli. PhD thesis, University of Malaya.

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Abstract

The current method for composition measurement of an industrial distillation column specifically offline method, is slow, tedious and could lead to inaccurate results. Among the advantages of using online composition designed are to overcome the long time delay introduced by laboratory sampling and provide better estimation, which is suitable for online monitoring purposes. Principal component and partial least square analysis are used to determine the important variables surrounding the column prior to implementing the neural network. It is due to the different types of data available for the plant, which requires proper screening in determining the right input variables to the dynamic model. Statistical analysis is used as a model adequacy test for the composition prediction of n-butane and i-butane in the column. Simulation results showed that the Artificial Neural Network (ANN) can reliably predict the online composition of the column. The major contribution of the current research is the development of composition prediction of n-butane and i-butane using equation based neural network (NN) models. Based on statistical analysis, the results indicate that neural network equation, which is more robust in nature, predicts better than the PLS equation and RA equation based methods. The temperature predictions using neural network equation are also compared with partial least square (PLS) and regression analysis (RA) equations methods. A new technique for nonlinear system, which is based on hybrid neural network modeling, is proposed. The hybrid model consists of combination of residual composition and residual temperature with first principle in terms of mass and energy balance. Hybrid neural network equation performs better than the hybrid neural network, and neural network predictions to estimate composition and temperature for the column. The use of an inverse neural network and forward neural network are used for the direct control of a distillation column. The neural network used for the control strategy to track the set point of the top and bottom temperature. Neural network iv estimators are used to track the set point of the top and bottom composition together with disturbances. There are two types of controller used for control strategies which are the direct inverse control (DIC) and internal model controller (IMC). Based on the results, IMC and DIC were found to perform better in controlling the temperature with respect to set point changes and disturbances compared to conventional PID controllers.

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