Distillation is one of the most important separation processes used in many chemical industries. It can be classified into two ways namely binary distillation for separation of mixture of two substances and multi-component distillation for separation of mixture of more than two substances. Distillation is an energy-intensive separation process in which a liquid or vapor mixture of two or more substances is separated into its component fractions of desired purity by the application and removal of heat. It has extensively been studied (Eden et al., 2000; McAvoy, 1990). There are essentially two approaches by which nonlinear models can be developed for a distillation column; from first principles by using the process knowledge or empirically from input/output data. The advantages and disadvantages of each approach are well known. In industrial practice, it is not always possible in general to obtain accurate first principles models for high-purity distillation columns. Most industrial columns are used to separate multi-component mixtures whose constituent elements are often not known completely; the fundamental thermodynamics of multi-component vapor-liquid equilibrium, the physical property data, and other essential constitutive relations required for the successful development of a first principles model are not always available. And even when such knowledge is available, the resulting models usually occur in the form of a very large system of coupled nonlinear ordinary differential equations, and may therefore not always be the most convenient for controller or fault detection (FD) design. On the other hand, when fundamental process knowledge is unavailable or incomplete, or when the resulting model may not be particularly suitable for FD applications, the input/output models identified from plant data may be more useful. Nonlinear autoregressive models have been used (James et al., 2002), as well as models that use a set of polynomial basis functions (e.g. Volterra functions). Also ANNs provide an excellent mathematical tool for dealing with severe nonlinear problems. Another attractive property is self-learning ability. As a result, nonlinear systems can be modeled with a great flexibility. These features allow one to employ artificial neural networks to model complex, unknown and nonlinear dynamic processes.
The main aim of this paper is to establish a reliable model both for the steady-state and unsteady-state regimes of a nonlinear process. The use of this model should reflect the normal behavior of the process and allow distinguishing it from an abnormal one. In order to obtain this reliable model, the artificial neural network (ANN) is used to model plant input-output data by means of a NARX model. Also this paper shows another technique for neural model reduction into account the physical knowledge of the process. The study shows the choice and the performance of the ANN in the training and test phases. An analysis of the inputs choice, time delay, hidden neurons and their influence on the behavior of the neural estimator is carried out. Three statistical criteria are used for the validation of the experimental data. The model is implemented by training a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with input-output experimental data. After describing the system architecture, a realistic and complex application as a distillation column is presented in order to illustrate the proposed ideas concerning the dynamics modelling and model reduction. Satisfactory agreement between identified and experimental data is found and results show that the reduced neural model successfully predicts the evolution of the product composition.
Keywords: Reliability; modelling; artificial neural network; NARX; distillation column; product quality.
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