Acrylic fiber is commercially produced by free radical polymerization, initiated by a redox system. Industrial production of polyacrylonitrile is a variant of aqueous dispersion polymerization, which takes place in a homogenous phase under isothermal conditions with perfect mixing. The fact that the kinetics are a lot more complicated than those of ordinary polymerization systems makes it difficult to control the molecular weight. On the other hand, abundant data is being gathered in industrial polymerization systems, and this information makes the neural network based controllers a good candidate for managing such a difficult control problem. Multilayer neural networks have been applied successfully in the identification and control of dynamic systems. In this work, the neural network based control of continuous acrylonitrile (ACN) polymerization is studied, based on a previously developed new rigorous dynamic model for the polymerization of acrylonitrile. Two typical neural network controllers are investigated, i.e., model predictive control and NARMA-L2 (Nonlinear Auto Regressive Moving Average) control. These controllers are representative of the variety of common ways in which multilayer networks are used in control systems. The results present a comparison of the two common neural network controllers, and indicate that the model predictive controller requires a larger computational time.

-A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES) has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.

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A common problem in model verification is to determine the values of model parameters that provide the best fit to measured data, based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently nonconvex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. As the need for a user-interactive parameter estimation software, especially for identifying kinetic parameters, was needed; in this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES) has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.

This study involves real-time monitoring and fault diagnosis in batch baker's yeast fermentation. A specific Real Time Statistical Process Analysis and Control (RT-SPAC) program was developed to monitor instantaneous reaction conditions. The air flow rate fed to the reactor, temperature, pH, and dissolved oxygen concentration in a laboratory-size fermenter were monitored and recorded by means of on-line sensors. Under control of the RT-SPAC program, 22 batch baker's yeast fermentation operations were carried out. In the first 20 operations, an ordinary process was followed under previously defined nominal operating conditions. Historical data collected from these batches were then used for online Dynamic Principal Component Analysis (DPCA) in the course of the following two batches. The last two batches were implemented such that some deliberate faults (in temperature and pH) were introduced during the operation. The results indicated that the software was capable of capturing the process faults, and furthermore the possible causes of these faults were identified by contribution plots.