Sunwon Park

SIMULATION AND OPTIMIZATION OF A STYRENE MONOMER REACTOR USING A NEURAL NETWORK HYBRID MODEL

Abstract The modeling and optimization of a dehydrogenation reactor in the industrial styrene monomer plant has been proposed in this study. Because this reactor consumes large amount of expensive high-pressure steam to produce the styrene monomer (usually more than two third of total energy costs), the minimization of the operating cost is highly desirable to maximize the profit. However, it is not easy to develop the accurate mathematical model because of the lack of internal or intermediate measurements of the industrial reactor, and also the lack of experimental results of the catalyst deactivation. To overcome these difficulties, we propose an alternative model of the styrene monomer reactor using a hybrid model in which the mathematical model is combined with neural networks. Major reaction mechanism is described in the mathematical model, and the deactivation effect is modelled in neural network using the real operation data. Using this model, the sensitivity analysis and the optimization of the industrial plant have been performed. The proposed optimal operation enlarges the profit of the plant very much.

Development of An Industrial Complex Steam Network Optimization Method Using Steam Networking Matrices(SNMs)

Most chemical companies try to maximize their energy efficiencies due to high oil price and reinforcement of environmental regulation. An individual factory continuously has tried to reduce energy consumption or carbon dioxide discharge for high profit. Nevertheless, it is found that waste heat is disposed with forms of low or medium pressure steams. It can be improved by the aspect of entire industrial complex. Therefore, we have developed a steam network optimization method using Steam Networking Matrices(SNMs) in this research. Results from an illustrative example show that energy consumption can be reduced by optimizing steam exchange networks.

Quantitative Analysis of Biological Models under the Internet Environment

The computational modeling and simulation of complex biological systems are indispensable for new knowledge extraction from huge experimental data and ever growing vast amount of information in systems biology. Moreover, gathering and sharing of the existing information and newly-generated knowledge can speed up this research process. In this regard, several modeling projects have been undertaken for quantitatively analyzing the biological systems via the internet. They include Virtual Cell, JWS and OBIYagns. We also develop an integrated web-based environment, which facilitate investigation of dynamic behavior of cellular systems.