عنوان مقاله [English]
Production of nano-size polymers has many applications in petrochemical industries. Product quality control and process operation state are two of the most important aspects of production of nano-polymer particles. For this purpose, we need to be informed about key process variables like conversion percentage, molecular weight distribution, diameter and number of particles, in an online manner. While a limited number of variables like temperature, pressure, and flow current can be measured online. These information are not adequate for process analysis. Traditionally, offline methods were used for this purpose with a considerable delay compared to high speed of polymerization processes. For this reason, the process efficiency and the product quality diverts from their optimum condition considerably. Researchers have used software sensors to resolve this problem. These sensors are known as state observers and use a simple mathematical model of the process with addition of a correction term that is proportional to the difference between estimated and actual values. Also, they use online data like temperature to predict required variable values. In this way, users have access to online characteristics of the product and system condition. So, they can apply process changes on time, prevent the system from diversion, be informed about possible faults of the production path and recover them. In this paper, we use high gain linear observer to follow monomer conversion in Butyl Acrylate polymerization in emulsion method with calorimetry data. Then we will be able to cross check the observers performance with our experimental data.