Soft-sensor estimation via parameter fitting and dynamic optimization in an experimental batch butadiene homopolymerization reactor

Abstract

In this work, we address modeling and experimental tasks to develop an industrial process for the anionic polymerization of butadiene, which is a key compound used in the production of high-performance tires. The aim of this study was to determine the processing conditions that lead to the efficient manufacturing of polybutadiene. Accordingly, an experimental polymer reaction facility was constructed to record both butadiene conversion and temperature profiles. A first-principles dynamic mathematical model was developed to track the measurement variables as best as possible. To improve the tracking of recorded variables, some process variables were deployed as manipulated variables. Because good experimental tracking was not acceptable, we used the soft-sensor approach to estimate some additional degrees of freedom. The estimation task was performed by describing the problem as a dynamic optimization problem. The results clearly show that the optimization and control tasks of butadiene polymerization reaction facilities are demanding and require good experimental information to develop reliable mathematical models that can be leveraged to speed up the development of such industrial polymerization systems.