110th Anniversary: An Effective Methodology for Kinetic Parameter Estimation for Modeling Commercial Polyolefin Processes from Plant Data Using Efficient Simulation Software Tools
Polyolefins are one of the most widely used commodity polymers with applications in films, packaging, and the automotive industry. The modeling of polymerization processes producing polyolefins, including high-density polyethylene (HDPE), polypropylene (PP), and linear low-density polyethylene (LLDP...
Gespeichert in:
Veröffentlicht in: | Industrial & engineering chemistry research 2019-08, Vol.58 (31), p.14209-14226 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Polyolefins are one of the most widely used commodity polymers with applications in films, packaging, and the automotive industry. The modeling of polymerization processes producing polyolefins, including high-density polyethylene (HDPE), polypropylene (PP), and linear low-density polyethylene (LLDPE) using Ziegler–Natta catalysts with multiple active sites, is a complex and challenging task. Most of the studies on polyolefin process modeling over the years do not consider all of the commercially important production targets when quantifying the relevant polymerization reaction kinetic parameters based on measurable plant data. Most of the published articles also do not make efficient use of simulation tools, particularly sensitivity analysis, design specifications, and data fit, that are available in commercial modeling software for polymerization processes, such as Aspen Polymers. This paper presents an effective methodology to estimate kinetic parameters that have the most significant impacts on specific production targets, and to develop the kinetics using all commercially important production targets validated over polyolefin processes producing HDPE, PP, and LLDPE using Ziegler–Natta catalysts. We demonstrate how to estimate kinetic parameters to fit production targets in a computer-aided step-by-step procedure. The percent errors between our model predictions and plant data are equivalent to or smaller than those in reported modeling studies for polyolefin processes. We report our insights and experiences from training practicing engineers to successfully apply our methodology to several dozen commercial HDPE, PP, and LLPDE processes for sustainable design, operation, and optimization at two of the world’s largest petrochemical companies in the Asia-Pacific region over the past two decades. Finally, we present supplements of detailed modeling examples and an Excel modeling spreadsheet for commercial polyolefin processes. |
---|---|
ISSN: | 0888-5885 1520-5045 |
DOI: | 10.1021/acs.iecr.9b02277 |