110th Anniversary: An Effective Methodology for Kinetic Parameter Estimation for Modeling Commercial Polyolefin Processes from Plant Data Using Efficient Simulation Software Tools

Abstract

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 stepby-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.

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

“…We build a first-principles steady-state model of a Mitsui slurry high-density polyethylene (HDPE) process by following the methodology and kinetic parameters presented in supplement 1b of Sharma and Liu. 78 For this application, it is easier to first estimate the complex multisite Ziegler-Natta polymerization kinetic parameters using steady-state production targets, and then convert the resulting steady-state simulation model based on Aspen Plus to a dynamic simulation model using Aspen Plus Dynamics. The resulting dynamic simulation model has similar independent process variables, including the feed flow and compositions and the reactor operating conditions.…”

“…The hybrid model includes the effect of temperature on the fermentation kinetics and show good nonlinear approximation capability. Sharma and Liu 78 show how to use plant data to estimate kinetic parameters of first‐principles models for industrial polyolefin processes. In a recent study Bangi and Kwong 79 estimate process parameters in hydraulic fracturing process using deep neural network which are then input to a first‐principles model.…”

“…We apply the combined direct modeling strategy to an industrial polyethylene process for the prediction of melt index. We build a first‐principles steady‐state model of a Mitsui slurry high‐density polyethylene (HDPE) process by following the methodology and kinetic parameters presented in supplement 1b of Sharma and Liu 78 . For this application, it is easier to first estimate the complex multisite Ziegler–Natta polymerization kinetic parameters using steady‐state production targets, and then convert the resulting steady‐state simulation model based on Aspen Plus to a dynamic simulation model using Aspen Plus Dynamics.…”

“…The goal is to predict the operating conditions for producing new polymer grades, given the desired product quality targets, such as melt index (MI), polymer density (Rho), polydispersity index (PDI) and polymer production rate (P). The details of the steady‐state simulation model are available in supplement 1b of reference 78.…”

“…We first estimate the polymerization kinetic parameters from plant production targets in a steady‐state model using Aspen Polymers based on our reported methodology 78 . This results in a validated Aspen Polymers steady‐state simulation model.…”