polymer modelling was to analyse, monitor, predict, and optimise process variables, such as olefin feed, initiator, hydrogen, catalyst rates, temperature, and pressure. The goal was to enhance polymerisation reactor performance, achieve desired polymer properties, and mini- mise grade changes by reducing transition time and off-grade production. This study examined high-density polyethylene (HDPE), MFI, polymer conversion rate, and yield using comprehensive process modelling.
Generic polymerisation reactor KPIs and key process variables
Key performance indicators (KPIs) • Polymer production rate/yield
Key process variables
• Feed flow rate
• Conversion
Monomer/comonomer flow rate Solvent/modifier/hydrogen flow rate
• Polymer properties
• Utility flow rate/temperature
Melt flow index (MFI) Polymer density ( ρ )
Table 1
In the polymer industry, multiple product grades are often produced in a single production line. Switching catalysts is common, as different catalyst types produce polymers with distinct physical properties, such as molecular weight distribution, branching, tensile strength, impact resistance, and MFI. In this study, catalyst type A was switched to catalyst type B to produce a polymer grade with higher MFI and lower density. By applying the simulation model within the pro- cess digital twin model, engineers could monitor real-time polymer MFI and maintain stable feed rates. This minimised off-grade production without delays caused by laboratory testing. The digital twin automatically ran and saved the sim- ulation, enabling sensitivity analysis of how different param- eters and operating conditions affected MFI, conversion rate, and yield without extensive reconfiguration. A high-fidelity digital twin linked with real-time plant historian data enabled plant-wide optimisation and energy integration across the petrochemical, polymer, and plas- tic recycling plants on a single platform. By simulating the behaviour of both catalysts and resulting polymer proper- ties, the digital twin fine-tuned the process to achieve the desired polymer grade quickly, minimising the off-spec pro- duction period during catalyst switches. It tracked variables such as temperature, pressure, cata- lyst activity, and polymer properties, providing immediate feedback on process response to the new catalyst changes. This approach reduced trial-and-error and ensured faster transitions. Dynamic simulation helped find the best cata - lyst injection rate, reactor temperature profile, hydrogen and comonomer feed strategy, within the transition time. Sensitivity analysis and strategy development The sensitivity analysis tools available in the process simu- lation software were applied to evaluate how key process variables and operating conditions impact KPIs. Table 1 lists polymerisation KPIs and key process variables, highlighting examples of polymer MFI and density. This analysis incorporates two KPIs, MFI and density, as polymer property metrics. The MFI is defined as the mass (in grams) of polymer that flows through a capillary of spec - ified diameter and length within 10 minutes under pressure generated by a 2.16 kg load at 190°C. Sensitivity analyses were conducted under the base oper- ating conditions shown in Table 2 . The process variables included temperature, pressure, mass flow rate of ethylene, hydrogen, and catalyst flow rate to evaluate their effect on polymer yield and properties, specifically MFI and density.
Base case operating conditions
Base condition T emperature °C Pressu re, bar
Value
100
41.22
Feed and catalyst mass flow rate, t/h Ethylene
20 0.1
1-Hexene Hydrogen Isobutane Catalyst A Catalyst B
0.001
16
0.01
0
Table 2
model. Integrating plant data with the model, including digi- tal twin capabilities, enabled online calibration of the polym- erisation model based on actual plant performance. The automated digital twin increased confidence in the model’s accuracy and reliability, with further elaboration as follows: • Integrating plant data with simulation software : The first step in setting up the process digital twin involved defin - ing key performance indicators (KPIs) for polymer model- ling and connecting the process simulator with plant data historians. This integration allowed engineers to monitor real-time operating data and plant performance alongside simulation results for continuous comparison and analysis. • Sensitivity analysis and strategy development : The sim- ulation software’s sensitivity analysis tools helped engineers identify critical process parameters and operating condi- tions that influenced polymer yield and properties. Applying sensitivity analysis with optimisation enabled engineers to fine-tune the polymerisation reactor to improve efficiency and product performance. • Real-time monitoring and calibrating with process dig- ital twin : With plant data accessible within the simulation software, engineers monitored deviations between sim- ulated and actual plant behaviour. This iterative process involved tuning reaction kinetic parameters to align simu- lation results with plant data. Using the calibrated model, engineers can predict key polymer properties, such as MFI, polymer density, and polydispersity index (PDI) to minimise off-grade products. The process digital twin also supported operator training and decision-making. Engineers simulated grade changes before implementing them, reducing the risks of delays or errors. The ultimate objective of using simulation software for
46
PTQ Q1 2026
www.digitalrefining.com
Powered by FlippingBook