Polymer/Polymer MFI
Polymer/Mass ow
3.5 5 4.5 4
20.2 20.5 20.4 20.3 20.1
Polymer/Mass ow - Measured Polymer/Mass ow - Simulator
Polymer/Mass ow - Measured Polymer/Mass ow - Simulator
3
2.5
20
Catalyst switched
2
19.5 19.7 19.6 19.9 19.8
1 0.5 1.5
0
Figure 4 Monitoring polymer MFI and mass flow rate: measured vs simulated data
variations in 1-hexene and hydrogen flow rates have a neg - ligible impact, while changes in ethylene feed flow rate have a slight impact. These findings underscore the importance of precise temperature control in optimising polymer properties. Polymer process digital twin modelling analysis Beyond parameter sensitivity, the digital twin solution enables the monitoring and prediction of polymer properties during grade transitions. A dynamic process simulation model was developed to monitor polymer grade changes during catalyst transitions and predict polymer properties. The model was connected to plant historian data, and simulation results were validated against actual measurements to confirm accuracy. Once validated, the model was used to predict the evolution of polymer properties, such as MFI, density, and molecular weight, over time as feed and catalyst conditions changed. This made it possible to estimate the required transition time. Figure 3 shows catalyst flow rates over time during the transition from catalyst A to catalyst B. To achieve the poly - mer grade transition from MFI 0.2-0.4 g/10 min to MFI 1.4– 1.6 g/10 min, approximately 12 hours of transition time was required. During this time, the MFI increased from 0.5 to 1.5 g/10 min, corresponding to catalysts A and B, respectively. Figure 4 compares measured and simulated MFI results dur - ing the transition. During that time, the polymer mass flow rate revealed a slight decrease before stabilising. By running the polymer modelling process digital twin, engineers could predict polymer properties in advance and reduce transition time, thereby reducing the off-grade prod - ucts per transition. The off-grade production quantity is the product of transition time multiplied by the polymer produc - tion rate that is outside the specification range. Collectively, these results demonstrate how sensitivity analysis and digital twin prediction complement each other to optimise polymer - isation performance while minimising off-grade production. Summary and conclusions The adoption of a process simulation digital twin offers a promising solution to address polymerisation challenges in polyolefin production. By using advanced modelling and
simulation capabilities, engineers can improve product qual - ity and polymer properties while optimising operational conditions. The study showed that increasing catalyst mass flow rate, temperature, and hydrogen feed produced higher polymer MFI, while temperature increases have a negative impact on polymer density. Sensitivity analysis within the polymerisation reactor simulation clearly identified these relationships, allowing operators to fine-tune process param - eters such as catalyst flow rate, temperature, and pressure to achieve desired polymer properties like MFI and density. By enabling real-time monitoring and predictive analysis, the digital twin reduced grade transition time and minimised off-grade products, ultimately lowering cost. Additionally, the integration of polymer modelling with advanced pro - cess control (APC) systems and operator training simulators (OTS) enhanced decision-making, allowing users to validate control strategies and train operators in a safe, virtual envi - ronment. Dynamic polymer models can be linked with dis - tributed control system (DCS) emulators in OTS. Additionally, these models can be connected to APC to tune proportion - al-integral-derivative (PID) before applying them. Together, these capabilities support more efficient, indi - rectly lower emission polymer production and continue Bringing Decarbonization to Life. Bringing Decarbonization to Life is a mark of KBC. Reference 1 Polyethylene Market Size (2025-2030 ), Virtue Market Research, January 2025, VMR-1463. Ghoncheh Rasouli is a Technical Consultant at KBC (A Yokogawa Company). She specialises in refinery and petrochemical polymer pro - cess simulation with a focus on AI- and ML-driven modelling, energy transition, and circular economy solutions such as waste-to-fuel. Ghoncheh holds a PhD from McGill University, specialising in computa - tional modelling of polymer phase separation kinetics. Email: ghoncheh.rasouli@kbc.global Alan Chew is the Technology Services Consultant at KBC (A Yokogawa Company) in Singapore. He has more than 10 years of experience in the process modelling and simulations of refinery and petrochemical units. Alan holds a degree in chemical engineering from Seoul National University, South Korea. Email: alan.chew@kbc.global
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