Advanced simulation of polyolefin production
Minimise grade transition times and reduce off-grade products using polymer modelling, process digital twins, and sensitivity analysis
Ghoncheh Rasouli and Alan Chew KBC (A Yokogawa Company)
P olyolefins, derived from olefins, are essential materials in various industries due to their versatility, cost-effec- tiveness, and favourable mechanical properties. The global polyolefin market, particularly polyethylene, is expe- riencing significant growth, with some forecasts suggesting it could double over the next 20 years. However, the pro- duction process faces challenges, including complex polym- erisation reactions that require precise control to achieve desired polymer specifications, such as bulk density and melt flow index (MFI). The following study investigates the challenges in polymer production using process simulation software integrated with advanced kinetic polymerisation models. It focuses on real- time reactor performance, prediction of polymer properties under varying conditions, and strategies to minimise grade transition times and reduce off-grade products. In addition, sensitivity analysis evaluates the impact of different operating conditions on polymer yield and quality, providing actionable insights for optimising the polyolefin manufacturing process. Ultimately, the proposed approach supports more consistent product quality and helps lower production costs. Polyolefin market Polyolefins are pivotal materials in packaging, automotive, construction, and consumer goods due to their versatility, durability, and low cost. Polyethylene accounts for roughly 34% of global plastic demand and continues to drive indus- try growth, according to recent market analysis.1 The polyolefin market is experiencing substantial growth, with the global polyethylene market valued at about $120 billion in 2024 and projected to reach $165 billion by 2030, reflecting a compound annual growth rate (CAGR) of around 5.5%.1 Global polyethylene production now exceeds 100 mil- lion metric tons annually, underscoring its dominance within the broader polyolefin sector. Sustained demand across flex- ible packaging, construction materials, and advanced man- ufacturing is expected to drive capacity expansion through the next decade, highlighting the importance of process effi- ciency, product quality, and innovation in polymer production. Production challenges However, polyolefin production presents several challenges. The polymerisation process involves complex reactions and
operating conditions that must be controlled to prevent reac- tor temperature runaways and achieve polymer-grade spec- ifications, such as bulk density and MFI. Lab-scale development and commercial scale-up for new polymer grades and technologies introduce further uncer- tainty. Variables such as monomer and comonomer com- position, solvent modifiers, hydrogen flow rates, reactor conditions, and catalyst activity affect product properties. These factors make it difficult to consistently produce poly- olefins with specific properties. Maintaining tight control over molecular weight distribution and MFI during grade transitions while minimising off-grade production remains one of the industry’s greatest challenges. Addressing these challenges is essential to meet market demand for high-quality polyolefin products. Therefore, manufacturers aim to validate process design and enhance performance through advanced monitoring, control, optimi- sation, and decarbonisation initiatives. Applying polymerisation modelling software helps reduce scale-up risks, experimental time, and operational issues. It also improves design accuracy, monitoring, and control for specific polymer grades. In addition, polymerisation modelling software calibrates process models to enhance prediction accuracy for reactor performance, polymer yield, and product properties. Sensitivity analysis tools within the simulator identify key process parameters that affect yield and quality. This approach enables engineers to consistently achieve desired specifications. The integrated platform, combining polymerisation reactor modelling, process digital twins, real-time optimisation (RTO), and advanced process control (APC), provides real-time pre- diction of polymer properties during grade transitions. This capability helps operators optimise operating conditions while reducing transition time and off-grade production. The result is a more efficient and cost-effective process. Methodology Engineers can apply process simulation and digital twin technologies to monitor plant data, tune simulation models, calibrate them to match actual plant behaviour, and predict polymer properties using the following methodology. In this study, the software’s historian and metering functions were used to connect real-time operational data to the polymer
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PTQ Q1 2026
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