It is possible to shorten the development time of new polymer grades, optimize reactor design, and maximize production volume while maintaining polymer properties.
A new feature has been added to gPROMS ProcessBuilder: "Optimization of Low-Density Polyethylene (LDPE) Autoclave Reactors." 【Advanced Polymer Thermodynamics】 The SAFT equation of state represents molecules as chains of different functional groups, accurately modeling branching and capturing precise polymer characteristics across the entire molecular weight distribution (MWD). This approach is ideal for polymer modeling. 【Complete MWD Modeling】 Detailed kinetic modeling considers the fundamental polymerization reaction steps in chemically initiated free radical polymerization. This model uses a fixed pivot method to predict the time evolution of the entire molecular weight distribution (MWD). 【CFD-Linked Multi-Zone Approach】 Advanced process modeling provides a means to understand the impact of changes in operating conditions on the shape of the MWD. 【PSE Consulting Services】 Consulting services provided by a specialized team ensure the swift and high-quality execution of projects.
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basic information
The faithful predictive model of the PSE low-density polyethylene (LDPE) autoclave reactor enables the following: ● Elimination of bottlenecks in continuous processes ● Maximization of production capacity while maintaining polymer properties ● Identification of operating conditions necessary for producing polymers with desired product characteristics ● Evaluation of the impact of changes within the reactor
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By using the PSE model, we were able to increase production by more than 10% while maintaining polymer properties. (U.S. polymer manufacturer)
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Company information
Founded in 1997 by the gPROMS development team at Imperial College London, PSE became part of the Siemens Group in 2019 and has been providing products and services in Japan as Siemens Corporation since 2023. gPROMS features a powerful equation-based computation engine that differs from traditional simulators, allowing for seamless handling of both steady-state and dynamic simulations. This enables the effective scaling up of models adjusted with experimental data from batch processing for the design and operational optimization of continuous processing plants. With gPROMS's excellent capabilities in custom modeling, parameter estimation, and optimization, it is possible to quickly build optimal manufacturing processes, resolve issues, and achieve optimization.
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