Complex multitubular reactors are at the heart of many chemical processes, and their performance is often key to plant profitability. However, operators often have little visibility of internal performance, relying instead on limited product stream instrumentation and after-the-event analysis.
gPROMS Reactor self-calibrating digital twins combine PSE’s advanced catalytic reaction models with live data from the plant to provide decision support information that helps operators deal with changing process conditions, feed supply and product demands.
Applications include real-time monitoring of catalyst deactivation, soft-sensing of reactor ‘internal’ variables, optimisation of operating conditions and provision of abnormal situation advice to operators.
How it works
High-fidelity predictive reactor models are constructed using gPROMS ProcessBuilder’s state-of-the art Advanced Model Library for Fixedbed Catalytic Reactors, with kinetics validated against laboratory or operating data:
|High-fidelity predictive reactor models are constructed using gPROMS ProcessBuilder’s state-of-the art Advanced Model Library for Fixed-bed Catalytic Reactors, with kinetics validated against laboratory or operating data. This provides reactor models that mimic actual operation to a high degree of accuracy for many different reactor configurations and catalyst forms. The reactor models is regularly updated to reflect the current operating state using plant data.|
|The resulting predictive model is implemented online using the gPROMS Digital Applications Platform (gDAP), inegrated with the plant historian or DCS database. Results are provided to operators via custom dashboards.|
|Typical dashboards for an olefins plant two-bed Acetylene Converter reactor are shown here,. These provide an overview of reactor key performance indicators (KPIs), soft-sensed values, distance from optimal operation for current conditions and so on.|
gPROMS Reactor is provided as a turnkey gPROMS Operational Excellence Solution. PSE works closely with your operations and engineering teams to create a high-fidelity predictive model and implement it online behind custom dashboards.