Reactor digital twins for operational excellence

Complex multitubular reactors are at the heart of many chemical processes, and their performance is often key to plant profitability. However, operators typically have little visibility of internal performance, relying instead on limited product stream instrumentation and after-the-event analysis. As the supply of raw materials and demand for products changes, operators adopt a conservative approach to ensure production. Without detailed insight into the reactor’s operation, money can be left on the table. Recent developments in digitalization are enabling chemicals producers to drive operational excellence and obtain a competitive edge. A key component of the approach is the creation of a digital twin that combines two key elements. The first is an advanced process model embodying deep process knowledge, including mass and energy balances, thermodynamics, reactions, heat and mass transfer relations, and correlations for plant “drift” phenomena such as fouling, degradation, coking, etc. The second is data from plant operations, augmented where appropriate with a small number of targeted experiments. The net result is a validated and highly predictive plant model. This presentation provides a) an overview of the laboratory-to-industrial reactor approach for model-based digital design and optimisation of fixed bed catalytic reactors, and b) a resultant online digital twin for operational decision support that combines reaction models with live data from the plant.