gPROMS – Digital Process Twin technology
Process organizations need to make complex design and operating decisions daily to generate sustainable value. Siemens gPROMS models capture deep process knowledge in the form of high-fidelity predictive process models that can then be used to explore the process and product decision space rapidly and effectively.
Use drag & drop flowsheeting to create models from the process industry’s most sophisticated and widest-ranging set of model libraries: pharmaceutical APIs, catalytic reaction, polymerization, adsorption electrochemical reactors and all the standard process operations, such as heat exchange and distillation. Or build your own model libraries using gPROMS’ industry-leading custom modelling capabilities.
Use established, state-of-the-art validation techniques to calibrate models against laboratory, pilot and operating data- using advanced parameter estimation techniques to ensure models are predictive over a wide range of conditions – allowing you to explore a wide decision space with confidence.
Analyze the system using steady-state and dynamic simulation, or deploy global system analysis to explore the process decision space rapidly and effectively by systematically assessing the effect of variations and uncertainty on key process indicators (KPIs).
Key applications: Helping the process industries address their most pressing challenges
Next Generation Modelling Tools Across the Process LifecycleProduct Portfolio
Siemens provides a set of advanced process modelling tools and environments that cover the entire digital process lifecycle, from R&D through Engineering Design to Operations and Manufacturing.
Online applications for monitoring, soft-sensing, optimizing and general operations support of process plants, based on high-fidelity process models.
Use the plant digital twin combined with real-time and historic run data to determine the values of key parameters subject to drift over the operation of the plant – for example, catalyst activation state in a catalytic reactor, or amount of coking in a furnace coil. This provides essential information for optimization of operations and maintenance planning, as well as early warnings of potential problems.
Use the plant digital twin combined with real-time plant data to provide reliable current values of KPIs such as yields, conversion/severity, coking rate, as well as equipment internal variables that cannot normally be measured. This provides valuable information for real-time monitoring of operation, or use in enhanced process control.
Use the plant digital twin to determine set points for economically optimal operation taking account current plant state, feedstock availability, product demands and equipment and processing plant constraints. This makes it possible to maximize the economic performance of the plant from hour to hour, and react rapidly to disturbances and upsets.
Use the up-to-date plant digital twin for what-if analysis of steady-state and dynamic operating scenarios. This allows operators to visualise and understand the consequences of their decisions
Use the up-to-date plant digital twin to determine the expected end-of-run date under different operational scenarios. This can be used to improve maintenance scheduling, or to determine the most profitable operation mode for the remainder of the run.
Industrial Strength, academic price
We support chemical engineering academic research and education by offering long term licenses of gPROMS platform products with full functionality at a small fraction of the commercial price. Over 200 institutions already use gPROMS to achieve rapid results across a wide range of research topics.