One of the key advances of the digital revolution for the process industries is the ability to bring deep process knowledge into process operations and control.

Digital Applications generate value by combining current or historic plant data with the deep process knowledge contained in high-fidelity predictive models. These models may be purpose-developed, or the same models used during process design optimization.

Digital Applications are typically used for equipment and process health monitoring, soft-sensing, real-time optimization and “what-if” operations decision support, to create incremental daily value from a plant.

They execute in a robust, fail-safe applications platform within or connected to the plant automation system (PAS), exchanging data with the historian or distributed control system (DCS) directly.

Digital Applications for Operations

The digital process twins use the combination of prior knowledge in the predictive process model and the up-to-date information in the plant data to generate valuable new information:

Digital Operations built on the gPROMS digital Applications Platform

This information can include:

  • accurate reconciled heat and material balance values for the plant
  • real-time yield and product quality information
  • unmeasurable process KPIs
  • equipment internal information such as temperatures and compositions
  • the degree of divergence from optimal operation
  • the setpoints required to achieve optimal operation for the current conditions
  • how to operate optimally in order to maximize production while meeting shutdown schedules
  • time until end-of-run / decoking / shutdown
  • and many more …

Digital Application types

The following are typical Digital Applications:

Long-term equipment and process health monitoring.
Long-term equipment and process health monitoring. This application uses 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 – using the predictive plant model combined with current and historic plant data.

This provides valuable information on the current plant state to Operations and Maintenance. It is also used to update a predictive master model that can then be used in digital process twins for many other applications:

Real-time soft sensing.
Real-time soft sensing. Here the real-time plant data is used with the high-fidelity model 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.
Real-time optimization.
Real-time optimization. The model is used to determine set points for economically optimal operation taking account of 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.
Operations decision support tools,
Operations decision support tools, The up-to-date plant model can be used for what-if analysis of steady-state and dynamic operating scenarios. This allows operators to visualise and understand the consequences of their decisions
Run-length prediction.
Run-length prediction. The up-to-date plant model can be used by operations 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.


Typical benefits of such applications include:

  • Better operations through enhanced, up-to-the-minute decision support information
  • Improved maintenance scheduling through run length prediction
  • Improved economics from real-time optimization
  • Improved asset integrity from better health monitoring.

Digital process operation technologies are already being used to drive next-level productivity enhancements to operations in the Chemicals & Petrochemicals, Oil & Gas, Refining, Pharmaceutical, Food & Beverage and Water industries.

Where are Digital Application used?

Digital Applications based on high-fidelity process models are increasingly being used to generate value throughout the process industries. Here are some examples:

Ethylene plants

Monitoring of coke buildup and yield monitoring in ethylene furnaces, and furnace section optimization based on the current state of furnaces.

Digital Applications for Olefins

Site-wide utilities optimization

Site-wide optimization is used to reduce emissions on an hourly basis, saving fuel costs. Typically, these are delivered as advisory systems: operators are presented with an indication of how far they are from optimal operation, in the form of potential savings that can be made through various actions that range from simple setpoint changes to boiler startup or shutdown. The operator can decide on the best course of action, taking into account their own knowledge of the current plant state and schedule. Operators are provide  with instructions to achieve optimal operation in each case.


Siemens Process Systems Engineering supplies a growing range of Digital Applications for monitoring, soft-sensing, real-time optimization and operation decision support based on high-fidelity models. Applications are implemented by SPSE as turnkey solutions using the gPROMS Digital Applications Platform (gDAP).