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