Creating added value:
An important feature of Industry 4.0 is the goal of providing consistent, end-to-end data models. Both for the supply chain and the entire lifecycle of a plant. Dealing with the process industry requires an in-depth understanding of both processes and procedures as well as extensive engineering know-how.
The partnership between Siemens and Process Systems Enterprise (PSE) is therefore a logical step towards the future, enabling you to optimize your plant’s productivity and performance using predictive models that faithfully reflect the real thing.
In cooperation with PSE, we are taking another step towards model-based plant operation using technologies that perfectly complement each other. This is digitalization in its truest sense.Eckard Eberle, CEO of the Process Automation Business Unit, Siemens AG
Ensuring a consistent data flow in the plantWorking with PSE, Siemens will make a new series of model-based solutions available. This technology covers the entire lifecycle of your plant. This means: long-term device and status monitoring, soft-sensing, prediction of future performance as well as real-time optimization and operator training based on predictive models using real-time and historical plant data. As the plant operator, you benefit from optimized production processes based on real-time information and an improved foundation for decision-making, for example, by being able to calculate the remaining service life.
Make the most of your plant’s full potential.Flexible production that is easy on resources as well as shorter times-to-market coupled with increased efficiency and consistently high quality are key demands in the process industry. The foundation for this is consistent data, which not only improves engineering workflows but also creates a foundation for accurate plant models. These support operators when it comes to planning and commissioning, from the process to the field and automation level, and monitoring.
Collate data and resolve any deviations
Mathematical estimation processes are combined with current and historical data to provide long-term status monitoring. This will record any anomalies in the core components and process parameters (catalyst activity, coke build-up in furnaces, heat transfer coefficients in heat exchangers, etc.) – and form the basis for optimized maintenance planning.
Evaluate real-time data and make use of decision-making aids
The process model is combined with real-time plant data in order to obtain accurate real-time values for KPIs that are difficult to measure. These can help the operator with decision-making, e.g. when conversion work or revenue checks are being performed.
Simulate operating scenarios and plan countermeasures
A model with a current plant status, based on long-term monitoring, is used to simulate future operating scenarios. Hypothetical operating scenarios can be simulated in this way, e.g. to accurately predict the end of the service life of a furnace or catalyst regeneration times for reactors.
Determine process values and improve performance
The model is used to determine the optimum computed values for process parameters within the plant. This can help maximize economic performance (i.e. profit) in real time.
Train employees and minimize risks
The high-precision process model in gPROMS can be used as part of the existing PCS 7/SIMIT operator training to make it even more realistic.