Consistent, end-to-end data models for the entire lifecycleAfter acquiring PSE in October 2019, Siemens extends its integrated digitalization portfolio for the process industry with the global cutting-edge software provider PSE for advanced process modelling.
Experiences to date and, above all, success show that model-based technologies are the key to simulations, process optimization and even to reliable predictions. Equation-oriented process modeling is the specialty of gPROMS ProcessBuilder, a modeling and solution environment from Process Systems Enterprise (PSE, London), a Siemens business since October 2019. The consistent application of the three digital twins for product, production and performance over the entire life cycle of a process plant maximizes the economic benefit. These benefits can be increased even further if the simulation models are not created new for every step but are coupled or transferred to each other.Eckard Eberle, CEO of the Process Automation Business Unit, Siemens AG
Ensuring a consistent data flow in the plantWorking with PSE, Siemens makes 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 SIMATIC PCS 7/SIMIT operator training to make it even more realistic. SIMIT can be used as a virtual training environment (Operator Training System – OTS) in order to already train the plant operating team before commissioning – on real operator displays and with original control system. To minimize response times, the plant personnel can thus be trained for normal operation or deviations from normal operation up to worst-case scenarios. In the case of the OTS as well, the level of detail plays a major role. A “high-fidelity OTS“, for example, is required to obtain a highly detailed process model. Instead of creating it from scratch as was the case so far, a so-called co-simulation – with coupling of the gPROMS to SIMIT – is now possible in the context of the Digital Twin.
From integrated engineering to integrated operationsBuilding up a store of consistent data is an important precondition for a model-based process plant. Integrating gPROMS, SIMIT, COMOS and SIMATIC PCS 7 throughout will permit a consistent data flow for the entire lifecycle of your plant.
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.