Siemens Steps Further Into Model-Based Operations

Aug. 14, 2018
Siemens and PSE sign a long-term collaboration agreement around model-based offerings.

Siemens AG and Process Systems Enterprise (PSE), a company specializing in advanced process modelling, sign a long-term collaboration agreement to bring PSE’s gPROMS APM technology to Siemens automation and digitalization offerings for the process industries. Under the agreement, the companies are bringing to market a new set of offerings for long-term equipment and health monitoring, soft-sensing, prediction of future process performance, real-time optimization and operator training incorporating high-fidelity models. The offerings are all based on the combination of process models that embody deep process knowledge with real-time as well as historical plant data.

Typical customer 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; and improved asset integrity from better health monitoring, according to Siemens. The technologies are aimed at enhancing operations in the chemicals and petrochemicals, oil and gas, refining, pharmaceutical, food and beverage, and water industries. Future developments will see similar model-based approaches integrated over the whole lifecycle of a process plant, according to the company.

“Siemens has already made integrated engineering a reality; by collaborating with PSE, we are taking a further step into model-based operations with two very complementary sets of technologies. This is digitalization at its best,” says Eckard Eberle, CEO of the Process Automation Business Unit.

“The combination of high-fidelity predictive models and real-time data is enormously powerful. This is a time of extraordinary opportunities for the process industries, made possible by the culmination of many years of development in advanced modelling and in the enabling computer science and mathematics,” says Costas Pantelides, MD of PSE. “There is an increasingly compelling case for capturing deep process knowledge in the form of predictive models, which can come naturally from R&D and engineering design activities, then using these within a digitalization framework to generate value at every step.”

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