Engineering software speeds plant modernisation using artificial intelligence

Emerson’s REVAMP software solution uses cloud computing and artificial intelligence (AI) to automate up to 70% of system configuration, reduce errors and manual conversion work, and slash capital costs by up to 15%.

“Modernisation projects too often surprise teams late in the process with cumbersome, unanticipated work and errors from manual conversion,” said Claudio Fayad, vice president of technology for Emerson’s process systems and solutions business. “Emerson’s REVAMP helps project engineering teams modernise their systems more easily, on time and within budget, while also minimising errors and disruptions to production.”

Organisations seeking to modernise control and safety systems often start with decades-old code that must be transitioned to current software. Manually converting and documenting this code is an arduous process that dramatically increases the time and capital requirements for these projects.

Emerson’s REVAMP advanced software combines an extensive knowledge base from similar modernisation projects with Emerson’s experience library to develop continuously updating AI models. Each modernised control system feeds back into the REVAMP software, creating learning algorithms that perpetually get smarter and faster at converting legacy code.

The applied AI in REVAMP informs project teams of the engineering requirements before migration projects even begin, making planning easy. The AI engine analyses native files from the existing distributed control systems, safety instrumented systems or programmable logic controller back-ups while using a global library of thousands of successful projects to sort, select and automate engineering tasks. The modernisation project is automatically fully documented, and significant portions can be generated in the DeltaV control system, enabling the latest capabilities and using modern standards.

Emerson project teams around the world have access to the most recent functionalities and libraries of this secure, cloud-native tool. And with embedded machine learning, the libraries grow and improve as projects become more efficient over time.