Emerson is assisting customers in transitioning legacy technology to modern DeltaV automation architecture, which modernises and digitises operations. Emerson’s REVAMP advanced 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. 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.” says Claudio Fayad, vice president of technology for Emerson’s process systems and solutions business.
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 a difficult process that increases time and capital requirements for these projects.
Emerson’s REVAMP advanced software combines a large knowledge base from similar modernisation projects with Emerson’s experience library to develop continuously updating AI models. Each modernised control system feeds back into REVAMP software, creating learning algorithms that always get wise at converting legacy code.
The applied AI in REVAMP informs project teams of engineering requirements before migration projects even begin, making planning easy. The AI engine analyses native files from existing distributed control systems, safety instrumented systems or programmable logic controller backups while using a global library of thousands of projects to sort, select and automate engineering tasks. The modernisation project is automatically fully documented, and vital portions can be generated in DeltaV control system, enabling updated capabilities, and using modern standards.
Emerson project teams around world have access to recent functionalities and libraries of this secure, cloud-native tool. And with embedded machine learning, libraries grow and improve as projects become more capable over time.
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