Rockwell Automation has initiated a project designed to optimize manufacturing assets at a U.S. Army-owned, contractor-operated, metal-parts manufacturing plant in Scranton, Pa.
The work is one of two test-bed efforts in the $10 million Smart Manufacturing Leadership Consortium (SMLC) project to develop an open smart-manufacturing (SM) platform in the U.S.
The U.S. Department of Energy-supported project intends to show how the SM platform of SMLC can be used for real-time management of energy across many small, medium, and large U.S. manufacturing companies.
The technologies developed by the consortium are expected to help improve energy productivity, reduce carbon dioxide, and boost production output.
Rockwell Automation is one of the principal members of the SMLC, along with University of Texas – Austin, University of California – Los Angeles, Emerson, Honeywell Automation and Control Systems, Schneider Electric, Praxair, and others.
The scope of Rockwell Automation on the project includes the extraction of energy data from existing automation systems, the enhancement of asset instrumentation, and the deployment of additional automation control hardware and software, including engineering services, to support the DOE project.
Tight collaboration with other consortium members is expected to help ensure the SM platform is compatible with multiple process-control and energy-management systems, as well as other manufacturing applications or “apps.”
“For the DOE-funded test bed, we will extract previously unavailable energy data from furnaces and other operational equipment utilizing the existing automation system enabled by an open communications protocol,” commented Phil Kaufman, business manager for Industrial Energy Management at Rockwell Automation. “The energy data correlated with production data provide the opportunity for real-time energy reduction.”
Rockwell Automation will also supply an innovative energy-aware control system coupled with energy-optimization functionality around the furnace test bed to provide sufficient measurements to validate a high-fidelity model of the combustion process on the SM platform.
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