PROJECT LEAD: Texas A&M Engineering Experiment Station (TEES)

PARTNERS: Praxair, Process Systems Enterprise, AspenTech, OSISoft, Rensselaer Polytechnic Institute, University of Texas Austin

PROBLEM STATEMENT: An Air Separation Unit (ASU) is a complex and energy intensive process for chemical industries. Sub-optimal performance of these processes result in a loss of energy efficiency. For Praxair in the US, each 1% in operating efficiency is worth about $10 M/yr.

PROJECT GOAL: The goal of this project is to develop a number of SM Platform™ ready tools and deploy them on Praxair’s commercial Air Separation Unit for improving energy efficiency.

TECHNICAL APPROACH: 

  • Create data acquisition tools, asset templates and predictive tools for the air separation units and demonstrate their application in an integrated environment.

KEY TASKS AND MILESTONES:

  • Develop data model templates for the air separation unit for data acquisition and modeling & control environments
  • Implement a coupled on-premise & cloud historian system to collect plant data for analysis in the cloud 
  • Develop steady state, dynamic, control oriented, planning and scheduling models for ASU
  • Develop data models for individual assets for integration into a Remote Asset Management workflow 
  • Develop analysis tools for the efficient monitoring and operation of the Air Separation Unit
  • Validate analysis tools through field testing on an air separation unit at Praxair 

     

    POTENTIAL IMPACT

    Increase in operating efficiencies of Air Separation Units estimated at $10M/yr. for one large manufacturer, with potentially similar impact to other manufacturers with similar ASUs Contributes to CESMII’s energy reduction and energy productivity goals

    BENEFITS: 

    • Data templates, models, analytics tools available to other members with similar processes through the SM Platform™
    • Contribution to the SM Platform™ core technologies (data acquisition, contextualization and apps)
    Member % Cost Share CESMII % Cost Share Duration
    42% 58% 24 months