SAS Energy Cost Optimization

作者 SAS Institute Inc.

SAS ECO helps to achieve a reduction of energy consumption, CO2 emissions and CO2 certificate costs.

SAS award-winning software and consulting solution helps heavy industries achieve a reduction of specific energy consumption, CO2 emissions and CO2 certificate costs.

The SAS solution, built on Microsoft Azure is designed to provide frontline engineers with the missing insights needed to impact key process parameters influencing an energy intensive process, through:

  • Analytics enabled drilldown – Provide a drilldown from corporate to plant level, to identify the plants, productions lines or pieces of equipment that have the largest contribution to the overall energy consumption and CO2 emissions.
  • Automated Explainers: A relationship analyzer for energy consumed and process parameters.
  • Shift Explainer: Identify what influencing factors cause a high or low energy consumption of individual shifts or batches to assist day-to-day tuning of the production process.
  • Setpoint Optimizer: Based on a digital twin and AI models, SAS offers a recommendation engine for operators on the exact setpoints they should apply to the process to minimize energy consumption while maintaining quality and throughput.

The SAS Energy Cost Optimization solution:

  • Combines IoT and Process data, such as: environmental, MES, PCS/PLC/ SCADA and LIMS data.
  • Deploys advanced analytics and machine learning models, such as: Predicted Specific Energy Consumption, Variable Impacts and Mathematical Optimization.
  • Includes prebuilt energy modules: Automated explainers, Golden Batch analyzer, Setpoint Optimizer.
  • Provides operators with prebuilt visualizations and dashboards, essential for operators to fine tune the SCADA setpoint parameters

SAS Energy Cost Optimization solution is proven to have driven at least 7% reduction in specific energy consumption at production sites, reduce client efforts to tune a plant from months to just days and deliver new insights previously unknown to production engineers and operators.