Optimization System Increases Profitability of Southern Mississippi Electric Power Association
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- SimSci-Esscor Connoisseur Online Optimization
Tech Stack
- Model Predictive Control
- Neural Network Based controller
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Environmental Impact Reduction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Utilities
Applicable Functions
- Maintenance
- Discrete Manufacturing
Use Cases
- Predictive Maintenance
- Energy Management System
Services
- System Integration
About The Customer
The Southern Mississippi Electric Power Association (SMEPA) is a power generation company based in Hattiesburg, Mississippi. They operate the R.D. Morrow Generating Station, which utilizes two parallel boiler-turbine units with a capacity of 204 MW at 2400 psig. The Riley Stoker Corporation manufactures each steam generator unit. Each has a turbo-furnace design with balanced draft operation, and is front and rear fired. Nominal steam conditions at HP turbine inlet are 2400 psig at 1005 DEGF and 1000 DEGF at the IP turbine inlet. Maximum continuous steam rate is 1,575,000 lhs/hr. The fuel is pulverized coal from three Riley doubleend ball tube mills fed by six Stock Gravimetric feeders.
The Challenge
The Southern Mississippi Electric Power Association (SMEPA) was facing a challenge of improving the heat rate and boiler efficiency while maintaining low NOx emissions at their R.D. Morrow Generating Station. The station utilizes two parallel boiler-turbine units with a capacity of 204 MW at 2400 psig. The fuel is pulverized coal from three Riley doubleend ball tube mills fed by six Stock Gravimetric feeders. The objective was to determine the most profitable operating point for the boiler and mills, as defined by a set of values for the controlled and manipulated variables in the process model.
The Solution
SMEPA implemented the Model Predictive and Neural Network Based controller, Connoisseur™ to improve efficiency at their Hattiesburg station. Both furnace and ball mill controls were optimized with coordinated multivariable control. Heat rate improvements were achieved through reduced dry gas losses and lower loss-on-ignition (LOI). In addition, the improved mill regulation increased maximum generation capability, particularly for lower grade coal. An Expert System Soot Blower Advisory supplements the heat rate benefits of applying Connoisseur™ by suggesting which blower to activate in order to maximize heat transfer area in the furnace. Ball mill optimization improves the grind in the mill, lowers LOI and improves the mill’s impact on the energy efficiency of the furnace.
Operational Impact
Quantitative Benefit
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