AVEVA (Schneider Electric) Case Studies Optimization System Increases Profitability of Southern Mississippi Electric Power Association

	Optimization System Increases Profitability of Southern Mississippi Electric Power Association AVEVA (Schneider Electric)

Optimization System Increases Profitability of Southern Mississippi Electric Power Association

AVEVA (Schneider Electric)

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Utilities
Applicable Functions	Discrete Manufacturing Maintenance
Use Cases	Energy Management System Predictive Maintenance
Services	System Integration
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Utilities

Applicable Functions

Discrete Manufacturing

Maintenance

Use Cases

Energy Management System

Predictive Maintenance

Services

System Integration

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.

About 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.

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.

Impact #1	Improved heat rate and boiler efficiency while maintaining low NOx emissions
Impact #2	Reduced dry gas losses and lower loss-on-ignition (LOI)
Impact #3	Increased maximum generation capability, particularly for lower grade coal

Impact #1

Improved heat rate and boiler efficiency while maintaining low NOx emissions

Impact #2

Reduced dry gas losses and lower loss-on-ignition (LOI)

Impact #3

Increased maximum generation capability, particularly for lower grade coal

Benefit #1	Expected heat rate improvements of 1.5%
Benefit #2	Project payback of less than one year

Benefit #1

Expected heat rate improvements of 1.5%

Benefit #2

Project payback of less than one year

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Overview

Optimization System Increases Profitability of Southern Mississippi Electric Power Association

Operational Impact

Quantitative Benefit