COAL MILL AND COMBUSTION OPTIMIZATION ON A ONCE-THROUGH, SUPERCRITICAL BOILER WITH MULTIVARIABLE PREDICTIVE CONTROL

摘要

This paper describes how multivariable predictive control techniques have been applied to optimize and improve the dynamic control of a 600 MW once-through, supercritical boiler at Dayton Power and Light's Stuart Station. Power plants have experienced significant heat rate improvement, NO{sub}x reduction and operational stability on a wide range of boilers including coal, gas and oil-fired through application of multivariable predictive control techniques. A variety of boiler sizes and configurations have shown high return on investments. This successful project will be discussed as well as lessons learned during the implementation of this technology. Some of the issues that this paper will address are: project development and justification, project implementation, auditing results and sustaining benefits. The variety of control and optimization issues that can be addressed and benefits derived from multivariable predictive control include NO{sub}x minimization, heat rate improvement, ramp rate improvement, pulverizer optimization, smart soot blowing, improved steam temperature control. Details of the Dayton Power and Light implementation will be given including some of the obstacles overcome in concluding a successful project. The role of controlling coal mills will be emphasized. The paper will include suggestions for maintaining the control and optimization solution to sustain maximum benefits. Following the success of the initial installation on Unit #3, further projects are underway to apply the same advanced process control and optimization to Units #1, 2 and 4 (also 600MW each).