Optimizing Boilers for NOx Control and Fuel Savings Three Utility Examples Using Sequential Process Optimization

In recent years, awareness has grown throughout industry of the need to utilize process modeling and optimization tools in design, development and production. While useful tools have been around for many years, they are not widely applied, especially in a production environment. These tools include empirical approaches such as various statistical design-of-experiment methods, evolutionary operations and the Taguchi Method. Spurred by Japanese competition, American companies began expanding the use of these methods in the early eighties to achieve notable quality improvements in the automotive and electronics industries. However, effective applications were mostly limited to design and development functions. In production, these methods have severe limitations that have inhibited their use. They introduce too much risk, do not deal with variables which are not being controlled, are project oriented and require substantial expertise that is often lacking. They tend to be time-consuming, ineffective and expensive to apply in production. One well-known and popular tool, statistical process control (SPC), offers a way to achieve greater quality consistency in production processes. However, SPC does not help with the fundamental needs to model processes, understand relationships and optimize settings of control parameters that contribute most to better process performance.