Sootblowing Optimization

nD Frameworks

Optimization

Optimization

nDimensional technology is used to help electric power generation plants increase fuel efficiency and reduce downtime by optimizing boiler cleaning activities. Firing coal in a steam generating unit results in the buildup of soot and slag on the heat transfer surfaces which affect the overall performance of the boiler. Boiler cleaning is done by scheduling high-velocity water streams through lances or cannons at the boiler walls to remove the soot and slag. Traditionally, this scheduling is done uniformly across all walls, regardless of where the slag buildup is located. Too much or too little cleaning in an area can lead to a loss of efficiency or operating outages.

Solution: To ensure the proper balance of cleaning, prediction models are being used. The prediction models estimate the heat duty and cleanliness factor of each heat transfer surface in the boiler (as calculated using a series of performance calculations) and optimization models determine the best sootblower action in real-time. The goal of the optimization scheme is to improve boiler performance while guaranteeing that constraints on blower activation are not violated. On average, across the dozens of optimizers installed, overall sootblower activations have been reduced by 20%, with a steam temperature improvement of 7 degrees, resulting in an overall fuel efficiency improvement of 1%. To ensure the proper fuel-to-air mixture, prediction models are being used to estimate the NOx and CO levels at the stack (as measured by upstream sensors) along with the overall fuel efficiency (a detailed engineering calculation) based on the burner and air port settings. Optimization models then determine the best settings to lower NOx and increase fuel efficiency within CO constraints. On average, across the hundreds of combustion optimizer already installed, NOx emissions have been lowered by 10-25% and fuel efficiency has increased by 0.5-0.75%.