Process Optimization

The Energy Research Center (ERC) has worked extensively on process optimization projects for the fossil fuel-fired power generation industry.  The ERC pioneered combustion optimization development in the mid-1990s.  The ERC developed an approach to process optimization that includes sound know-how practices, artificial intelligence (AI) and on-line software for interfacing with the plant control system.  Examples of AI utilized by the ERC include neural network, support vector machine, fuzzy logic and genetic algorithms.  The ERC approach has been applied to combustion optimization for NOx emissions control, for improvements in net unit hear rate, for reduction in mercury and particulate emissions, for slagging mitigation and optimal sootblowing, and for SCR/SNCR cost of compliance optimization.

The research performed by the ERC in process optimization has led to the development of proprietary software technologies.  Boiler OP™ and IntelliCLEAN™ are two software packages licensed by the ERC for combustion optimization and sootblowing optimization, respectively.  The Boiler OP™ software can be deployed on an advisory mode or for closed-loop control of NOx emissions.  Approximately 30 Boiler OP™ projects have been completed in the US, Canada, Mexico, Europe and China, achieving NOx emissions reduction in the range from 5 to 40% and heat rate benefits as high as 1.0 Btu/kWh.  Some of the Boiler OP™ software applications cover the entire unit load range, boiler and firing system designs and fuels, and accounted for coal variability, gas cofiring, and operational and environmental constraints.  Boiler OP™ is well suited to help utility companies comply with the Work Practices provision of the EPA’s MATS rule that requires periodical boiler tuning, and emissions testing and optimization.  Currently, the ERC is working on an upgraded version of its combustion optimization software, Boiler OP Plus™.  The upgraded software incorporates new on-line measurement technology for coal quality, fly ash unburned carbon and boiler exit flue gas conditions for continuous biasing of the combustion control curves for real-time adaptive optimization.

Related Links:

• "Comprehensive Approach to Performance Improvement and Emissions Reduction on a 400MW Tangentially-Fired Boiler: Part 1 – Combustion Optimization"
• "Combustion Optimization: Part II – Results of Fields Studies"
• "Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms"