Examples of Typical Applications and Successes

Optimize Operations (Increase Flame Temperatures) Problem: Optimization of a coal-burning 300 MW multi-cyclone unit for consistent high flame temperatures; increase the flame temperatures to avoid forming slag, burning fuel oil, etc. Solution: Analyze 12 months of 3-minute historical data, using StatSoft's specialized data-driven (data-mining) methodologies; optimize settings for Stoichiometric Ratios (S.R.), Coal Flows, Primary Air, Tertiary Air, Split Secondary Air Damper flows, etc. Results: StatSoft identified optimized control parameter settings (S.R., Coal Flow, Primary Air, Tertairy Air, and Split Secondary Air damper flows); after dialing in StatSoft optimized settings at 8:40 am, flame temperatures immediately responded (strongly), resulting in more stable and higher flame temperatures (cleaner combustion). Note: The flame temperature at some of the cyclones had been abnormally and critically low for several days, requiring the burning of fuel oil (at a substantial cost), and intermittent shut-downs; flame temperatures “recovered” almost immediately after SatSoft's optimized control settings were applied.
Improve Efficiency and Performance of Your Equipment Problem: Optimize performance and reliability of ongoing operations; stabilize and improve flame temperatures of an 85 MW coal-burning multi-cyclone unit. Solution: Apply StatSoft's specialized data-driven (data-mining) methodologies to consistently increase flame temperatures under a variety of loads. Results: Flame temperatures increased consistently across all cyclone burners, leading to more reliable operations. Note: Even though the flame temperatures had been within satisfactory limits, StatSoft's settings improved temperatures further and significantly beyond historical values.
Stabilize Operations Problem: Optimization of a 400 MW coal-fired DRB-4Z burner for consistent and robust low-NOx operations; avoid excursions, expensive downtime. Solution: Apply StatSoft's specialized data-driven (data-mining) methodologies to optimize for both average lower NOx and less variabilitiy (control variability, then target process for better performance); optimized solution allows burner to operate consistently under normally occurring (external) variability in load, coal quality, etc. Results: Optimized settings for combinations of control parameters not only resulted in lower NOx, but also greater robustness, i.e., consistently lower NOx emssions with less variability (no excursions) were achieved over continued operations at low load.
Reduce emissions (NOx, CO) Problem: Optimization of a 400 MW coal-fired DRB-4Z burner for low-NOx operations under low load (50-175 MW). Solution: Apply StatSoft patented data driven (data mining) technologies to historical data; identify optimized parameter settings (changes to air flows); results consisted of a set of specific (achievable) input parameter ranges that could be implemented easily into the existing DCS (digital control system). Results: After optimization, NOx emissions under low-load operations were now comparable to NOx emissions under higher loads.

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