Comparison of data-based methods for monitoring increasing air leakages into oxyfuel power plants

Transcription

1 Faculty of Mechanical Engineering Institute of Power Engineering Chair of Thermal Power Machinery and Plants Comparison of data-based methods for monitoring increasing air leakages into oxyfuel power plants R. Preusche a, S. Böhlmann b, H. Ecke c, U. Gampe a a, Institute of Power Engineering, Germany b, Institute of Process Engineering and Environmental Technology, Germany c Vattenfall Research and Development AB, Sweden 2 nd Oxyfuel Combustion Conference September 14, 2011 (Yeppoon, Australia)

2 The negative effects of leakage air specific energy consumption in kwh / t CO2 CO 2 recovery rate 88 % CO 2 recovery rate 90 % CO 2 recovery rate 92 % CO 2 recovery rate 94 % Air leakage results in: - Increasing aux power - Decreasing CO 2 recovery rate - Changing thermodynamics in GPU CO 2 concentration in flue gas before GPU in % (vol, dry) Source: Ritter et al. (2009), Energetische Bewertungen einer CO 2 -Kompressions- und Reinigungsanlage für den Oxyfuel-Prozess am Beispiel einer Demonstrationsanlage. In: Kraftwerkstechnik. Sichere und nachhaltige Energieversorgung, eds Beckman, M., Hurtado, A., TK Verlag, Neuruppin, pp High CO 2 concentration in flue gas is crucial for an efficient plant operation! Data-based monitoring of air leakages in oxyfuel plants Slide 2 of 11

3 m Characteristic air leakage rates leakage air CO 2 concentration in flue gas in % (vol, dry) 95 / m flue gas after boiler ) Vattenfall s Oxyfuel Pilot Plant Boundary conditions: O 2 purity: 99.5 %(vol) O 2 concentration after boiler: 3.5 %(vol, wet) O 2 concentration in oxidizer: 27 %(vol, wet) Fuel: predried Lusatian lignite Conv. PPs (new) m / m air leakage rate α in % Aging Plants ( leakage air flue gas after boiler ) Data-based monitoring of air leakages in oxyfuel plants Slide 3 of 11

4 Experimental simulation of air leakages in OxPP Coal CO 2 **, H 2 O**, O 2 **, SO 2 ** Secondary Recirculation O 2 * Oxygen Recirc Fan Primary Recirculation Boiler ESP ID Fan 1 FGD ID Fan 2 FGC GPU CO 2 *, H 2 O*, O 2 *, SO 2 * CO 2 *, O 2 *, SO 2 * CO 2 *, O 2 *, SO 2 * Location of artificial leakage * Measurements used in Standard Instrumentation Case and Advanced Instrumentation Case ** Measurements used in Advanced Instrumentation Case only - Leakage quantity: in each location leakage rate 1.5% 2.0% and 1.5% 2.5% - Analysis of influence of leakages on operational data changing correlations Data-based monitoring of air leakages in oxyfuel plants Slide 4 of 11

5 Training data set and investigated methods Training data set - Time series of 11 variables (15 in advanced instrumentation case) - 10-second mean values - Transient operation periods were removed Investigated data-based methods - Discriminant Analysis - Fuzzy Pattern Classification - Neural Network Classification Data-based monitoring of air leakages in oxyfuel plants Slide 5 of 11

6 Results Identification of the importance of variables for classification reclassification rate in % Discriminant Analysis and Fuzzy Pattern Classification can identify variables that do not contribute to discrimination 65 Discriminant Analysis Fuzzy Pattern Classification number of variables used for modeling Data-based monitoring of air leakages in oxyfuel plants Slide 6 of 11

7 Results Dimensionality reduction with Discriminant Analysis leak in recirculation leak in FGD leak in ESP normal operating condition leak in boiler Data-based monitoring of air leakages in oxyfuel plants Slide 7 of 12

8 Results Leakage source prediction FGD Flue Gas Desulphurization RECIRC Recirculation Duct α = 2.6 % Real Plant Condition Prediction - CASE 1 Prediction - CASE 2 Real plant condition Prediction with standard instrumentation Prediction with advanced instrumentation ESP Electrostatic Precipitator BOILER Boiler NOC Normal Operating Condition α = 2.2 % UOC Unknown Operating Condition α = 1.5 % 13:09 13:29 13:49 14:09 14:29 time Data-based monitoring of air leakages in oxyfuel plants Slide 8 of 11

9 Model-based generation of training data Data-based models are plant specific New training data required for every plant Air leakage experiments not appropriate for big commercial plants How can we generate training data w/o experiments? Simple stationary thermodynamic plant models Virtual leakage tests Superimposing Gaussian distributed noise Training data set First results show possibility of this approach Data-based monitoring of air leakages in oxyfuel plants Slide 9 of 11

10 Concluding Remarks Data-based methods available to identify and localize air leakages in oxyfuel plants DA Dimensionality reduction, identification of important variables FPC identification of important variables, drift recognition via sympathy vector NNC easy to train Training data can be generated using simple plant models Data-based monitoring of air leakages in oxyfuel plants Slide 10 of 11

11 Further information R. Preusche, S. Böhlmann, H. Ecke, U. Gampe (2011). Comparison of databased methods for monitoring of air leakages into oxyfuel power plants, International Journal of Greenhouse Gas Control 5, Supplement 1, pp. S186- S193 Acknowledgement Vattenfall Europe Generation AG for enabling experiments in OxPP and authorizing publication Vattenfall Europe Technology Research GmbH for financial support Vattenfall Oxyfuel Pilot Plant team Data-based monitoring of air leakages in oxyfuel plants Slide 11 of 11

12 Dimensionality reduction of discriminant analysis leak in ESP normal operating condition leak in FGD leak in boiler leak in recirculation Data-based monitoring of air leakages in oxyfuel plants Back-up slide 1 of 4

13 m Characteristic air leakage rates leakage air CO 2 concentration in flue gas in % (vol, dry) 95 / m flue gas after boiler ) Tigges et al. (Energy Procedia 4 (2011) Vattenfall s Oxyfuel Pilot Plant Conv. PPs (new) Boundary conditions: O 2 purity: 99.5 %(vol) O 2 concentration after boiler: 3.5 %(vol, wet) O 2 concentration in oxidizer: 27 %(vol, wet) Fuel: predried Lusatian lignite Flue gas composition at GPU inlet for α = 0 %: CO 2 = %(vol, dry) O 2 = 5.22 %(vol, dry) N 2 = 0.83 %(vol, dry) Ar = 0.23 %(vol, dry) m / m air leakage rate α in % Aging Plants ( leakage air flue gas after boiler ) Data-based monitoring of air leakages in oxyfuel plants Back-up slide 2 of 4

14 Plant areas prone to leakages Example for Vattenfall s Oxyfuel Pilot Plant flue gas gauge pressure in mbar recirculation duct recirc fan inlet recirc fan outlet recirc preheater outlet O 2 mixer outlet furnace ESP inlet ESP outlet ID fan 1 outlet FGD precooler inlet FGD precooler inlet flue gas path FGD absorber outlet ID fan 2 outlet FGC outlet Data-based monitoring of air leakages in oxyfuel plants Back-up slide 3 of 4

15 Standard score (z-score) z = x µ σ µ mean σ standard deviation Data-based monitoring of air leakages in oxyfuel plants Source of chart: wikipedia.co Back-up slide m 4 of 4