COMBUSTION MODELING AND OPTIMIZATION

Transcription

1 COMBUSTION MODELING AND OPTIMIZATION Application of Computational Fluid Dynamics and Systematic Optimization in Emission Reduction of Biomass Fluidized Bed Boiler Ari Saario, Antti Oksanen Tampere University of Technology, Finland Matti Ylitalo, Juha Roppo Metso Power Oy, Finland Acknowledgments: Lars Kjäldman - VTT, Kaisa Miettinen - HKKK, Juhani Koski - TTY

2 Introduction

3 Fluidized Bed Boiler Modeling Bubbling fluidized bed boiler burning biomass studied using computational fluid dynamics (CFD) More stringent nitric oxide emission limits new and improved design tools required Nitric oxide reduction by ammonia injection (SNCR process): two-step global reaction mechanism NH 3 + O 2 NO + H 2 O H 2 (1) NH 3 + NO N 2 + H 2 O H 2 (2) too high temperature more NO produced too low temperature NH 3 passes unreacted

4 Grid dependency and submodels

5 Unstructured (tetrahedral) grids Unstructured grid I ( cells) Unstructured grid II ( cells) Unstructured grid III ( cells) Unstructured grid IV ( cells) Dense unstructured grid ( cells) Very dense structured grid ( cells) 0.12 u / u r / x

6 Structured grids - local grid refinement 4 x Baseline grid ( cells) Once refined grid ( cells) Twice refined grid ( cells) Three times refined grid ( cells) Dense grid ( cells) Very dense grid ( cells) k / u r / x

7 Temperature distribution Left C Left B Left A Front Left 5 4 Width (m) 3 2 Front Right Depth (m) Right C Right B Right A

8 Optimization

9 Optimization problem Find x = [x 1, x 2,..., x n ] T which minimizes f(x) = [f 1 (x), f 2 (x),..., f k (x)] T subject to g j (x) 0 j = 1, 2,..., p h j (x) = 0, j = 1, 2,..., q x is design variable vector f(x) is objective function vector g j (x) and h j (x) are inequality and equality constraints

10 Optimization - CFD interaction Design Variables OPTIMIZATION ALGORITHM CFD Objective Function

11 Why optimize? Exhaustive search over the whole design space: Assume nine design variables (n = 9) and 64 design points per each variable (q i = 64) Number of required function evaluations, N: N = n i=1 q i = 1.8e+16 Assume further that one CFD evaluation takes one hour we need 2.1e+12 years to complete the optimization!

12 Optimization methods A great number of possibilities exist Genetic algorithm: population-based method survival of the fittest: selection - crossover - mutation no need for gradients not dependent on starting point (global method) requires many function evaluations Powell s method: point by point iterative method no need for gradients dependent on starting point (local method)

13 Genetic algorithm principle START STOP g = 0 YES Generate initial population P(g) with s individuals [s = 10] Evaluate fitness of each individual in P(g) [CFD] Replace j worst individuals in P(g) by j best individuals in P(g-1) [j = 1] Stopping criterion met? [g > 48] NO g = g + 1 Apply mutation with probability p [p = 0.1] m m Apply crossover with probability pc [uniform crossover, p = 0.9] c Apply selection [tournament of two individuals]

14 Minimum NO emission Nitric oxide concentration (ppm vol ) Baseline injection case Pop. 10 (a) Pop. 10 (b) Pop. 10 (c) Pop. 10 (d) Pop. 10 (e) Pop CFD evaluations

15 Pareto points in objective space Nitric oxide concentration (ppm vol ) Ammonia concentration (ppm ) vol Baseline case SOO (NO) SOO (NH3) MOO

16 Optimum design variables NH 3 concentration (vol %) Rear Front Left Left A Left B Left C Injection Front Right Right B Right A Right C

17 Conclusions

18 Conclusions Careful CFD modeling study may be able to give information about trends in boiler emissions Fast-growing computing power use of systematic optimization with CFD offers great possibilities in near future. Combination of CFD and optimization gives high quality results in efficient manner.

19 Conclusions - CFD Modeling Challenges / problems in fluidized bed boiler CFD modeling Description of bubbling bed and fuel feed Uncertainties in CFD model boundary conditions Coarse computational grids Shortage of experimental data for model validation Nitrogen chemistry and turbulence - chemistry interaction modeling Radiative heat transfer modeling

20 Conclusions - Optimization Challenges / problems in optimization Great number of CFD evaluations required Definition of objectives and design variables Finding best optimization method and its parameters for specific case Multi-objective optimization Constrained optimization

21 Selected publications SAARIO, A., OKSANEN, A. YLITALO, M. (2007). Application of Computational Fluid Dynamics and Multi-Objective Optimization in Design of Low Emission Combustion Equipment. 15 th IFRF Member s Conference, Pisa, Italy. to appear. SAARIO, A., OKSANEN, A. (2007). Detailed Study on the Effect of Computational Grid in Industrial-Scale Boiler Modeling. Submitted. SAARIO, A., OKSANEN, A., YLITALO, M. (2006). Combination of Genetic Algorithm and Computational Fluid Dynamics in Combustion Process Emission Minimisation. Combustion Theory and Modelling, Vol. 10, pages SAARIO, A., OKSANEN, A., YLITALO, M. (2006). Nitric Oxide Emission Modeling in Bubbling Fluidized Bed Furnace for Biomass. International Journal on Energy for a Clean Environment, Vol. 7, pages SAARIO, A., OKSANEN, A., MAKIRANTA, R., YLITALO, M. (2005). Optimization of Selective Non Catalytic Reduction Process Using Computational Fluid Dynamics and Genetic Algorithms. Swedish - Finnish Flame Days, Boras, Sweden.