Optimization of biomass fired grate stoker systems

Transcription

1 Opimizaion of biomass fired grae soker sysems Sjaak van Loo, Rober van Kessel TNO Environmen Energy and Procesinnovaion EPRI/IEA Bioenergy Workshop Sal Lake Ciy, Uah, USA February 19, 2003

2 Inroducion The TNO mission: To apply echnological knowledge wih he aim of srenghening he innovaive power of indusry One field of experise is Thermal Conversion Technology Mahemaical models are used o opimize hermal conversion processes, like: Cemen process Biomass gasificaion Biomass combusion Municipal Solid Wase Combusion 2

3 Conens Dynamic model for grae soker sysems Model validaion Pracical applicaion Process conrol a solid fuel combusion plans Conclusions Opimizaion of biomass fired grae-soker sysems

4 Dynamic model for grae soker sysems (1) Dynamics of he furnace: Fuel layer Gas phase and he boiler secion are described Ineracion beween he gas phase and he fuel layer hrough radiaion Opimizaion of biomass fired grae-soker sysems

5 Dynamic model for grae soker sysems (2) The grae is divided ino many slices Mass and energy balances solved for every slice Conversion process: Propagaion of igniion fron Fuel layer divided in wo pars: Ho reacing par on op Cold fresh fuel a he boom Speed of propagaion fron is depending upon: Fuel composiion Amoun of combusion air Opimizaion of biomass fired grae-soker sysems

6 Dynamic model for grae soker sysems (3) Geomery is included: Co-curren par Counercurren par Ideally mixed par The co-curren and counercurren pars are divided ino many ideally mixed gas reacors wih CO, H2: mixed is burn Non-ideal mixing parameer Opimizaion of biomass fired grae-soker sysems

7 Model validaion (1) How o validae dynamic models? Sep response mehod Sysem idenificaion Sysem idenificaion: Experimenal modeling resuling in dynamic inpu-oupu relaions wihou any physical meaning (black-box modeling) Can be used for MIMO sysems and for closed-loop sysems. Opimizaion of biomass fired grae-soker sysems

8 Model validaion (2) Three sep procedure in sysem idenificaion: 1. Experimenal phase exciaion of process by user-defined signals + collecion of in- and oupu daa u() resp.. y(), =1 N: disurbances ex() disurbances ex() y() u() u() PROCESS y() y() r() CONTROLLER u() u() PROCESS y() OPEN LOOP 2. Esimaion of a model by minimizing he difference beween he measured oupu signals y() and predicion of hese N oupu signals y*(,parameers): 1 2 min ( y( ) y * (,parameers )) parameers N 3. Validaion of he esimaed model by using, for example, saisical echniques CLOSED LOOP = 1 Opimizaion of biomass fired grae-soker sysems

9 Model validaion (3) Sysem idenificaion resuls in a separaed process and disurbance model: whie noise signals DISTURBANCE MODEL fuel inle flow speed of grae primary air flow secondary air flow PROCESS MODEL disurbances seam O 2 Opimizaion of biomass fired grae-soker sysems

10 Model validaion (4) Validaion firs-principles model by means of he esimaed model: u() Black-box model y1() y2() Comparison of y1() and y2(): Enough Resemblence? Yes STOP Mehod: Whie-box model Adap parameers whie-box model and obain new response(s) y2() No Resuls: Opimizaion of biomass fired grae-soker sysems

11 Pracical applicaion (1) Operaional experiences: fly-ash deposiion o he furnace walls, leading o reduced hroughpu. high fly-ash raes due o a relaive small grae and high primary air flows. flue gas emperaures a inle second boiler pass fairly high, causing high emperaure corrosion. no reliable combusion of low-calorific fuels. Opimizaion of biomass fired grae-soker sysems

12 Pracical applicaion (2) Tile: Process analysis wih sysem idenificaion: Opimizaion of biomass fired grae-soker sysems

13 Pracical applicaion (3) Conclusions of process analysis: Influence primary air is bad due o excess air and wrong disribuion Grae speed is used as conrol variable Conrol concep is no in agreemen wih philosophy: primary air is he bes conrol variable Opimizaion of biomass fired grae-soker sysems

14 Pracical applicaion (4) Applicaion of TNO-simulaor: Adapaion of he model o he specific siuaion, including conrol conceps Conclusion from simulaions (1): Very shor fire, high hermal load on zones 1 and 2. The seam producion can be influenced mainly wih he wase flow and grae speed. The primary air has nearly no influence. Oxygen can be conrolled well wih he primary and secondary air flow. Opimizaion of biomass fired grae-soker sysems

15 Pracical applicaion (5) Conclusion from simulaions (2): Presen conrol concep is no ransparen, so adapaion is needed Primary air disribuion along he grae has o be changed A new conrol concep has been developed based upon experiences wih he simulaor Opimizaion of biomass fired grae-soker sysems

16 Pracical applicaion (6) Conrol objecives: Maximal conversion (combusion) of he fuel Maximal fuel hroughpu (highly flexible) Maximal seam producion/energy oupu Operae below bu as close as possible o he maximum levels imposed ou of life span consideraions Reduce he flucuaions in he process variables due o he variaion in fuel composiion: Reduce faigue due o variabiliy of hermal sress To be able o operae more closely o consrains imposed ou of life span consideraions To reduce load on pos-combusion conrol equipmen Opimizaion of biomass fired grae-soker sysems

17 Pracical applicaion (7) Resuls: Opimizaion of biomass fired grae-soker sysems

18 Conrol of solid fuel combusion plans (1) Performance assessmen convenional solid fuel combusion conrol sysems: Inefficien conrol due o: Complex (mulivariable) characer of he process Presence of muliple conflicing conrol objecives No being allowed o exceed cerain consrains (limis) Opimizaion of biomass fired grae-soker sysems

19 Conrol of solid fuel combusion plans (2) Model Predicive Conrol: Compuaion of manipulaed variables via solving on-line, a each sample insan, a mahemaical opimizaion problem Ingrediens: Dynamic Model Objecive funcion: reflecs conroller performance Consrains 0= 2 MPC algorihm cv Updae model Sensors 1 0 Deermine manipulaed variables for [ 0, 1 ], wih 1 > 0, ha saisfy consrains MODEL mv feasible se of manipulaed variables Obain conrolled variables for given manipulaed variables using model (predicion) se of conrolled variables Consrains on conrolled variables saisfied? feasible se of conrolled variables YES Compue conroller performance = f(conrolled variables,manipulaed variables) and deermine is opimaliy Conroller performance opimal? YES Measure variables a = 2 relevan for updaing he model Sar / Resar compuaion a = 0 Plan Acuaors NO cv NO feasible se of manipulaed variables Implemen manipulaed variables for [ 0, 2 ), wih 0 < 2 < 1, as sepoins acuaors = 0 Opimizaion of biomass fired grae-soker sysems 2 0

20 OPTICOMB Projec (1) Opimizaion and design of biomass combusion sysems Objecive: Increasing flexibiliy of fuels and reducing emissions Expeced resuls: Reducion of emissions Innovaive conrol conceps New grae sysem New furnace concep Opimizaion of biomass fired grae-soker sysems

21 OPTICOMB Projec (2) Sponsered by EU. Sar January Duraion 3.5 years Coordinaion: TNO Parners: Universiy of Graz Eindhoven Universiy Vyncke Universiy of Lisbon Naional Swedish Research Insiue Bio-energy plan Schijndel Opimizaion of biomass fired grae-soker sysems

22 Conclusions A dynamical firs principal model of grae firing sysems is available Validaion has shown ha he model is in good compliance wih pracical daa The model forms a good basis for improvemen of combusion sysems, in specific wih respec o he conrol concep Resuls have been demonsraed a biomass combusion sysems Developmen of MPC has been sared Opimizaion of biomass fired grae-soker sysems