COMPARISON OF CONTROL STRATEGIES FOR WASTE HEAT RECOVERY ORGANIC RANKINE CYCLE SYSTEMS

Transcription

1 COMPARISON OF CONTROL STRATEGIES FOR WASTE HEAT RECOVERY ORGANIC RANKINE CYCLE SYSTEMS V. Lemort 1, A. Zoughaib 2, and S. Quoilin 1 1 Laboratoire de Thermodynamique Université de Liège 2 Centre d Energétique et Procédés Ecoles de Mines de Paris Sustainable Thermal Energy Management in the Process Industries International Conference (SusTEM2011) October 25 th 26 th, United Kingdom 1

2 Introduction Organic Rankine Cycle (ORC) : Small scale, lower temperature Application fields : Waste heat recovery, Solar sources, engine exhaust gases, biomass CHP, geothermy,... Selection of the working fluid and optimization of the working conditions are key issues in waste heat recovery applications Dynamic models are required to define proper control strategies under transient conditions 2

3 Table of contents Introduction Steady-state models Description Experimental validation Dynamic models Cycle optimization and control Model predictive control Conclusions 3

4 Volumetric expander model Heat transfers Friction Internal Leakage Pressure drops Built-in internal volume ratio Under and over-expansion 4

5 Plate Heat exchangers model: ORC cycle steady-state model Pump model: Cycle model:. (P r,su,p;v s,p). (P r,ex,p;v s,p) (X,η ) p s 5

6 Experimental validation 6

7 Introduction Steady-state models Table of contents Description Experimental validation Dynamic models Cycle optimization and control Model predictive control Conclusions 7

8 HEAT EXCHANGER MODEL Simplified heat transfer and pressure drop laws: Conservation of energy: Conservation of mass: Metal wall: 8

9 CYCLE MODEL Performance indicators: 9

10 Introduction Steady-state models Table of contents Description Experimental validation Dynamic models Cycle optimization and control Model predictive control Conclusions 10

11 CONTROL STRATEGIES Degrees of freedom: Controlled variables: 11

12 Optimal working conditions Main optimization parameter : evaporation temperature No Recuperator! 12

13 OPTIMAL EVAPORATION TEMPERATURE Static optimization of the cycle Tev,optim depends on the heat source/sink conditions: 13

14 CYCLE FEEDBACK CONTROL STRATEGIES 14

15 CYCLE FEEDBACK CONTROL STRATEGIES 15

16 Introduction Steady-state models Table of contents Description Experimental validation Dynamic models Cycle optimization and control Model predictive control Conclusions 16

17 Model predictive control Future action on the manipulated variable is based on a model. Balance between two different approaches: model evaluation of the manipulated variable to reach the set point Constant rectification of the trajectory by measurement of the control variable. Anticipates the effects of external perturbations Well-performing with constrained processes 17

18 First order model to predict the process: T processoutput K e d s G(s) processinput 1 τ s Defined a desired trajectory to the set point Define an horizon time for which the model predicts an intersection between the desired trajectory and the process. Control unit 18

19 Accounting for measured pertubations The effect of an external pertubation is anticipated by the control. A first order model must be defined to account for these external influences. 19

20 Modelica implementation 20

21 T ex,ev [K] T ev,sp [ C] Simulation results PI controller MPC controller T PI ev,set T MPC ev,set T PI ev T MPC ev time [sec] Time evolution of the superheat at the outlet of the evaporator time [sec] Time evolution of the saturation temperature at the expander supply 21

22 Summary A robust dynamic model of an ORC has been developed, based on validated steady-state component models Different control strategies have been proposed an compared A varying set-point control strategy allows increasing the output by more than 4% MPC controller follow the superheating set point in a much better way than PI-based controllers. Future work will focus on the implementation and the improvement of MPC controllers with delayed processes, during start & stop procedures and in more severe conditions. 22

23 Thank you! 23