TOWARDS THE OPTIMAL OPERATION OF AN ORGANIC RANKINE CYCLE UNIT BY - - PowerPoint PPT Presentation

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TOWARDS THE OPTIMAL OPERATION OF AN ORGANIC RANKINE CYCLE UNIT BY MEANS OF MODEL PREDICTIVE CONTROL Andres Hernandez*, Adriano Desideri, Clara Ionescu, Sylvain Quoilin, Vincent Lemort and Robin De Keyser Electrical energy, Systems and Automation

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TOWARDS THE OPTIMAL OPERATION OF AN ORGANIC RANKINE CYCLE UNIT BY MEANS OF MODEL PREDICTIVE CONTROL

Andres Hernandez*, Adriano Desideri, Clara Ionescu, Sylvain Quoilin, Vincent Lemort and Robin De Keyser

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Electrical energy, Systems and Automation Ghent University, Belgium Thermodynamics laboratory University of Liège, Belgium

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SLIDE 2

Outline

  • 1. Motivation
  • 2. Organic Rankine Cycle
  • 3. Optimal conditions
  • 4. Control strategies
  • a. PI, SW-PI and MPC.
  • 5. Results
  • 6. Conclusion

2

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Energy Efficiency in Industry

Motivation Process description Optimal conditions Control strategies Conclusions Results

Energy Energy

Low-temperature Waste heat

In the product In the product

Low-temperature Waste heat

Recovery system

Energy recovered Losses Losses

Challenge: System dynamics change due to the fluctuating nature of the waste heat

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SLIDE 4

Outline

  • 1. Motivation
  • 2. Organic Rankine Cycle
  • 3. Optimal conditions
  • 4. Control strategies
  • a. PI, SW-PI and MPC.
  • 5. Results
  • 6. Conclusion

4

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Organic Rankine Cycle

Operate in safe conditions Maximize the

  • utput power

Waste heat

Organic Rankine Cycle

Motivation Process description Optimal conditions Control strategies Conclusions Results

π‘ˆπ‘‘π‘π‘’ ,𝑓𝑀 = 𝑔(π‘žπ‘‘π‘π‘’ ,𝑓𝑀)

Evaporating temperature

βˆ†π‘ˆπ‘‘β„Ž = π‘ˆπ‘“π‘¦π‘ž ,π‘—π‘œ βˆ’ π‘ˆπ‘‘π‘π‘’,𝑓𝑀

Superheating

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Organic Rankine Cycle

  • 1. Heating loop: Electrical Boiler (therminoll66)
  • 2. Cooling loop: glycol-water system
  • 3. Regenerative cycle
  • 4. Working fluid: SES36
  • 5. Expander capacity 11Kwe (single screw)

Test-rig

Motivation Process description Optimal conditions Control strategies Conclusions Results

(Ghent University - Campus Kortrijk)

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Simulation environment

Motivation Process description Optimal conditions Control strategies Conclusions Results

FMI toolbox Dymola/Modelica: System dynamics and fluid properties Matlab: Model Predictive Control (MPC) and optimization algorithm. Control algorithm is then compiled as a β€˜dll’ and linked to the current Labview interface.

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Model validation

Motivation Process description Optimal conditions Control strategies Conclusions Results

Adriano Desideri et.al. COMPARISON OF MOVING BOUNDARY AND FINITE-VOLUME HEAT EXCHANGERS MODELS IN THE MODELICA LANGUAGE, ASME ORC 2015, Brussels, Belgium

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Npp βˆ†Tsh Tsat,ev Inputs Outputs Disturbances ṁhf Thf

Control variables

Motivation Process description Optimal conditions Control strategies Conclusions Results

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Outline

  • 1. Motivation
  • 2. Organic Rankine Cycle
  • 3. Optimal conditions
  • 4. Control strategies
  • a. PI, SW-PI and MPC.
  • 5. Results
  • 6. Conclusion

10

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Basic Control: Optimized control: Main optimization parameter: Evaporating temperature

The concept of an optimized control

Motivation Process description Optimal conditions Control strategies Conclusions Results

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Relationship between power and controlled variables

Motivation Process description Optimal conditions Control strategies Conclusions Results 70 80 90 100 110 120 500 1000 1500 2000 2500 3000 3500 4000 4500 Tsat,ev [Β°C] Wexp [W] a) Outpower as function of evaporating temperature 10 20 30 40 50 500 1000 1500 2000 2500 3000 3500 4000 4500  Tsh [Β°C] Wexp [W] b) Outpower as function of superheating Pump Speed - Npp [rpm] 1400 1500 1600 1700 1800 1900 2000 Pump Speed - Npp [rpm] 1400 1500 1600 1700 1800 1900 2000 Thf=125 Β°C Thf=110 Β°C Thf=100 Β°C Thf=90 Β°C Thf=125 Β°C Thf=110 Β°C Thf=100 Β°C Thf=90 Β°C

π‘›β„Žπ‘” = 1.0 𝑙𝑕/𝑑

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Optimal evaporating temperature (Static Map)

π‘ˆπ‘‘π‘π‘’,π‘π‘žπ‘’ = βˆ’290.915 + 183.33 βˆ— π‘šπ‘π‘•10 π‘ˆβ„Žπ‘” + 10.636 βˆ— π‘›β„Žπ‘” Valid in the range between 0.5 < mhf < 1.5 kg/s and 90 < Thf < 125 Β°C, for Psat,cd=1.4 bar π‘›β„Žπ‘” = {1.5 ; 1.0 ; 0.5} 𝑙𝑕/𝑑

Motivation Process description Optimal conditions Control strategies Conclusions Results

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Extremum Seeking (ES) algorithm

Motivation Process description Optimal conditions Control strategies Conclusions Results

1) Modulation phase The adaptation signal shifts the sine wave towards the gradient direction 2) Obtaining the gradient direction 3) Computing the adaptation law

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SLIDE 15

Outline

  • 1. Motivation
  • 2. Organic Rankine Cycle
  • 3. Optimal conditions
  • 4. Control strategies
  • a. PI, SW-PI and MPC.
  • 5. Results
  • 6. Conclusion

15

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Control architecture

Controllers:

  • 1. PI
  • 2. Switching PI
  • 3. Model Predictive Control

Motivation Process description Optimal conditions Control strategies Conclusions Results

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Control Design

EPSAC - MPC

v = 𝑠 𝑒 + 𝑙 𝑒 βˆ’ 𝑧(𝑒 + 𝑙|𝑒) 2

𝑂2 𝑙=𝑂1 Subject to:

𝑧+ = 𝑔 𝑧, 𝑣 π‘‰π‘›π‘—π‘œ < 𝑉 < 𝑉𝑛𝑏𝑦 𝑍

π‘›π‘—π‘œ < 𝑍 < 𝑍 𝑛𝑏𝑦

βˆ†π‘‰π‘›π‘—π‘œ < 𝑉 𝑒 βˆ’ 𝑉 𝑒 βˆ’ 1 < βˆ†π‘‰π‘›π‘π‘¦

min U

Umin = 1320 rpm Umax = 2100 rpm βˆ†U = 100 rpm/s N1=1, N2=15, Nu=1

Motivation Process description Optimal conditions Control strategies Conclusions Results

Switching PI strategy

If DTsh > DTsh,min PID Tsat SP = Tsat,opt Ui Tsat = Ui DTsh PID DTsh SP = DTsh,min Ui DTsh = Ui Tsat End NO YES

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Sampling time Ts = 1s

200 400 600 800 1000 1200 1400 1600 1800 2000

  • 10
  • 8
  • 6
  • 4
  • 2

2 4 6 Identification experiment: Superheating Superheating [Β°C] Data; measured sys; fit: 71.24% 200 400 600 800 1000 1200 1400 1600 1800 2000

  • 2.5
  • 2
  • 1.5
  • 1
  • 0.5

0.5 1 1.5 2 2.5 Identification experiment: Saturation temperature Time [s] Temperature [Β°C] Data; measured sys; fit: 88.71%

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System Identification

Motivation Process description Optimal conditions Control strategies Conclusions Results

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SLIDE 19

Outline

  • 1. Motivation
  • 2. Organic Rankine Cycle
  • 3. Optimal conditions
  • 4. Control strategies
  • a. PI, SW-PI and MPC.
  • 5. Results
  • 6. Conclusion

19

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Heat Source conditions

Motivation Process description Optimal conditions Control strategies Conclusions Results

100 200 300 400 110 115 120 125 130 Thf (Β°C) Heat source conditions 100 200 300 400 10 15 20 25 Heat sink conditions Tcf (Β°C) 100 200 300 400 0.5 1 1.5 Mhf (kg/s) Time (s) 100 200 300 400 2 2.5 3 3.5 4 Time (s) Mcf(kg/s)

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Control performance for MPC strategy

100 150 200 250 300 350 400 450 10 20

 Tsh (Β°C)

Control performance for MPC strategy 100 150 200 250 300 350 400 450 90 100 110 Tsat,ev (Β°C) 100 150 200 250 300 350 400 450 1600 1800 Npp (rpm) 100 150 200 250 300 350 400 450 3 4 5 Time (s) Wexp (kW)

ES Map

Motivation Process description Optimal conditions Control strategies Conclusions Results

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ES Map

100 150 200 250 300 350 400 450 10 20

 Tsh (Β°C)

Control performance for PI strategy 100 150 200 250 300 350 400 450 90 100 110 Tsat,ev (Β°C) 100 150 200 250 300 350 400 450 1600 1800 Npp (rpm) 100 150 200 250 300 350 400 450 3 4 5 Time (s) Wexp (kW)

Control performance for PI strategy

Motivation Process description Optimal conditions Control strategies Conclusions Results

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Control performance for SW-PI strategy

100 150 200 250 300 350 400 450 10 20

 Tsh (Β°C)

Control performance for Switching PI strategy 100 150 200 250 300 350 400 450 90 100 110 Tsat,ev (Β°C) 100 150 200 250 300 350 400 450 1600 1800 Npp (rpm) 100 150 200 250 300 350 400 450 3 4 5 Time (s) Wexp (kW)

ES Map

Motivation Process description Optimal conditions Control strategies Conclusions Results

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Controller Net Energy produced Advantages Profit Switching PID 2.79 kWh Safe operation 100 % MPC 3.37 kWh Safe and optimal

  • peration

120 %

Performance comparison

Motivation Process description Optimal conditions Control strategies Conclusions Results

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Motivation Process description Optimal conditions Control strategies Conclusions Results

Experimental results using correlation as optimizer

200 400 600 800 20 40

Tsh [Β°C]

Basic PI 200 400 600 800 85 90 95

Tsat,ev [Β°C]

200 400 600 800 1800 2000 2200

Npp [rpm] Time [s]

200 400 600 800 20 40

Tsh [Β°C]

Basic MPC 200 400 600 800 85 90 95

Tsat,ev [Β°C]

200 400 600 800 1800 2000 2200

Npp [rpm] Time [s]

200 400 600 800 20 40

Tsh [Β°C]

Optimal MPC 200 400 600 800 85 90 95

Tsat,ev [Β°C]

200 400 600 800 1800 2000 2200

Npp [rpm] Time [s]

200 400 600 800 20 40

Tsh [Β°C]

Switching PI 200 400 600 800 85 90 95

Tsat,ev [Β°C]

200 400 600 800 1800 2000 2200

Npp [rpm] Time [s]

100% 115% 121% Unsafe

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Conclusions and perspectives

Motivation Process description Optimal conditions Control strategies Conclusions Results

  • 1. There exists an optimal evaporating temperature which maximizes

the output power given some heat source conditions

  • 2. Small amount of superheating is necessary to guarantee stable
  • peration of the ORC unit.
  • 3. MPC is able to produce the highest net output power since it
  • perates closer to boundary conditions, due to a better constraint

handling.

  • 4. Explore the influence of the expanderΒ΄s speed on the system.
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Acknowledgement

Grant No. (IWT SBO-110006)

www.orcnext.be

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Thank you

AndrΓ©s HernΓ‘ndez Andres.Hernandez@Ugent.be www.orcnext.be