Applications Junqiang (James) Fan Fellow, Systems and Controls - - PowerPoint PPT Presentation

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Applications Junqiang (James) Fan Fellow, Systems and Controls - - PowerPoint PPT Presentation

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Control Design and Verification with Physics Based Models for HVAC/R Applications Junqiang (James) Fan Fellow, Systems and Controls Engineering Sept 28, 2016 OUTLINE Vapor compression refrigeration cycle Model Based Control Development

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Control Design and Verification with Physics Based Models for HVAC/R Applications

Junqiang (James) Fan Fellow, Systems and Controls Engineering Sept 28, 2016

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OUTLINE

Vapor compression refrigeration cycle Model Based Control Development Process Application Examples

  • Transportation Refrigeration
  • Commercial Refrigeration
  • Residential HVAC
  • Commercial Building HVAC

Conclusions

2

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SLIDE 3

VAPOR COMPRESSION REFRIGERATION CYCLE

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SLIDE 4

WHAT IS CONTROL OF HVAC/R?

HVAC/R Plant Control Equipment

Measurements Actuations

What’s important?

  • Control architecture & algorithm design
  • Implementation and test/verification
  • Tuning and commissioning
  • Operation & upgrading

Reliably operating HVAC/R systems to be functional and energy efficient

Fan-coil unit Heat pump

Multideck Cold room evap. Condenser Sanitary hot water Space heating Fresh food Rack Frozen food Rack Cold room evap. Controls Island Combi

  • freezer

Serve

  • ver

Multideck Cold room evap. Condenser Sanitary hot water Space heating Fresh food Rack Frozen food Rack Cold room evap. Controls Island Combi

  • freezer

Serve

  • ver

Multideck Cold room evap. Condenser Sanitary hot water Space heating Fresh food Rack Frozen food Rack Cold room evap. Controls Island Combi

  • freezer

Serve

  • ver

Systems Large-scale systems (building /campus level) PID+ logic coordinated PIDs+ logic Increasing Complexity

4 Supermarket refrigeration

What’s important?

  • Know the physics, systems objectives and limitations
  • Model the physics, component to system
  • System complexity

Controller parameters

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SLIDE 5

From requirements definition to field support

Control design Commissioning Field upgrades and configuration Requirements Modeling and simulation

Symptom 1 Symptom 2 Symptom 3 Symptom N Symptom 4

Verification and validation

Diagnostics and fault detection

Operation

Hardware/software updates Internet

C
  • nt rol
er I nvert er V apor compresso r syst em and heat ers Box
  • Software-in-the-loop

Rapid prototyping, Hardware-in-the-loop

5

Tuning guideline

MODEL BASED CONTROL DEVELOPMENT PROCESS

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SLIDE 6

Infinity NGTM: Residential HVAC System

Developed control architecture and algorithm for robust system performance and optimal efficiency Developed control commissioning guidelines in use by Carrier installers

CO2OLtecTM: Supermarket Refrigeration System

PulsorTM: Truck Refrigeration Equipment

Multideck Cold room evap. Condenser Sanitary hot water Space heating Fresh food Rack Frozen food Rack Cold room evap. Controls Island Combi

  • freezer

Serve

  • ver

Multideck Cold room evap. Condenser Sanitary hot water Space heating Fresh food Rack Frozen food Rack Cold room evap. Controls Island Combi

  • freezer

Serve

  • ver

Multideck Cold room evap. Condenser Sanitary hot water Space heating Fresh food Rack Frozen food Rack Cold room evap. Controls Island Combi

  • freezer

Serve

  • ver

0.5 1 1.5 2 2.5 3 Default tuning New tuning Default tuning New tuning Default tuning New tuning

Control variable 1 Controlled variable 2 Controlled variable 3

39% 66% 60%

19 20 21 22 23 24 74 76 78 80 82 84 Time, hr Zone temp, oF Zone3 SP Zone3 temp Zone4 SP Zone4 temp

Demonstrated HW-independent, model based developed control algorithm on scalable SW platform

Equipment Systems

APPLICATION EXAMPLES

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Supervisory control algorithm : 10% to 15% energy consumption reduction.

Large Systems/Buildings

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PULSOR™…TRUCK REFRIGERATION

Architecture and algorithm design

Product Verification and Validation

Rapid prototyping No control algorithm changes during field trials

Algorithm design

Active constraint control algorithm

  • Eliminated cycling
  • Better performance

Setpoint

Modeling and Simulation

  • Small (~kW) capacity
  • Air-cooled, standard vapor compression

system

  • Single-input-multiple-output control

(Hybrid control solution)

1 2 3 4 5 6 7 8 5 10 15 20 25 30 35 Suction Pressure [bar] Discharge Pressure [bar]

Requirements

Operating constraints

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SLIDE 8

CO2OLTEC™…SUPERMARKET REFRIGERATION

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Faster and accurate system commissioning

  • Large (~100kW) capacity
  • CO2-based refrigeration system
  • Multiple-input-multiple-output control

(100’s control loops)

  • Site-specific configuration

Product

0.5 1 1.5 2 2.5 3 Default tuning New tuning Default tuning New tuning Default tuning New tuning

39% 66% 60%

Control variable 1 Controlled variable 3 Controlled variable 2

CCS using transitioned SW

2010 Control analysis and design

50 100 150 200 50 60 70

Controlled variable 1

Setpoint

50 100 150 200 32 34 36

Setpoint

Controlled variable 2

Modeling and Simulation Requirements

Sanitary hot water Space heating Fresh food Rack Controls Island Combi

  • freezer

Serve

  • ver

Multideck Cold room evap. Condenser Sanitary hot water Fresh food rack Frozen food rack Cold room evap. Controls Island Combi freezer Serve over Controls

  • Space heating

Commissioning guidelines

Control tuning instructions

Before After Before After Before After

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SLIDE 9

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CO2OLtec™: Gas Cooler Modeling

More physics captured by 2-D cross-flow HX model versus 1-D counter flow HX model at reasonable cost of simulation speed

Front view Side view

2-D Gas Cooler Model

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SLIDE 10

INFINITY NG…RESIDENTIAL HVAC

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Software architecture and system control design

Field trial results

19 20 21 22 23 24 74 76 78 80 82 84 Zone temp, oF Zone3 SP Zone3 temp Zone4 SP Zone4 temp

1 2 3 4 5 Time (hr)

No control algorithm changes during field trials

Product … 2012

System control algorithm

  • North American

residential application

  • Multiple-input-multiple-output

control

  • Large variety of configurations

New programming model

Final Code

System Control Automatically generated code

User Interface

Application SW

Automatically generated code Data Dictionary

HW resource mapping

Hardware/software separation

Model-based control algorithm development Requirements

Control algorithm

Layered base software architecture

  • Appl. SW
  • comp. 1
  • Appl. SW
  • comp. 3
  • Appl. SW
  • comp. 5
  • Appl. SW
  • comp. N
  • Appl. SW
  • comp. 2
  • Appl. SW
  • comp. 4
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INTEGRATED WHOLE-BUILDING HVAC MODEL Building

Floor 1 Floor 3 Floor 2 AHU1

3rd Floor Ret. Air Mass Flow, Temp., RH

AHU2 AHU3 Chiller Plant

3rd Floor Sup. Air Mass Flow, Temp., RH Water-Side Sup. Pressure & Temp. Chilled-Water Ret. Pressure & Temp. Water-Side Sup. Pressure & Temp. Water-Side Sup. Pressure & Temp. 2nd Floor Sup. Air Mass Flow, Temp., RH 1st Floor Sup. Air Mass Flow, Temp., RH 2nd Floor Ret. Air Mass Flow, Temp., RH 1st Floor Ret. Air Mass Flow, Temp., RH

Individual Zone Temp. Controls (PI) SAT Control (PI) CHWST CWST DP Control (PI) Inputs

  • Weather & Schedules

Key Outputs

  • Chiller Plant Eqp.

Power, Flow, Temp.

  • AHU Fan Power &

Valve Pos.

  • Zone Temp., RH.

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Case Configurations Definitions

Case Configuration 1 Medium Office + Primary-Only Chiller Plant Configuration Case Configuration 2 Medium Office + Primary-Sec. Chiller Plant Configuration Case Configuration 3 Large Hotel + Primary-Only Chiller Plant Configuration Case Configuration 4 Large Hotel + Primary-Sec. Chiller Plant Configuration

SUMMARY OF CASE STUDIES

4 Case Configurations

1 2 3 4

Web-Bulb Temp.

Test Cases Test Case Scenarios Test 1 Miami Summer Test 2 Miami Shoulder Test 3 Las Vegas Summer Test 4 Las Vegas Shoulder Test 5 Baltimore Summer Test 6 Baltimore Shoulder Test 7 Chicago Summer Test 8 Chicago Shoulder

8 Test Profiles (each case config.)

Control Algorithms Descriptions

  • 1. Baseline Control

Constant chilled-water supply temp. (CHWST) setpoint of 7°C. Load based chiller staging logic.

  • 2. OAT-Based Reset

(ASHRAE 90.1) A linear schedule to reset CHWST setpoint based on outdoor air temperature (ASHRAE 90.1). Load based chiller staging logic.

  • 3. Heuristic-Based

(Trim-Respond) Trim-Respond logic resets CHWST setpoint based on the demand measured by AHU’s chilled-water valve

  • position. One request is generated when one chilled-water valve position becomes greater than a prescribed

threshold (e.g., 90%). Load based chiller staging logic.

  • 4. Low-Cost Optimal

Maximize CHWST setpoint while performing real-time load estimation. Load based chiller staging logic.

4 Chiller Plant Control Algorithms

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LOW-COST OPTIMAL CONTROL

18.1 11.5 12 6.4

Case Config. 1 (Office-PriOnly) Case Config. 2 (Office-PriSec ) Case Config. 3 (Hotel-PriOnly) Case Config. 4 (Hotel-PriSec)

Average Energy Savings (%) from Low-Cost Optimal Control

~15% (office) ~10% (hotel)

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CONCLUSIONS

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Better performing and more robust products  Physics based dynamic modeling and control enabling

  • Control architecture (actuation/sensing) trade-off analysis
  • Algorithm analysis and design
  • Installation/commissioning guidelines development
  • Software robustness testing
  • Equipment diagnostics development

No turn-backs or surprises after the products are developed/deployed