A Systematic Control Approach to Improve Energy Efficiency ... · • Cooling tower outlet...
Transcript of A Systematic Control Approach to Improve Energy Efficiency ... · • Cooling tower outlet...
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Dr. Pinakpani Biswas
Principal Scientist
R&D, SS
TATA Steel
Jamshedpur
Sayani Adhikari, ME, BIT Mesra
Arunima Giri, Mtech, VIT, Vellore
A Systematic Control Approach to
Improve Energy Efficiency of
Industrial Cooling Towers
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❖ Brief Introduction
❖ Key Challenges
❖ Solution Overview
❖ Objectives & Assumptions
❖ Approach
❖ Model Development
❖ Tools Used
❖ Optimization Flowchart
❖ Results and Discussion
❖ Conclusion
❖ Future Scope of work
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A device used to COOL hot water stream based on evaporative cooling
Hot stream(DM Water)
Cold stream(DM Water)
Warm Process Water
Cold Process Water
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➢Tuning cooling tower parameters for optimized
operation
➢Optimizing Energy Consumption
Cold Water
(Temperature
approach
ambient WBT)
Ambient Air
(Driving force:
Relative humidity)
Hot Air
Hot Water
Cooling
Tower
CT Objective
To decrease hot water temperature
by 4-8 degrees (TATA Steel
Jamshedpur)
Cooling tower is an energy
intensive process
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Monitor Operation/
Collect data
Optimization:
E value
obtained
MATLAB Program
Validation
MATLAB/Simulink
Simulation
Implementation
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• Tune the model parameters to decrease energy consumption
❖ Stage efficiency was assumed to be equal for all stages for
basic simulation purpose which later on to be optimized with the
help of available data
❖ Vapor liquid equilibrium
Assumptions
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Development of Equilibrium Model
for existing cooling tower
Mass Balance Energy Balance
Equilibrium Model
Optimized Model
Simulink Model Validation and
Implementation
MATLABCompiler , Optimization
toolbox & Simulink
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Mass balance for 2 stages
8
Stage-1
𝑮 × 𝑪𝒑𝒂 × 𝑻𝟐 + 𝑳𝟎 × 𝑪𝒑𝒘 × 𝑻𝟎 + 𝑮 ×𝑯𝟐 × 𝑪𝒑𝒗 × 𝑻𝟐 + 𝑮 ×𝑯𝟐 × 𝝀
= 𝑮 × 𝑪𝒑𝒂 × 𝑻𝟏 + 𝑳𝟏 × 𝑪𝒑𝒘 × 𝑻𝟏 + 𝑮 ×𝑯𝟏 × 𝑪𝒑𝒗 × 𝑻𝟏 + 𝑮 ×𝑯𝟏 × 𝝀
𝐌𝐚𝐬𝐬 𝐨𝐟 𝐰𝐚𝐭𝐞𝐫 𝒊𝒏 = 𝐌𝐚𝐬𝐬 𝐨𝐟 𝐰𝐚𝐭𝐞𝐫 𝐨𝐮𝐭
𝐒𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐡𝐞𝐚𝐭 𝐨𝐟 𝐢𝐧𝐥𝐞𝐭 𝐚𝐢𝐫 + 𝐬𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐡𝐞𝐚𝐭 𝐨𝐟 𝐢𝐧𝐥𝐞𝐭 𝐰𝐚𝐭𝐞𝐫+ 𝐬𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐡𝐞𝐚𝐭 𝐨𝐟 𝐢𝐧𝐥𝐞𝐭 𝐰𝐚𝐭𝐞𝐫 𝐯𝐚𝐩𝐨𝐮𝐫 + 𝐥𝐚𝐭𝐞𝐧𝐭 𝐡𝐞𝐚𝐭 𝐨𝐟 𝐢𝐧𝐥𝐞𝐭 𝐰𝐚𝐭𝐞𝐫 𝐯𝐚𝐩𝐨𝐮𝐫= 𝐬𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐡𝐞𝐚𝐭 𝐨𝐟 𝐞𝐱𝐢𝐭 𝐚𝐢𝐫 + 𝐬𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐡𝐞𝐚𝐭 𝐨𝐟 𝐞𝐱𝐢𝐭 𝐰𝐚𝐭𝐞𝐫+ 𝐬𝐞𝐧𝐬𝐢𝐛𝐥𝐞 𝐡𝐞𝐚𝐭 𝐨𝐟 𝐞𝐱𝐢𝐭 𝐰𝐚𝐭𝐞𝐫 𝐯𝐚𝐩𝐨𝐮𝐫 + 𝐥𝐚𝐭𝐞𝐧𝐭 𝐡𝐞𝐚𝐭 𝐨𝐟 𝐞𝐱𝐢𝐭 𝐰𝐚𝐭𝐞𝐫 𝐯𝐚𝐩𝐨𝐮𝐫
Stage-1
𝑳𝒐 + 𝑮𝑯𝟐 = 𝑳𝟏 + 𝑮𝑯𝟏 => 𝑳𝟏 = 𝑳𝒐 + 𝑮(𝑯𝟐−𝑯𝟏)
Stage-2
𝑳𝟏 + 𝑮𝑯𝟑 = 𝑳𝟐 + 𝑮𝑯𝟐 => 𝑳𝟐 = 𝑳𝟏 + 𝑮(𝑯𝟑−𝑯𝟐)
Stage-2
𝑮 × 𝑪𝒑𝒂 × 𝑻𝒂 + 𝑳𝟏 × 𝑪𝒑𝒘 × 𝑻𝟏 + 𝑮 ×𝑯𝟑 × 𝑪𝒑𝒗 × 𝑻𝒂 + 𝑮 ×𝑯𝟑 × 𝝀
= 𝑮 × 𝑪𝒑𝒂 × 𝑻𝟐 + 𝑳𝟐 × 𝑪𝒑𝒘 × 𝑻𝟐 + 𝑮 ×𝑯𝟐 × 𝑪𝒑𝒗 × 𝑻𝟐 + 𝑮 ×𝑯𝟐 × 𝝀
Energy balance for 2 stages
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➢ Unknowns: L2, L1, T2, T1, H2, H1 (No of unknowns is 6)
➢ No of equations: 4
➢ Degrees of freedom=2
➢ To eliminate one set of unknowns in order to make the number of unknowns and number of equations equal murphy
efficiency equation is used
𝑬𝒏= 𝒀𝒏−𝒀 𝒏+𝟏
𝒀𝑺𝒏−𝒀(𝒏+𝟏)
➢ Equations obtained for ‘2’ stages are
𝒀𝟏 = 𝑬𝟏 𝒀𝑺𝟏 − 𝒀𝟐 + 𝒀𝟐
𝒀𝟐 = 𝑬𝟐 𝒀𝑺𝟐 − 𝒀𝟑 + 𝒀𝟑
➢ H1 and H2 can be calculated from Y1 and Y2
𝑯𝟏 =𝒀𝟏
𝟏 − 𝒀𝟏∗𝑴𝑯𝟐𝑶
𝑴𝑨𝒊𝒓
𝑯𝟐 =𝒀𝟐
𝟏 − 𝒀𝟐∗𝑴𝑯𝟐𝑶
𝑴𝑨𝒊𝒓
➢ Degrees of freedom after variable substitution: No. of variables 4 and No. of equations 4. This implies D.O.F=0
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MATLAB R2016b
Simulink
MATLAB Compiler
Optimization toolbox
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Variable initialization
Calculation of primary unknowns from initial parameters
(vapor pressure, inlet humidity, mole fraction etc.)
Calculation of partial pressure, humidity, mole
fraction and liquid flow rate for each individual
stages (in terms of temperature)
Calculation of temperature by substitution
Outlet temperature value for all stages
Providing the final
temperature value to the
respective calculations
Final values
i
ii
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Main function
Data set
Function solver
Temperature value
Error<0.1
Optimized efficiency
and number of stages
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A: Comparison of initial MATLAB model with optimized model validated with
experimental data. B: Error Comparison before optimization and after
optimization
A B
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S-Function block
Used to solve differential equations with initial conditions
Conditions: flag=0 initialization, flag=1 derivatives
Flag=3 output, flag=2 discrete, flag=9 termination
Heater
Input: water temperature, flowrate, constant heat supply
Output: heat gain (increase in temperature due to heat supply)
Temperature calculation module
Input to the module: gas and liquid flow rate, relative humidity, partial
pressure, ambient air temperature, inlet liquid temperature
Output of the module: stage temperatures, liquid flow rate at each stage
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-Controllers were tuned by auto tuning method and by adjusting time and robustness
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10% reduction in liquid flowrate
567.586553.071
30.86
446.72
498.324
32.6476
0
100
200
300
400
500
600
CT Fan (KW) PumpPower (KW) CT in Temp(oC)
Power (in KW)
Original Final
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20% reduction in liquid flowrate
567.586553.071
30.86
430.254442.947
33.41907
0
100
200
300
400
500
600
CT Fan (KW) PumpPower (KW) CT in Temp(oC)
Power (in KW)
Original Final
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30% reduction in liquid flow rate
567.586553.071
30.86
411.753387.567
34.4027
0
100
200
300
400
500
600
CT Fan(KW) PumpPower (KW) CT in Temp(oC)
Power (in KW)
Original Final
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40% reduction in liquid flow rate
567.586553.071
30.86
338.184 332.478
34.4193
0
100
200
300
400
500
600
CT Fan(KW) PumpPower(KW) CT in Temp(oC)
Power (in KW)
Original Final
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• Cooling tower outlet temperature was predicted by mathematical model created
• Created model was optimized dynamically
• Improved efficiency and optimized number of stages were obtained
• Three stages with 60 % efficiency
• Cooling tower can be operated by decreasing the water flowrate up to 30 % without
affecting the overall performance
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Data collection
Development of mathematical
model
Model creation in Matlab
Validation of Matlab result
using collected data
Matlab model optimization
Optimized number of stages=3,
efficiency= 60%
Performance evaluation at
reduced flowrate
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Control Application based on MIMO system
Model predictive control, robust control
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