MODULE TITLE CONTROL SYSTEMS AND AUTOMATIONTOPIC TITLE.docx
Report_EBACProject_BBP
1. REPORT
A COMPREHENSIVE ANALYSIS AND OPTIMIZATION
OF CHILLER PLANT
NUS ISS EBAC PROJECT
REP
PREPARED BY
Arun V Sankar
Under the Guidance of
Mr. C V KUMAR & Mr. Sunil Yadav
Barghest Building Performance
And
Mr. Aditya Shankar
Faculty, Institute Of System Science
National University of Singapore
2. 1 | P a g e
TABLE OF CONTENTS
Executive Summary.................................................................................................2
Business Domain.....................................................................................................3
Business and Data Objective...................................................................................4
Data Description ....................................................................................................5
Exploratory Analysis................................................................................................6
Design of Experiment..............................................................................................9
Regression Model .................................................................................................13
Inference ..............................................................................................................15
Challenges ...........................................................................................................16
Appendix ...........................................................................................................16
Reference ..........................................................................................................16
3. 2 | P a g e
Executive Summary
This report provides an analysis of the impact of relative humidity, cooling load and wet bulb
temperature on energy consumption in the chiller and cooling tower of central chiller plant.
This report also tries to model the best approach temperature which when implemented
automates the existing manual intervention of updating approach temperature.
Methods of analysis include Design Of Experiment and Linear Regression modeling and
approach includes removing relevant outliers and wrong data using exploratory analysis
followed by classification of the input variables for better interpretability. Input Data is
classified into binary values (Low/High) for Design Of Experiment purpose. Chiller Kilowatt per
Tonnage (CH_eff) and Cooling Tower Kilowatt per Tonnage (CT_eff) are taken as two y
values(target values) for which experiment is designed. Impact of each input variables
(individual/interaction) is analyzed by plotting line graphs and pie chart. Regression model is
made from a subset of the dataset made by segregrating the best ( minimum CH_CT_eff) in all
possible combination of relative humidity, cooling load and wet bulb temperature. Approach
temperature equation is modelled from the subset and a weighted average of difference
between the CH_CT Kw/Ton of the model and raw data is measured to estimate the savings
made if any. All calculations can be found in the appendices.
Major Conclusion from this report/study include:
WB_T is the major variable impacting chiller side KW while Relative humidity in Cooling
tower KW ·
It is found that all input variables impact on CH_eff and CT_eff are opposite in nature hence
an optimal control so as to minimise the total power (Chiller KW+Cooling Tower KW) consumed
as a whole is proposed in modeling the approach temperature.
The report also investigates the fact that the analysis conducted has limitations. Some of the
limitations include non repeteability of equation for another chiller plant and effect of incorrect
or wrong readings fetch at times.
4. 3 | P a g e
BUSINESS DOMAIN:
Barghest Building Performance involves in long term energy performance savings
contracts in existing buildings. We use customized control systems, sensors and SCADA
environment to fetch 1 minute level data to analyze and optimize working of the chiller plant of
the building. SCADA (Supervisory Control And Data Acquisition) is a system for remote
monitoring and control that operates with coded signals over communication channels.
Working Of Chiller Plant
Two water loop namely condensor water loop and chilled water loop helps in removing the
heat from the building . Chilled water loop is cooled by cold condensor water ( CWS_T) which
gets reheated again (the hot condensor water ( CWS_R)) goes to cooling tower where it is
cooled by blowing air from outside.
The basic principle of the cooling tower operation is that of evaporative condensation and
exchange of sensible heat. The air and water mixture releases latent heat of vaporization which
has a cooling effect on water by turning a certain amount of liquid into its gaseous state
releasing the latent heat of vaporization.
5. 4 | P a g e
Business Objective
Improve the energy efficiency of the chiller plant through control of the existing
processes with the help of data analysis.
Data objective
To analyze the dataset and use the insights from the data to model the best
approach temperature which minimizes power consumed without compromising
on the cooling needs of the building.
Success Criteria
Automating input of best approach temperature resulting in minimal
manual intervention.
Evaluate the projected power savings made by the proposed model.
6. 5 | P a g e
Data Description
1 minute data from 1 month long dataset;44510 rows with 84 variables
Data were cleaned which had faulty readings of some variables.
At a time 2 out of 4 chillers will work, either Chiller 2 and 3 or Chiller 1 and 4.
Since the study is limited to estimate approach temperature only relevant variables are
considered and others are discarded based on subjective knowledge and discussions.
Variables considered for the analysis of approach temperature are
1. Relative Humidity (RH)
2. Wet Bulb Temperature (WB_T)
3. Cooling Load (CL_CH)
4. Chiller_KW/Ton (CH_eff)
5. Cooling Tower_KW/Ton (CT_eff)
RH is the ratio of the quantity of water vapor present in a cubic feet of air to the greatest
amount of vapor which that air could hold at a given temperature.
WBT is the temperature when bulb of a thermometer is covered with a wet cloth or actual
temperature felt by us taking into consideration the humidity factor present in air.
CL_CH is the amount of heat generated within a building space from occupants,electrical
equipements, lighting and solar radiation that chiller system must remove.
CH_eff and CT_eff are measures of efficiency of Chiller and Cooling Tower respectively
measured in Kilowatt per Tonnage.
7. 6 | P a g e
ExploratoryAnalysis
Further analysis of these variables are done by plotting box-plots as given below.
9. 8 | P a g e
RH is seen to be in 40-100 value out of which most of values are found in 90-100.
Cooling Load is found in the range 1200-2200 KW/Ton
Wet Bulb Temperature lies in 23-30 deg C.
Outliers found from the above three box plots are discarded (whole row) which
otherwise can affect the accuracy of the model build.
Thus the Cooling Load range is reduced from 1400-2000 KW/Ton.
For easier interpretation, these 3 continuous variables are converted to categorical
variable by binning values into different bins.
Ceiling is put rounding the value to upper nearest 0.5,100,10 for WB_T, CL_CH, and RH
respectively.
WB_T_0.5 CL_CH_100 RH_10
23.5 1500 50
24 1600 60
24.5 1700 70
25 1800 80
25.5 1900 90
26 2000 100
26.5
27
27.5
28
28.5
29
29.5
30
10. 9 | P a g e
Design of Experiments (DOE) techniques is used in the dataset to determine
simultaneously the individual and interactive effects of three factors that affect the output
results. Since we chose three elements, we must construct 8 experiments (2^3) for a full
factorial experiment. Here each factor taken (ie. RH,WB_T,CL_CH) is converted to binary values
either Low or High.
WB_T_0.5 CL_CH_100 RH_10
23.5 1500 50
24 1600 60
24.5 1700 70
25 1800 80
25.5 1900 90
26 2000 100
26.5
27
27.5
28
28.5
29
29.5
30
8 experiments with two y values y1=CH_eff, y2=CT_eff is designed from the dataset
Wb_T CL_CH RH CH_eff CT_eff
Low Low Low 0.518375 0.030379
High Low Low 0.530831 0.027305
Low High Low 0.522544 0.026887
High High Low 0.533175 0.025872
Low Low High 0.518748 0.033012
High Low High 0.529602 0.03528
Low High High 0.519543 0.031423
High High High 0.530735 0.032313
Low
High
h
11. 10 | P a g e
We define the main interaction E of a variable X the difference between the average response
variable at the high level samples and the average response at the low level samples.
Here E1 denotes interaction of WB_T alone with y1=CH_eff, y2=CT_eff.
Similarly E2, E3 for CL_CH and RH respectively, while E12 denotes combined WB_T and CL_CH
against y1 and y2 ; similarly other E values for respective variable combination.
SS in table below denotes Sum of Squares
13. 12 | P a g e
Sum of CH_eff and CT_eff (CH_CT_eff) vsWetBulb in different RH range
14. 13 | P a g e
Sum of CH_eff and CT_eff (CH_CT_eff) vs WetBulb in Cooling Load
range
Regression Model
Regression model is made from a subset of the dataset made by segregrating the best (
minimum CH_CT_eff) in all possible combination of
1. Wet Bulb Temperature
2. Cooling Load
3. Relative Humidity
210 combinations/rows of these 3 variables are in the subset made.
15. 14 | P a g e
.
All variables in the model is found to be having very high significance as denoted by *** sign.
Various combination and transformed variable are tried while modeling and the above combination is
found to be giving highest adjusted R-squared value
Approach_model =-33.35+0.01791*RH+3.077*WB_T+-0.07297*WB_T^2+ 0.001653*CL_CH.
Measures of error is found to be giving a fair enough value(low) for the proposed model:
Mean Absolute Error = 0.30
Root Mean Squared Error (RMSE)= 0.4496,
Mean Absolute Percent Error (MAPE) = 14.45.
Average Approach temperature(Approach_Raw) of the above set of 210 combinations/rows
from the full dataset is calculated and compared with the Approach temperature the model has
estimated (Approach_model).
For this comparison we have used the same set of binned variables of the three variables
namely WB_T_0.5, CL_CH_100, RH_10 where the subscript value in the variable denotes the
nearest digit to which it is rounded.
For the purpose of estimating the KW/Ton saved using the model we have calculated the
occurences or frequency of rows which is classified in each of the 210 rows considered.
By knowing the frequency , a weighted average of difference between the CH_CT Kw/Ton of
the model and raw data is measured.
CH_CT_diff = 0.026332153 which is approximately 4.78% of total energy consumed per month.
16. 15 | P a g e
Inference
E1=.011283 implies a unit increase in WB_T ( 1 degree increase) results in 0.011283
increase in CH_eff (Chiller Kilowatt per ton) ; similarly E2 and E3 value increase in CH_eff
with unit increase in Cooling Load (CL_CH) and Relative Humidity(RH) respectively.
While there is increase in CH_eff with increase in WB_T and CL_CH, there is decrease in
value of CH_eff with every unit increase in RH.
It should be noted that Cooling load ranges are in thousands and relative humidity
ranges in 40-100 while wet bulb temperature range is from 23-30 and subsequent effect
with unit increase (addition of 1) are not scaled.
Interaction of these 3 variables E12, E13 does not have higher percentage contribution
to CH_eff while E23 has the highest among these with 9% contribution.
It is also noteworthy to see that all E values for CH_eff and CT_eff are of opposite signs ;
say E1 value of CH_eff being positive while E1 value of CT_eff is negative and similarly
for all E values. This implies that for subsequent increase in CH_eff there is a decrease in
CT_eff.
WB_T % contribution for CH_eff is very high (93%) while its % contribution for CT_eff is
too low which implies WB_T does not play any role in CT_eff which is exactly opposite to
CH_eff.
RH factor has high impact in CT_eff (75%) and E value is positive i.e CT_eff increases
with increase in RH.
Chiller KW(CH_eff) increases with higher wetbulb temp while cooling tower KW(CT_eff)
decreases with higher wetbulb temperature
Lower CWS_T can be achieved at the expense of higher cooling fan power but lowest
possible CWS_T might not save the most energy
Implementing the above linear regression model has an estimated power consumption
savings of 4.78%.
17. 16 | P a g e
Challenges
This model works good only in the selected range which are basic standard
conditions for which the chiller in the particular site is built
WB_T 23-30 deg C
Cooling Load 1200-2200 KW
RelativeHumidity 40-100 RH
Model accuracy depends on the accuracy of data captured by the sensors and
meters. It has come to the notice that at times readings are wrongly captured.
Cutoff values need to be set along with the equation which will then require manual
intervention.
Implementing the same model equation in another plant where capacity constraints
are different require the repetition of the above work using the new dataset from
the new site and equation will differ based on the plant environment and other
constraints.
Appendix
https://drive.google.com/file/d/0B1J1_Vm7VpxLYzdsc2J6XzZWMVU/view?usp=sharing
Reference
http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/design-of-
experiments-tutorial.html
