Infiltration.ipynb

Kernel: Python 3 (Anaconda)

Infiltration Calculations

Things needed to make calculations more accurate:

• No inf or zeros present in data

• Find true asymtote for CO2_inside (seems to go below 400 "outside ppm")

• Get accurate ETC volume. (used 40,000 ft$^3$ here)

• Isolate Negative slopes

• Determine best days/times to isolate further

check plot at bottom

Calculations

VolumetricFlowRate:

$$\Delta CO_2 = VolumetricFlowRate * (CO_2\_outside - CO_2\_inside)$$$$VolumetricFlowRate = \Delta CO_2\div (CO_2\_outside - CO_2\_inside)$$

• $\Delta CO_2$ = $CO_2$ cubic feet per minute (cfm)

• Volumetric Flow Rate = Total cubic feet per minute (cfm)

• $CO_2$_outside = proportion (unitless)

• $CO_2$_inside = proportion (unitless)

Infiltration:

$$Infiltration = \frac{\Delta CO2}{(CO2\_outside - CO2\_inside)} * \frac{1}{Volume}$$

• Infiltration = "air changes" Per Minute ($\large{\frac{1}{min}}$)

• $\Delta CO_2$ = $CO_2$ cubic feet per minute (cfm)

• $CO_2$_outside = proportion (unitless)

• $CO_2$_inside = proportion (unitless)

• Volume = ETC cubic feet ( $ft^{3}$ )

import pandas as pd

df = pd.read_csv('Deltas_rough.csv')

# df. head()

df.drop(df.columns[[0]], axis =1, inplace = True)

df['CO2_inside'] = df['CO2']

df.head(2)

	CO2	Time	Time2	TimeDelta	CO2_2	CO2_diff	CO2_per_min	CO2_inside
0	718.0	2016-02-19 13:27:00	2016-02-19 13:31:00	4.0	337.0	-381.0	-95.25	718.0
1	337.0	2016-02-19 13:31:00	2016-02-19 13:36:00	5.0	332.0	-5.0	-1.00	337.0

df['ppm_per_min'] = df['CO2_per_min']

df['CO2_cfm'] = (df.ppm_per_min / 1E6) * 4E4

df.head()

	CO2	Time	Time2	TimeDelta	CO2_2	CO2_diff	CO2_per_min	CO2_inside	ppm_per_min	CO2_cfm
0	718.0	2016-02-19 13:27:00	2016-02-19 13:31:00	4.0	337.0	-381.0	-95.25	718.0	-95.25	-3.810
1	337.0	2016-02-19 13:31:00	2016-02-19 13:36:00	5.0	332.0	-5.0	-1.00	337.0	-1.00	-0.040
2	332.0	2016-02-19 13:36:00	2016-02-19 13:41:00	5.0	328.0	-4.0	-0.80	332.0	-0.80	-0.032
3	328.0	2016-02-19 13:41:00	2016-02-19 13:46:00	5.0	307.0	-21.0	-4.20	328.0	-4.20	-0.168
4	307.0	2016-02-19 13:46:00	2016-02-19 13:51:00	5.0	296.0	-11.0	-2.20	307.0	-2.20	-0.088

df.CO2_cfm.describe()

count    1.009840e+05
mean              NaN
std               NaN
min              -inf
25%     -4.800000e-02
50%      0.000000e+00
75%      4.000000e-02
max               inf
Name: CO2_cfm, dtype: float64

df['CO2_outside'] = 0.04

df['Net_proportionCO2'] = df.CO2_outside - (df.CO2_inside / 1E6 )*100

df.head()

	CO2	Time	Time2	TimeDelta	CO2_2	CO2_diff	CO2_per_min	CO2_inside	ppm_per_min	CO2_cfm	CO2_outside	Net_proportionCO2
0	718.0	2016-02-19 13:27:00	2016-02-19 13:31:00	4.0	337.0	-381.0	-95.25	718.0	-95.25	-3.810	0.04	-0.0318
1	337.0	2016-02-19 13:31:00	2016-02-19 13:36:00	5.0	332.0	-5.0	-1.00	337.0	-1.00	-0.040	0.04	0.0063
2	332.0	2016-02-19 13:36:00	2016-02-19 13:41:00	5.0	328.0	-4.0	-0.80	332.0	-0.80	-0.032	0.04	0.0068
3	328.0	2016-02-19 13:41:00	2016-02-19 13:46:00	5.0	307.0	-21.0	-4.20	328.0	-4.20	-0.168	0.04	0.0072
4	307.0	2016-02-19 13:46:00	2016-02-19 13:51:00	5.0	296.0	-11.0	-2.20	307.0	-2.20	-0.088	0.04	0.0093

df['VolumetricFlowRate'] = df.CO2_cfm / df.Net_proportionCO2
df.head()

	CO2	Time	Time2	TimeDelta	CO2_2	CO2_diff	CO2_per_min	CO2_inside	ppm_per_min	CO2_cfm	CO2_outside	Net_proportionCO2	VolumetricFlowRate
0	718.0	2016-02-19 13:27:00	2016-02-19 13:31:00	4.0	337.0	-381.0	-95.25	718.0	-95.25	-3.810	0.04	-0.0318	119.811321
1	337.0	2016-02-19 13:31:00	2016-02-19 13:36:00	5.0	332.0	-5.0	-1.00	337.0	-1.00	-0.040	0.04	0.0063	-6.349206
2	332.0	2016-02-19 13:36:00	2016-02-19 13:41:00	5.0	328.0	-4.0	-0.80	332.0	-0.80	-0.032	0.04	0.0068	-4.705882
3	328.0	2016-02-19 13:41:00	2016-02-19 13:46:00	5.0	307.0	-21.0	-4.20	328.0	-4.20	-0.168	0.04	0.0072	-23.333333
4	307.0	2016-02-19 13:46:00	2016-02-19 13:51:00	5.0	296.0	-11.0	-2.20	307.0	-2.20	-0.088	0.04	0.0093	-9.462366

df['Infiltration'] = df.VolumetricFlowRate / 4E4

df['ACH'] = df.Infiltration * 60 

df.set_index(df.Time2, inplace = True)
df.ACH.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f7c08ea7a58>