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)
412
3 DATAANDRESULTS
3.1 Descriptionofexperiments
Threetestswereperformed:
InTest1,ashortseriesofcontrolledtestsaimedat
testing equipment performance were carried out
withintheworkarea(engineroom).
Thereadings of thebuilttools were comparedto
the readings of more expensive classical
equipment,
certified.
InTest2, withintheworkingarea (thecarroom)
were placedon therow differentvariable quantities
of the elements under monitoring and data were
collectedfromthetwoequipment.
InTest3,theequipmentwasexposedtoanumber
of common sources of internal emission, specific
to
theworkarea.
The Working Party (working area) has been
instructed to operate normally, except that the
bracelethasbeen attachedduring activities,thedata
obtained is centralised and synchronized with the
datafromthefixedunit.
3.2 Theresultsandgraphycalinterpretation
1000 values for NO
2 sensor, CO sensor and NH3
sensor in Excel files have been collected in current
time(e.g.,fromH16.30toH17.56)(Table1)andafter
were designed graphycal interpretations for each
sensor(Figure6,Figure7,Figure8).
Figure6.Thesensorvalue.
Figure7.Thevaluesofsensorsincurrenttime.
Table1.Therealvaluesfrommeasurementswithsensors
_______________________________________________
CurrentSenzor Senzor Senzor
time valueNO2 valueCO valueNH3
_______________________________________________
16:30:40 0.0011.000.00
16:30:40 0.000.000.00
16:30:40 0.002.001.00
16:30:41 0.005.004.00
16:30:41 0.009.006.00
16:30:42 0.0011.000.00
16:30:42 0.000.000.00
16:30:43 0.002.002.00
16:30:43 0.006.005.00
16:30:44 0.009.007.00
16:30:44 0.0012.000.00
16:30:45 0.000.000.00
16:30:45 0.001.00
1.00
16:30:46 0.006.004.00
16:30:46 0.008.005.00
16:30:46 0.0011.000.00
16:30:47 0.000.000.00
16:30:47 0.000.000.00
16:30:48 0.001.002.00
16:30:48 0.006.004.00
16:30:49 0.008.005.00
16:30:49 0.0011.000.00
16:30:50 0.000.000.00
16:30:50 0.000.000.00
16:30:51 0.002.002.00
16:30:51
0.006.004.00
16:30:51 0.009.006.00
16:30:52 0.0011.000.00
16:3:52 0.000.000.00
16:30:53 0.000.000.00
16:30:53 0.001.002.00
16:30:54 0.007.0054.00
16:30:54 12.0079.000.00
17:56:32 0.001.000.00
17:56:32 15.001.003.00
17:56:33 0.002.002.00
17:56:33 1.000.005.00
17:56:34 0.002.005.00
17:56:34 0.000.000.00
17:56:35 5.000.000.00
17:56:35 6.001.008.00
17:56:36 0.0010.0010.00
17:56:36 1.000.000.00
17:56:36 14.005.008.00
17:56:37 0.000.000.00
17:56:37 1.000.000.00
17:56:38 1.000.006.00
17:56:38 0.0011.000.00
17:56:39 15.001.000.00
17:56:39 26.005.008.00
17:56:40 0.00
3.001.00
17:56:40 0.005.001.00
17:56:41 0.005.000.00
17:56:41 10.005.000.00
17:56:41 8.000.008.00
17:56:42 0.0010.000.00
17:56:42 6.001.0015.00
17:56:43 1.000.001.00
17:56:43 8.001.0010.00
17:56:44 0.000.001.00
17:56:44 0.004.001.00
_______________________________________________
Thetestresultswhicharemeasuredby 3sensors
(Table1) in thesame timeare different, becausethe
qualities of these new generation sensors are being
testedandsomesensors do notrespondcorrectlyto
changesintheparametersinthepremises.Thatʹswhy
three measurements are made, finally
taking their
averagevalue.