**9. The VOC detector**


The VOC detector obviously plays a key role for the real-time monitoring system; the main requirements are listed in Table 1.

Table 1. VOC detector requirements

Inspection of Table 1 shows very demanding requirements; an extensive analysis of the state-of-the-art of VOC detectors available on the market was performed to identify the most suitable technology. Different candidate technologies were considered, including Photo Ionisation Detector (PID), Amperometric Sensors, Quartz Crystal Microbalance (QMC) sensors, Fully Asymmetric Ion Mobility Spectrography (FAIMS) based on MEMS, Electrochemical Sensors and Metal Oxide Semiconductor Sensors (MOSS).

It turned-out that PID technology fitted quite well to the requirements of Table I, and thus it was elected as the basic technology to be used for this application. The device chosen for this application was he Alphasense AH, which exhibits 5ppb (isobutylene) minimum detection level.

Both theoretical and experimental investigations of PID operation were carried-out to assess the technology. Two major issues were identified, capable of potentially affecting the use the PID in our application; the first was that in the low ppb range the calibration curve of the PID is non-linear; this would require an individual, accurate and multipoint calibration with inherent cost and complexity; the second was that, when operated in diffusion mode at low

regulator which was specifically designed to provide maximum energy transfer efficiency from the panel to the battery under any operative condition. In Fig. 8 upper left, the weekly graph of the power absorbed/generated by the photovoltaic power supply is represented; the blue line represents the positive balance, i.e. the panel is charging the battery, while the red line represents the negative balance, i.e. the primary source is supplying energy to the subsystem. In Fig. 8, bottom left, a comparison between the current generated by the system and the solar radiation under very clean daylight condition is presented; the right sheet represents the energy budget statistics generated by the system for one of SN unit. In Fig. 8 right, a summary of the daily, weekly and monthly energy balance is represented; more

The VOC detector obviously plays a key role for the real-time monitoring system; the main

**Operation mode Diffusion (no pumped)**  Targeted gas VOCs IP> 10.6 eV Concentration range (ppb) 2,5 to 5,000 Minimum Detectable Level (ppb) > 2,5

Accuracy < 5% in the overall range

Specificity to benzene typically broad band

Electrochemical Sensors and Metal Oxide Semiconductor Sensors (MOSS).

Inspection of Table 1 shows very demanding requirements; an extensive analysis of the state-of-the-art of VOC detectors available on the market was performed to identify the most suitable technology. Different candidate technologies were considered, including Photo Ionisation Detector (PID), Amperometric Sensors, Quartz Crystal Microbalance (QMC) sensors, Fully Asymmetric Ion Mobility Spectrography (FAIMS) based on MEMS,

It turned-out that PID technology fitted quite well to the requirements of Table I, and thus it was elected as the basic technology to be used for this application. The device chosen for this application was he Alphasense AH, which exhibits 5ppb (isobutylene) minimum detection

Both theoretical and experimental investigations of PID operation were carried-out to assess the technology. Two major issues were identified, capable of potentially affecting the use the PID in our application; the first was that in the low ppb range the calibration curve of the PID is non-linear; this would require an individual, accurate and multipoint calibration with inherent cost and complexity; the second was that, when operated in diffusion mode at low

Sensitivity > 20 mV/ppm

Linearity n.a. VOC data sampling int. (minutes) < 15 Power consumption (mW) < 200 Stabilisation time from power-on T90 (s) < 60 Warm-up time (s) < 60 Interval between services (days) > 120 Lifetime (years) > 5

detailed analysis and diagnostics are available.

**9. The VOC detector** 

requirements are listed in Table 1.

Table 1. VOC detector requirements

level.

ppb and after a certain time of power-off, the detector requires a stabilisation time of several minutes, thus preventing from operating it at minutes duty-cycles.

As for the calibration issue, a linearisation procedure was developed based on a behavioural model of the PID2; accordingly, the voltage read-outs received by the detector, *Vn*, are prior preprocessed by multiplying with a non-linearity compensation factor, *α(C)*, function of the concentration *C:* 

$$W\_{\mathfrak{C}\mathfrak{n}} = \alpha (C\_{\mathfrak{n}}) V\_{\mathfrak{n}} = S\_{\mathfrak{v}} C\_{\mathfrak{n}} \tag{1}$$

where *Vcn* is the read-out corrected by the non-linearity compensation factor *α*, *Cn* is the concentration in ppm and *Vn* is the *nth* read-out in mV, and *Sv* is the PID sensitivity in mV/ppm. Equation (1) shows that, after compensation, the values *Vcn* can be easily mapped in the corresponding concentration value.

In Fig. 9 and 10 the linearised calibration curves in the range 0-500 ppb are presented for two different PIDs. Fig. 9 represents the experimental calibration curve (read-out vs concentration) of a PID with a relatively high sensitivity, 150 mV/ppm. The non-linearity in the range 0-200 ppb is clearly observed, blue line.

Fig. 9. Calibration curves for a PID with high sensitivity before (blue) and after (red) linearisation

The result of the linearisation process, according to the previously outlined procedure, is represented by the red line. Fig. 10 represents the same as Fig. 9 for a PID with relatively low sensitivity (50mV/ppm). In both cases, the linearisation procedure proved to be effective. The main advantage of the described approach is that for performing the PID calibration, one single parameter is needed, i.e. the value of the PID sensitivity, which is measured at ppm concentrations; this makes much simpler and less costly the calibration process.

<sup>2</sup> GF Manes, unpublished results

Real-Time Monitoring of Volatile Organic Compounds in Hazardous Sites 237

efficiency degradation. For those reasons it was decided to operate the PID in continuous

Data from the field are forwarded to a central database for data storage and data rendering. A rich and proactive user interface was implemented, in order to provide detailed graphical data analysis and presentation of the relevant parameters, both in graphical and bidimensional format. Data from the individual sensors deployed on the field can be directly accessed and presented in various formats by addressing the appropriate sensor(s)

The position of each SN and EN unit is displayed on the map; by positioning the mouse pointer over the corresponding icon, a window opens showing a summary of current

A summary of the sensor status for each deployed unit can be obtained by opening the summary panel, Fig. 12, right. The summary panel reports current air temperature/humidity values, along with min/max values of the day (left lower, in Fig. 12), wind speed and direction (left upper, in Fig. 12), and VOC concentration (right, in Fig. 12), in the last six hours. A graphic representation of data gathered by each sensor on-the field can be obtained by

The graphic panel allows anyone to display the stored data in any arbitrary time interval in graphic format; up to six different and arbitrarily selected sensors can be represented in the

Fig. 11. PID stabilisation curve on duty-cycled power-on

**10. Experimental results** 

parameter values.

displayed on the plant map, see Fig 12 left.

opening the graphic panel window, see Fig. 13.

same graphic window for purpose of analysis and comparison.

operation mode.

As for the stabilisation time, several experiments were performed to qualify the PID performance; it was found that at low concentration (tens or hundreds ppb), which represents the area of operation of the VOC detectors in our application and when operated in the diffusion mode, the PID exhibits a stabilisation time of some minutes after a poweroff/power-on cycle. A typical PID duty cycled response after storage is represented in Fig. 11. The experimental stabilisation curve is compared with a 80 s decay-time exponential function showing an excellent fitting. After a warm-up of several hours the PID was powered-off for 15 minutes and then powered-on again; thie sequence simulated a 15 minute sampling interval, which was the initial target of our application; in this experiment ambient concentration was around 50 ppb, which represents the average concentration where the PID is supposed to be set up.

Fig. 10.Calibration curves for a PID with low sensitivity before (blue) and after (red) linearisation

As observed in Fig. 11, a 300 seconds stabilisation time is needed prior the PID can reach a stable read-out value. This experiment shows that a 15 minutes sampling interval calls for a 5 minutes stabilisation time, thus resulting in some 30% duty-cycle. A duty-cycled operation, as compared with a continuous power-on operation, is desirable in principle to prolong both the battery- and lamp-life; however, the benefit of energy saving allowed for by the 30% duty cycle is marginal, when compared with the advantage of achieving a more time-intensive monitoring of VOC concentration, as provided by continuous power-on operation. In terms of energy resources, continuous power-on operation requires some 35 mAh charge, which corresponds to 1 month of full operation with a 30 Ah primary energy source; the corresponding power consumption of 360 mW@12 Vdc can be balanced using a 5 W photovoltaic panel.

The UV lamp expected life is more than 6000 hours of continuous operation; we expect at least a quarterly service for the PIDs, due to environment contamination and related lamp efficiency degradation. For those reasons it was decided to operate the PID in continuous operation mode.

Fig. 11. PID stabilisation curve on duty-cycled power-on
