Abstract
Biofiltration is one of the techniques used to reduce odorants in the air. It is based on the aerobic degradation of pollutants by microorganisms located in the filter bed. The research presents the possibility of using the electronic nose prototype for monitoring and efficiency assessment of air biofiltration. The study was conducted using model gas mixtures containing representatives of three groups of chemical compounds: n-hexane (alkanes), acetone (ketones) and toluene (aromatic hydrocarbons). Based on the proposed linear models it has been shown that the determination of concentrations of these substances in the ternary mixture is possible at relatively low concentrations. It was also shown that the used filter medium (pine bark) had the highest removal efficiency of acetone.
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Introduction
In recent years, many areas of human activity, especially waste disposal and waste water treatment processes are the source of odour nuisance. It is caused mainly by inorganic gases (hydrogen sulphide and ammonia) and volatile organic compounds (VOCs), which are formed during the biodegradation of organic compounds (Lehtinen and Veijanen 2011; Lewkowska et al. 2016).
There are four main groups of techniques used to reduce odorants in the air: combustion, adsorption, absorption and biological methods. Biofiltration is one of the biological purification methods (Leson and Winer 1991). Due to the low operating costs and its ability to treat large volumes of purified gas, it is considered to be the best deodorization method for low-concentrated emissions (Muldiar et al. 2010). Biofiltration is based on the aerobic degradation of pollutants by microorganisms located in the filter bed (Sówka et al. 2013). At the contact of the odorants with the first layer of bed covered with biofilm, they are absorbed and then decomposed by microorganisms (Delhoménie and Heitz 2005). This process goes deeper into the bed and when the gases reach the end of the biofilter, most of the pollutants are already degraded. Several types of biofilter media are used: coated ceramic particles, compost, peat, soil or wood bark (Barbusinski et al. 2017). Low molecular weight and water-soluble compounds, such as hydrogen sulphide, ammonia, alcohols, aldehydes and ketones, are the most susceptible to biofiltration (Burgess et al. 2001). The efficiency of removal of selected groups of chemicals using biofiltration is shown in Table 1.
Several analytical methods can be used for the determination of gas concentrations in the biofiltration process. The most popular is gas chromatography coupled with different types of detectors: olfactometry (O), mass spectrometry (MS) or flame ionization detector (FID) (Gutiérrez et al. 2015). From a quantitative and qualitative point of view, gas chromatography is the best solution. However, the need to buy expensive equipment and provide appropriate working conditions (high vacuum or hydrogen availability) often makes use of GC uneconomical.
Another approach to evaluate the odour concentration is olfactometry. This method is described in the EN 13725:2003 standard. The measurements are based on dynamic dilution of the gas sample to the point where 50% of the test panel do not smell odour. Then the odour concentration is determined using the formula:
where c odour is the odour concentration and Z 50% is the dilution degree.
This method is characterized by good repeatability for relatively high concentrations of odorants in the sample. In the case of lower concentrations, its use is troublesome. Similarly, the use of this method for continuous monitoring of biofiltration process is practically impossible, due to the use of the human panel and long time and cost of a single analysis.
In recent years, there has been a significant increase in the interest of using electronic noses for quantitative and qualitative analysis in environmental monitoring (Capelli et al. 2014). Due to the short time and low cost of a single analysis, they have become an alternative to gas chromatography and olfactometry. Electronic noses are the devices equipped with a set of several chemical sensors. The most commonly used types of sensors in e-nose measurement chambers are metal oxide sensors (MOS) (Gancarz et al. 2017). Nowadays, also other types of commercially available sensors gain increasing interest: conductive polymer sensors (CP), quartz microbalances (QMB), surface acoustic wave sensors (SAW) and electrochemical sensors (EC) designed for VOC analysis (Szulczyński and Gębicki 2017). Another element of the e-nose is an appropriate pattern recognition system capable of detection (identification) of simple or complex odours. E-noses in their functioning resemble the sense of human smell. Sensors are analogues of receptor proteins in the epithelia of the nose, turning chemical information into analytically useful signal. This signal is sent to the recognition system which, in the case of human, is the brain, and in the case of e-nose, is the corresponding mathematical–statistical algorithm (Boeker 2014). By appropriate selection of the algorithm to determine characteristic properties of the samples, it is possible to achieve highly repeatable results (quantitative and qualitative) in real time with minimal effort. The monitoring of the biofilter process using the electronic nose has already been reported (Cabeza et al. 2013; López et al. 2011; Sohn et al. 2009).
The research presents the possibility of using the electronic nose prototype (equipped with eight MOS sensors) as an alternative way (to gas chromatography and olfactometry analysis) for monitoring and efficiency assessment of air biofiltration. The study was conducted using model gas mixtures containing three aroma compounds: n-hexane, acetone and toluene. These substances have been selected for three reasons: their presence has been confirmed in atmospheric air around potential odour sources (waste landfills or sewage treatment plants) (Fang et al. 2012; Gębicki et al. 2016; Zarra et al. 2014), they have relatively low odour thresholds and they represent a simple structure within their group of chemical compounds what makes them potentially available to microorganisms located in the filter bed. The linear models connecting substance concentrations to sensors signals have been proposed.
Theoretical
The most commonly used type of sensors in the electronic noses is metal oxide sensors (MOS). The dependence of the sensor signal (change of its resistance) on the concentration of gaseous substance occurring in the sensor environment is described by the Eq. (2):
where R S is the sensor resistance; B, α are the constants values characterizing the sensor; c is the substance concentration.
Using voltage divider as a way to measure the change of the resistance of a sensor, the dependence of the sensor signal (read by the analogue-to-digital converter) on the concentration of the gaseous substance present in the sensor environment is described as follows (3):
where S is the sensor signal, V is the voltage divider input, R is the divider resistance (constant value), and R s is the MOS sensor resistance.
Combining the Eqs. (2) and (3) has shown that the relationship (4) is linear (Gębicki et al. 2015):
For this reason, a linear model (5) describing the concentration of substances in function of the e-nose sensor signal was proposed for further calculations,
where a is the model coefficients and n is the number of sensors in the e-nose system.
The statistical significance of model coefficients was determined using t-statistics for α = 0.05 significance level.
Experimental
Biofiltration unit
The system used during the research is shown in Fig. 1.
Compressed, purified air flowed through a gas mixture generation system. Barbotage technique was used to generate the mixtures with a defined composition. Vials containing liquid substances such as n-hexane, toluene and acetone were incubated at 25 °C. The chemicals (Sigma-Aldrich) were of analytical reagent grade. The concentration ranges of the individual components of the mixture were changed by varying the air flow rate through the given vial. The inlet concentration was determined by loss of liquid weight at a constant air flow rate in a given time (6),
where c is the concentration, Δm is the vial mass change, F is the air flow rate, and t is the time.
The produced mixture was directed to a humidifier, which maintained a constant gas humidity of 85 ± 3%. During the research, column biofilter (OD/ID: 0.05/0.04 m, h = 1 m) has been used. The filter medium was a 4–7 mm pine bark.
GC-FID analysis
The sampling for the GC analysis was performed using the Philips PU4500 gas chromatograph, equipped with FID detector. Chromatographic conditions are presented in Table 2.
Electronic nose measurement
The gas samples were analysed using a constructed electronic nose prototype. The device was equipped with eight metal oxide sensors manufactured by Figaro Engineering Inc. Sensor models are shown in Table 3. Presented sensor models were designed for specific chemicals or applications. However, each of them responds to a wide range of chemical vapours. They have been selected for adjusting the concentration ranges occurring during the research with the detection ranges of the sensors.
Analysed sample was sucked by the pump and flowed through the e-nose system to the measurement chamber at a constant flow rate of 300 cm3 min-1 (controlled by a rotameter). The electronic nose worked in the stop-flow mode: the sample flow time was 25 s and the stop time of the mixture in the sensors chamber − 10 s. These time values have ensured that sensor responses have reached a stable state. They were selected by the optimization method. After this time, the purified air was directed to the measurement chamber for regeneration of the sensors. Signals from the sensors were recorded using an ADC converter (Simex SIAi-8) and saved on the computer.
Concentrations of the individual components of the mixture were determined using the models based on the dependence (5). For calculations, the maximum value of the recorded sensor signal was used. Data analysis and other calculations were performed using RStudio Desktop (v. 1.0.143) software.
Results and discussion
Based on the chromatographic and electronic nose analysis of the same samples, model (5) coefficients were determined for each component of the model gas mixtures. Then iteratively the statistical significance of the model coefficients was determined. In each step, the least significant coefficient of the equation has been eliminated. Iterations were performed until the obtained model had all statistically significant members (for significance level α = 0.05). Obtained models together with determination coefficients are shown in Table 4.
Each model member corresponds to a specific sensor signal (according to Table 3). In the case of n-hexane these were TGS813 and TGS822 sensors (for detection of organic solvent vapours). As can be seen from the proposed model, the concentration of n-hexane in the mixture could be determined using only two sensors signals. Similarly, for acetone and toluene, the analytically useful information is provided by the sensors: TGS822, TGS2602 and TGS2201A.
Figure 2 shows comparison scatter plots of predicted concentrations of the compounds and the values determined by gas chromatography analysis. Presented plots clearly indicate that for the relatively high concentrations of the substances (more than 700 ppm v/v), the proposed model underestimates the expected values. This fact may be due to the sensors saturation phenomenon and their transition to a nonlinear operating range. The biggest differences were observed for acetone.
Similar correlations were observed in the graphs of the biofiltration efficiency depending on the filter bed load under steady conditions (Fig. 3). For relatively high concentrations of acetone and toluene, there were discrepancies in the results obtained by gas chromatography and electronic nose technique. The best agreement of the results was obtained for n-hexane model.
According to the theory, the removal efficiency of biofiltration is decreasing with the increase in bed load. For the test case, the best substance to be removed was acetone. Compared to the other mixture ingredients, acetone has the lowest molecular weight, and is characterized by very good solubility in water. This makes it most accessible to microorganisms.
Conclusions
Presented research shows the use of electronic nose as an alternative method of quantifying the concentrations of acetone, toluene and n-hexane in air. Three linear mathematical models have been proposed to determine the relationship between the concentration of individual components and the signals of the electronic nose sensors (R 2 > 0.9). Statistical analysis has shown that not all e-nose sensors provide useful analytical information. It has been shown that only four sensors are sufficient to determine toluene, acetone and n-hexane. This significantly reduces the cost of building such a device. The results obtained using the model were compared with the values obtained by gas chromatography analysis. The results obtained using both methods showed good agreement. Only in the areas of high concentrations of a given mixture ingredient, the results obtained by the electronic nose were underestimated. Due to the significantly shorter analysis time and possible automation of measurements, it has been found that the use of electronic noses to monitoring of the biofiltration process is appropriate and purposeful. In future, the authors also intend to verify detection abilities of the electronic nose using other calibration techniques such as principal component regression (PCR) or partial least squares regression (PLS).
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Acknowledgements
The investigations were financially supported by the Grant No. UMO-2015/19/B/ST4/02722 from the National Science Centre. Also, the authors want to thank Professor M. Kamiński for sharing equipment to perform gas chromatography analysis.
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Szulczyński, B., Gębicki, J. & Namieśnik, J. Monitoring and efficiency assessment of biofilter air deodorization using electronic nose prototype. Chem. Pap. 72, 527–532 (2018). https://doi.org/10.1007/s11696-017-0310-9
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DOI: https://doi.org/10.1007/s11696-017-0310-9