Selection of the most influential flow and thermal parameters for predicting the efficiency of activated carbon filters using neuro-fuzzy technique
Introduction
Indoor air purification is one of the main strategies to improve indoor air quality (IAQ) [1]. In the air filtration and purification applications, activated carbon or activated charcoal is widely used due to many potential advantages [2]. The applications of activated carbon can be found in a large number of industries, water filters, respirators, solvent recovery systems, etc. [3], [4]. Activated carbon can be used for filtering most organic pollutants and solvents from air through the adsorption process [5].
The aim of this study was to analyze how different flow and thermal parameters of the air conditioning system (temperature, humidity, and air velocity) influence the efficiency of the activated carbon filter for different concentrations of the test pollutant. We used acetone, a volatile organic compound (VOC) frequently present in occupational and residential environments, as a test gas pollutant. Depending on its concentration in air, it can cause different symptoms, from irritations to headaches and dizziness [6], [7], [8], so effective elimination of this compound from air is mandatory.
Numerous studies regarding VOC adsorption were reported in open literature [9], [10], [11], [12], [13], [14], [15]. Benzene, toluene, MEK and acetone were used as prevailing challenge gases. Most of these studies were focused on activated carbon filters with concentrations ranging from a couple of ppm (residential indoor environments) to a few hundred ppm (industrial environments). Additionally, numerous studies were performed for theoretical modeling of fixed-bed adsorption filters. A comprehensive literature review of existing sorbent-based gas filter models used for predicting filter performance for nonindustrial buildings is provided in Ref. [16]. All of these studies were conducted for dry air, despite the fact that parameters of the two most important models for breakthrough curves, Wheeler–Jonas and Yoon–Nelson, are a function of humidity level. This inadequacy was reported in Ref. [17], where a framework was developed for predicting the breakthrough curve of activated carbon filters at low concentrations and different levels of relative humidity, applying accelerated test data. However, the influence of different temperature levels as well as of the simultaneous effect of temperature and humidity on the breakthrough and efficiency of filters has not been analyzed.
The aim of this study was to analyze the magnitude of the effect of different flow and thermal parameters (velocity of gas mixture through the ventilation duct (flow), temperature, humidity, and upstream concentration of challenge gas (acetone)) on the efficiency of active carbon filters. We used a statistical learning approach, based on artificial neural networks, to develop suitable models, which were subsequently used to assess the importance of different flow and thermal parameters. We also used artificial neural networks (ANN) as a substitute for the analytical approach, as ANN offers advantages such as no required previous knowledge of internal system parameters [18]. In this study, we applied the adaptive neuro-fuzzy inference system (ANFIS), which is a specific type of the ANN family, to select the most influential parameters for predicting downstream concentration of acetone in the airstream [19].
Section snippets
Experimental setup
Elements of a filter-ventilation system used in experiments under the different thermal and flow conditions are shown in Fig. 1. Supervisory control and data acquisition (SCADA) system was developed for examining the characteristics of the filter elements. The measurement system consists of sensors and transmitters of physical and nonelectrical quantities, a personal computer, and a source of direct current as a transmitter of power supply. The positions of measuring points on the examination
Evaluating accuracy indices
Predictive performances of the proposed model were presented by root means square error (RMSE), coefficient of determination (R2), and Pearson coefficient (r). These statistics are defined as follows:
- 1)
root-mean-square error (RMSE)
- 2)
Pearson correlation coefficient (r)
- 3)
coefficient of determination (R2)
Conclusion
This study employed a systematic approach to select the most influential parameters for the activated filter outlet concentration prediction by means of the ANFIS methodology. The ANFIS is used to eliminate the vagueness in the information and to produce the best conditions. We used the proposed ANFIS model to convert the complicated multiple performance characteristics into a single multi response performance index. As a result, the prediction methodology developed in this research is useful
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