Improvement of a new acoustic emission analysis technique to determine the activated carbon saturation level: A comparative study

Journal Pre-proof Improvement of a new Acoustic Emission Analysis technique to determine the activated carbon saturation level: a comparative study ´ ˜ Peacok, Harold Crespo Sariol, Jan Yperman, Angel Thayset Marino ´ Sanchez Roca, Robert Carleer, Jeamichel Puente Torres, Guy Reggers, Tom Haeldermans, Elsy Thijssen, Pieter Samyn, Grazyna ´ Garc´ıa Gryglewicz, Lissette Salomon

PII:

S2213-3437(20)30142-1

DOI:

https://doi.org/10.1016/j.jece.2020.103794

Reference:

JECE 103794

To appear in:

Journal of Environmental Chemical Engineering

Received Date:

19 August 2019

Revised Date:

29 January 2020

Accepted Date:

16 February 2020

´ Carleer R, Puente Please cite this article as: Peacok TM, Sariol HC, Yperman J, Roca AS, ´ Garc´ıa L, Torres J, Reggers G, Haeldermans T, Thijssen E, Samyn P, Gryglewicz G, Salomon Improvement of a new Acoustic Emission Analysis technique to determine the activated carbon saturation level: a comparative study, Journal of Environmental Chemical Engineering (2020), doi: https://doi.org/10.1016/j.jece.2020.103794

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Improvement of a new Acoustic Emission Analysis technique to determine the activated carbon saturation level: a comparative study. Thayset Mariño Peacok1, Harold Crespo Sariol1, Jan Yperman2*, Ángel Sánchez Roca3, Robert Carleer2, Jeamichel Puente Torres4, Guy Reggers2, Tom Haeldermans2, Elsy Thijssen2, Pieter Samyn2, Grazyna Gryglewicz5, Lissette Salomón García6 1

Faculty of Chemical Engineering, Applied Acoustic Laboratory, Universidad de Oriente, Santiago de Cuba, Cuba. [email protected]; [email protected] 2

Research group of Applied and Analytical Chemistry, Hasselt University, Agoralaan building D, 3590 Diepenbeek. Belgium. [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected] 3

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Faculty of Mechanical Engineering, Universidad de Oriente, Santiago de Cuba, Cuba. [email protected] 4

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Faculty of Electrical Engineering, Universidad de Oriente, Santiago de Cuba, Cuba. [email protected] 5 Department of Polymer and Carbonaceous Material, Faculty of Chemistry, Wroclaw University of Technology, Gdanska 7/9, 50-344 Wroclaw, Poland. [email protected] 6 Electricity Generator Plant Specialist, Santiago de Cuba, Cuba. [email protected]

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*Corresponding author: Jan Yperman tel.: +32-11-268295; [email protected]

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Acoustic emission analysis (AEA) is a sensitive technique to characterise activated carbons. AEA can quantify the exhaustion level of the different layers in a GAC filter AEA is also an alternative method for nitrogen adsorption (BET analysis) TGA, XRF and Elemental Analysis are used to validate the acoustic emission results AEA offers a solution for monitoring a correct management of GAC application.

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Highlights

Abstract

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A new Acoustic Emission Analysis (AEA) method has been successfully applied to quantify the exhaustion degree in an industrial granular activated carbon (GAC) filter for water treatment in different industrial processes. Five samples of GAC obtained at different depths of the target industrial filter have been evaluated. For the first time the analysis of the acoustic energy and the signal power was applied, besides the processing of signal its frequency, amplitude and integral area under the signal envelope curve. The envelope and energy of the acoustic signal were mathematically processed using Gilbert and Parseval theorems. Additionally, image segmentation techniques are applied to analyze spectrograms in order to determine the exhaustion level of the GAC samples. Acoustic emission results are discussed in terms of CaCO3 adsorption mechanisms in the water filter and correlated with dedicated analytical methods such as TGA, SEM, FTIR, XRF and N2 gas adsorption. The implementation of AEA in 1

engine power plants in Cuba constitutes an interesting approach to implement an improved strategy of GAC management in water treatment industries. Keywords: activated carbon, acoustic emission, porosity, water treatment 1. Introduction

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Activated carbon (AC) is a common term used to describe carbon-based materials which contain well developed internal pore structures [1]. AC is produced from a variety of carbonaceous materials such as wood, coal, lignite, coconut shell, agriculture sources and others for adsorption of pollutants from liquids and gases [2, 3]. The developed surface area, the large and diverse porosity consisting of micro-, meso- and macropores, as well as the presence of a variety of functional groups present on the surface of AC, makes it a versatile material as an universal adsorbent [4]. AC can be used in powdered or granular (GAC) form (0.2–5mm) [5]. They are widely employed for sweetener decolorization, household uses, in food and beverages, in mining and in pharmaceuticals [6]. AC has also been used as one of the most popular and widely applied adsorbent for removing organic and inorganic pollutants from water and wastewater treatment [4, 7]. The main mechanisms by which AC removes contaminants from water are adsorption, ion exchange, complexation and catalytic reduction [8].

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Mostly, water coming from rivers and wells are stored in reservoirs where the water receives a pre-treatment to reach the desirable standard (being a water source for drinking water f.i.). Chlorine is generally the most used chemical oxidant for drinking water disinfection at one or two stage(s) in the treatment process, i.e., for pre-treatment (to induce a primary) and/or for post-treatment (to maintain a residual amount of disinfectant in the distribution system). Chlorine reacts with water according to reaction (1) resulting in hypochlorous acid and hydrochloric acid [9]. 𝐶𝑙2 (𝑔) + 𝐻2 𝑂(𝑙 ) 𝐻𝑂𝐶𝑙(𝑎𝑞) + 𝐶𝑙 − (𝑎𝑞) + 𝐻 + (𝑎𝑞)

(1)

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In small power plants (used in rural regions of developing countries like Cuba), the electricity is generated by engine systems. The heat produced during the combustion reaction in the engine has to be dissipated and generally water is used as cooling medium [10]. Untreated water contains several compounds such as CaCO3, Ca(HCO3)2, MgCO3, Mg(HCO3)2, sulphates, nitrates, phosphates which can affect the cooling systems [11].

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Hypochlorous anions, chloride and metal ions have to be removed from the water before passing into the engine cooling system [12]. Otherwise, corrosion, erosion and insoluble salt precipitation affect the engines by damaging the mechanical structure thus negatively affecting the performance of the engine and consequently the power generation efficiency gets lower. GAC fixed bed contactors (commonly termed as “ GAC filters”) are effectively used to remove several compounds from the cooling water, thus improving its industrial application quality [10, 13] by reducing the water hardness which creates incrustations in the engines [14, 15]. However, when GAC is used for water treatment, the presence of dissolved HOCl in contact with the carbonaceous matrix of the GAC can react according to reaction (2) [16]. Indeed HOCl can act as an oxidant towards AC, forming new functional groups containing oxygen and therefore, gradually decreasing the adsorption performance of the GAC. This reaction occurs very quickly and systematically attacks all layers of the filter in a progressive way. 𝐶 ∗ (𝑠) + 𝐻𝑂𝐶𝑙(𝑎𝑞) → 𝐶 ∗ 𝑂(𝑠) + 𝐻 + (𝑎𝑞) + 𝐶𝑙 − (𝑎𝑞)

(2)

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Where, C*O represents oxidized carbon sites of the GAC, including formation of hydroxyl, carbonyl or/and carboxylic functionalities. Table 1 displays some chemical properties of the input water. This water is under the Cuban standards of drinking water [17] but not according cooling water standard quality, an extra treatment is needed to avoid corrosion and incrustation in the engines. Taking into account the calcium content in water (CaCO3 concentration is within the range of 121 mg/l to 180mg/l) this water is classified as “hard”. One of the purposes of the GAC filter is to reduce the CaCO3 concentration; as it is one of the main incrusting compound in the engines cooling systems [18].

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Table 1. Chemical properties of the inlet water to be treated with GAC before being used in the engine power plant cooling system in Cuba. Chemical Property Value Chemical Property Value Dissolved O2 (mg/l) 2.88 Calcium hardness (mg CaCO3/l) 168 pH 8.3 Magnesium hardness (mg CaCO3/l) 89 Conductivity (µS/cm) 1565 Chloride (mg/l) 44 TDS (mg/l) 780 Total alkalinity (mg CaCO3 /l) 161 Salts (g/l) 0.78 Turbidity (NTU) 19 Total hardness (mg CaCO3 /l) 257

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Specifically in the water treatment plants in Cuba, a GAC water filter is declared as “exhausted” when the quality of the filtered outlet water is out of the specifications according to the defined standard which are just based on the pH and conductivity of the outlet water. However, this is not a good indicator in determining the real exhaustion degree of the GAC contained in the filter bed. Technological limitations in Cuba make it impossible to do better.

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Based on published experiences in the rum industry [24, 25], AEA can be used to accurately determine the exhaustion degree of the GAC and to provide a proper GAC management strategy. Once the GAC filter is declared exhausted, the carbon bed is completely removed and replaced by imported virgin (expensive) GAC and discarded in the nearby area without a proper regeneration or treatment strategy, thus creating an economic and environmental problem [19].

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Nowadays, different analytical techniques allow to characterize different aspects of the AC [5]. However, its complete and differentiated characterization is a major challenge due to the demand of dedicated equipment, expensive reactants, high-tech experimental conditions and is mostly time-consuming [20, 21]. Another disadvantage of many of these techniques is the necessity of a rigorous sample preparation prior to measurement which can induce chemical and textural changes in the AC [22] and are destructive almost in all cases. Acoustic emission analysis (AEA) has been used in applied and fundamental researches for monitoring and controlling processes and materials characterization being regarded as a sensitive and accurate method [23]. According to recent publications [24, 25], the sound spectra produced by harmonic vibrations from bubbles coming from GAC after flooded with an immersing liquid (usually water) have been a satisfactory approach to elucidate the textural and porous characteristics of GAC used in different industrial processes. The GAC characteristic acoustic signal is produced by bubbles escaping from the pores and cracks of the GAC due to the displacement of the adsorbed air in pores and crack of the material when water molecules occupy the accessible porosity.

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During the bubbling process, the bubbles create pressure waves which travel through the fluid and pass through the fluid-air interface into the air where they burst. The simple harmonic oscillator model is similar to a linear spring-damper-mass system. In this bubbling system, the spring forces are provided by internal gas pressure and surface tension, and inertia is due to the effective mass of the surrounding liquid [26, 27]. The process becomes more complex if a massive number of bubbles are produced and suddenly bursting at the same time or coalesce in bigger bubbles producing changes in the frequency and amplitude of the produced sound. Particularly, the acoustic analysis of the bubbling phenomena in different processes has been theoretically and experimentally studied [28]. The bubble oscillator stores potential energy as compressed air and surface tension, and kinetic energy as surrounding fluid vibrations [27]. The relationship of bubbles production and acoustic emission analysis has been applied in different processes and operations such as fluids mechanics, corrosion, sonoluminescense, sonochemistry, cavitation, steam generation, among many others [28-32].

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Specifically in the GAC acoustic signal, the amount and size of produced bubbles during the GAC-water contact influence the acoustic signal parameters which vary significantly according to the textural properties of the GAC thus closely related with the adsorbent [24, 25, 33].

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Therefore, the main objective of AEA application for GAC characterization is to evaluate and predict (in a non-destructive way) the adsorption capacity of the material. When a GAC is exhausted, it presents less and smaller pore volumes because they are filled with adsorbate molecules/compounds. Also the surface area can be covered and pores and cracks can be blocked, thus creating a reduction in the water-accessible locations and as a consequence a decrease in the number of air bubbles and a lower sound production. In this way, by comparing the acoustic parameters of the sound produced by the virgin and exhausted GAC applying the suitable signal processing tools, it is possible to assess the difference in the porous characteristics of the material.

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In previous studies it was reported that the best measurement conditions were obtained in using water as immersing liquid and when the GAC acoustic signal is band-pass filtered at 1.3 kHz [25, 33]. However, different set-ups have been further explored and other frequency ranges were studied to obtain even more specific and accurate information about GAC textural characteristics. Additionally, the cost saving of the AEA technique for GAC characterization is significantly lower if compared with other analytical methods such as gas adsorption or mercury porosimetry: a minimum of glassware is needed, only 90 sec of test execution and almost no sample pretreatment. To perform a BET analysis, high-tech lab facilities are demanded, cryogenic temperatures (77 K) (usually for several hours) is needed. Overnight sample preparation consisting of high-vacuum at 300 °C prior to analysis is demanded. Resulting in more reactants and analysis time consumed by the conventional methods compared with AEA.

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Several studies have demonstrated the potential advantages of analyzing the bubbling process by applying an image analysis method, where the two-dimensional sound event information is naturally represented [34-37]. Due to the stochastic nature of the sound events, characterizing the spectrogram through the image pixel distribution can provide a reliable and alternative way of interpreting the sound information associated to the bubbling process of the AC. This work is focused on exploring the potentialities of the improved AEA to study porous characteristics of the exhausted GACs at different layers of the filter used in a water treatment system. The acoustic results by signal and spectrogram image digital processing are compared/correlated with conventional well established analytical methods such as Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM), Fourier-Transform Infrared Spectroscopy (FTIR), X-Ray fluorescence (XRF), Gas adsorption (N2) to confirm the

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suitability and robustness of the improved acoustic method expanding future applications of this method.

2. Materials and methods 2.1. GAC Samples Five samples of GAC were obtained from a target water filter of an engine power plant which was declared as “exhausted” by the plant specialist. The samples were collected at different locations in the GAC bed: Top (0.0m), 0.2m, 0.4m, 0.6m and Bottom (0.7m) as shown in Figure 1a and collected from different points in each layer but also sampling at different radial locations per layer (Figure 1b) using a specific sampler (under patent registration in Cuba). b)

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2.2. Acoustic emission experiments

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Figure 1. Simplified scheme of a) the water filter and sample locations, b) points in a layer where the samples were collected.

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Acoustic emission set-up: The GAC acoustic signal acquisition was performed under similar conditions as reported in [24, 25, 33]. G.R.A.S.® microphone, Type 46AG (Nominal sensitivity 12 mV/Pa, Frequency range 3.15 Hz - 40 kHz, Dynamic range 17 dB - 146 dB) and a BRANSON® sound enclosure box, to avoid possible external interferences for precision acoustic measurements were used. In addition, the reaction vessel was surrounded with sand to avoid unwanted external noise during the GAC bubbling process. Using a Venti-line series oven, all samples were dried for 3h at 110°C according to ASTM D 2867-04 Standard Test Methods for moisture determination in AC [38]. After drying, samples were kept in a silica-gel desiccator till being measured.

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Figure 2. Scheme of the experimental set-up for acoustic emission signal acquisition: a) acoustic measuring chain; b) detailed view of the sound enclosure box with the sample. Signal acquisition and processing: Following the procedure presented in [24, 25, 33], the GAC sound is acquired by the microphone placed into the sound enclosure box where GAC flooding with water takes place (Figure 2b). The acoustic signal was amplified using a G.R.A.S.® Power Module type 12AQ and digitalized using a NI 9234 data acquisition card. Digital data were recorded using specific software designed in LabView and processed using MATLAB® software. The set-up was calibrated using a G.R.A.S.® 42AP Intelligent Pistonphone. The recording time for all the GAC samples was 90 s.

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Similar methodology was applied for signal processing and analysis as described in [25, 33]. Briefly: spectrogram and components of frequency within the range of 0-25.6 kHz were recorded at empty sound enclosure box to identify the frequency band associated with the external noise. The selected cut-off frequency of the band-pass (BP) filter was set now at 3.525.6 kHz, because at these frequencies more information associated to the bubbling process emerging from the GAC is obtained compared with the first experiments where BP was filtered at 1.3 kHz [25]. A characterization of the acoustic signals within the time domain was possible using the Hilbert Transform (equation (1)) for the vibro-acoustical signals in the frequency ranges of interest [25, 33]. 1

∞ x(t)

H(x) = π ∫−∞ x−t dt

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(1)

An analytical signal is a complex signal consisting of the original signal: x(t), as the real part, and the imaginary part, as the Hilbert transform of the original signal: y(t), where y(t) = H(t).

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A Finite Impulse Response (FIR) filter was used to calculate the Hilbert Transform of the signal. It is multiplied by “i” (the imaginary fraction) and added to the original signal, obtaining a new complex signal: z(t), named the analytical signal (equation (2)) [25]. The envelope of the signal can be found by taking the absolute value of the analytical signal z(t). z(t) = x(t) + i.y(t)

(2)

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The physical meaning of the Hilbert transform helps to gain a much deeper access to the transformation of the sound and is an equivalent to a special kind of a linear filter, where all the amplitudes of the spectral components are left unchanged [39]. It facilitates the formation of the analytical signal, which is useful for analyzing the studied frequency of interest and can be correlated with GAC porous characteristics [25].

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The total energy of the signal was calculated according to the Parseval theorem (with the contribution of Plancherel and Rayleigh [40]) which takes into account the energy conservation law and being useful for non-periodic signals like the GAC acoustic signal [41]. Based on the Parseval theorem, no energy in the signal is lost in the process of performing a Fourier transform [42]. The Parseval theorem often is written as equation (3): ∞

𝐸𝑥 = ∫−∞|𝑥(𝑡)|2 𝑑𝑡

(3)

It can be confirmed that the energy rate based AE parameters, measured in the time and frequency domains (reals), were very suitable to represent the AE signals [43]. By the Parseval theorem the results will always have the same signal energy as the sum of energies of the components [44]. Power is an useful feature in detecting changes in an acoustic signal [45]. For an amplifier output signal with a specific length (x), the total average power (P) can be calculated in the time domain by equation (4):

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∑𝑛

𝑥2

𝑃 = 𝑖=0 𝑛 where n is the number of samples in the signal length.

(4)

2.3. Color segmentation over frequency spectra obtained with AEA Several color segmentation indexes have been proposed and applied to separate the color components in a digital image with reliable results: excess green (ExG), excess red (ExR), and excess blue (ExB) [46-48]. All the mentioned indexes need to fix a threshold for final segmentation (black and white segmentation or binarization).

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The mentioned indexes are applied in order to find a new color index to separate the yellow color in the frequency spectrum images from the AEA. The yellow color is one of the most abundant colors in the GAC frequency spectra, and is usually used to represent the medium power components, while the red color is used to represent the high power frequencies. In general, the sound energy is showed in the spectrogram using yellow and red colors, enclosing the relevant sound information. The segmentation of the yellow color has the advantage of taking into account the red color already present in the original spectrogram (see equation 9).

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Therefore, the color layout approach (yellow) could be applied to the grey-scale intensities to characterize the distribution of spectral information in the image pixels. The spectrogram can be easily normalized into a grey-scale image after the yellow segmentation by scaling the dynamic range of the spectral information into the [0, 1] range, “0” being the lower value (representing the black color) and “1” representing the higher value (white color).

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Spectrograms (frequency spectra) are time-frequency distributions obtained from an acoustic signal, and are represented by images. To extract information from these images it is necessary to apply the correct segmentation method. In this work the yellow color index (ExY) was selected to obtain reliable results from the intensity of the sound (produced by GAC flood into water) reflected in the spectrograms and has its basis on the combination of three indexes for color extraction (ExG, ExR and ExB). The three color components are separated according to equations (5): imR imG imB ,G = ,B = imR 𝑚𝑎𝑥 imG𝑚𝑎𝑥 imB𝑚𝑎𝑥

(5)

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R=

Where:

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imR, imG and imB are the separated color components of the images red, green and blue. imR 𝑚𝑎𝑥 = imG𝑚𝑎𝑥 = imB𝑚𝑎𝑥 = 255 for 8 bits color images.

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Then the color indexes can be calculated using equations (6, 7, 8) [46, 47, 49]: ExG = 2G − R − B

(6)

ExR = 1.4R − B

(7)

ExB = 1.4B − G

(8)

Finally, the yellow color can be calculated using the equation (9): ExY = 𝐸𝑥𝐺 + 𝐸𝑥𝑅 − 𝐸𝑥𝐵

(9)

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The ExY color index is calculated taking into account the natural origin of the yellow color as the sum of the green color and the red color. In order to obtain a better segmentation, the ExB matrix (blue) is subtracted from the combination of the ExG and ExR indexes. The quantification of the amount of yellow color in the black and white image after binarization was calculated using the amount of pixels in “1” from the black and white image histogram. Pixels in “1” in the grey scale directly represent the amount of yellow in the original images after a complete segmentation process.

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Different classical segmentation algorithms were applied to grey scale images obtained after applying “yellow” segmentation on original spectrograms for optimal black and white segmentation. The first threshold to develop the segmentation was selected using the image histogram. In this case, the selection of the threshold must be done manually [37]. A second threshold was selected using Otsu’s method which is based on the histogram of the grey scale image, selecting the threshold value that maximizes the variance between classes [50, 51]. A third threshold was obtained using the iterative version of the Otsu’s method [50, 51]. Figure 3 displays a general segmentation strategy.

Select the more effective binarization method

Spectrogram “Original” RGB image”

Extraction of the yellow color “ExY”

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Fit binarization results with AEA parameters

Perform the grey segmentation

Binarize the image (black and white)

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Combination of ExG, ExR and ExB

Select the best acoustic parameters

Determine white pixels (Pw)

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Test several Binarization Methods

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Figure 3. Segmentation strategy for the selection of the best binarization method. Mathematical data processing to obtain the acoustic parameters, frequency spectra and to develop the segmentation strategy was done using MATLAB® software.

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2.4. Thermogravimetry (TGA)

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TGA curves were obtained using a Q 500 Thermogravimetric Analyzer. About 10mg of AC is pyrolyzed under approximately 35ml.min-1 N2 gas flow at a heating rate of 20°C.min-1 from room temperature to 600°C. The inert atmosphere was switched to O2 after 30 sec during an isothermal period of 5 min at 600°C and subsequent heating to 800°C at 20°C.min -1. The sample weights used for TGA analysis are as follows: GAC-Top 12.035mg, GAC-Bottom 11.383mg and GAC-Virgin 9.433 mg. 2.5. Ash content determination ASTM E 1755 – 01Standard test method for ash in biomass [52] was used for the determination of the ash content by dry oxidation at 575 ± 25°C for a minimum of 3h by incineration in a muffle oven. In addition, the samples were heated until 800°C for 3h for total oxidation. The experiments were performed in triplicate. 2.6. Scanning Electron Microscopy (SEM)

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The surface morphology of the carbons was analyzed using a HITACHI-TM3000 Scanning Electron Microscope at accelerating voltages of 15kV. Standard experimental conditions were applied and a magnification of x250 (300 µm) was used. An emission current of approximately 46 µA and a filament current of 1.85 mA were used for all analyzed samples. To analyze the characteristics of the resultant SEM images, they were filtered using a low pass Gaussian filter to attenuate the high frequency components in the image and to highlight the low frequency components. The histogram of a grey-scale for each image (based on the equation (10) [51, 53]) was obtained. Hence, grey level indicates the state of the GAC surface since it provides information about the intensity and the total of pixels in the images.

ℎ(𝑟𝑘 ) = 𝑛𝑘

(10)

Where:

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𝑟𝑘 : normalized grey-scale intensity level in the discrete interval of [0; 1] ∴ [black; white] 𝑛𝑘 : number of pixels at the 𝑟𝑘 grey-scale intensity

The GAC surface with available pores and cracks in that space presents a value closer to “zero” in a normalized grey scale. On the contrary, if the surface is saturated or loaded (adsorbed compounds), the grey level decreases closer to “one” in a normalized grey scale.

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2.7. Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR)

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2.8. X-Ray Fluorescence (XRF)

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ATR-FTIR spectra of the GAC samples were obtained using a Bruker Vertex 70 FTIR spectrometer equipped with a DTGS detector. The particles size of the samples was <125µm. FTIR spectra were recorded at a resolution of 4 cm-1, with 32 scans per sample and an aperture setting of 6. A recorded background spectrum was subtracted from the spectrum for each sample.

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2.9. BET analysis

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XRF experiments were performed using a Bruker S4 Explorer dispersive XRF spectrometer, 1000W. Powdered samples <125µm were measured in a He atmosphere. The observed intensities of the analyzed samples were compared to a set of universal calibration standards. The concentration of the elements in the samples was obtained applying a correction to the sample matrix (for carbon) and using the EVAL-software for this purpose (standard less calibration).

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Prior to analysis, the GAC samples were degassed 16h at 300°C in high-vacuum. Gas sorption experiments were performed in an Autosorb-iQS using nitrogen (77K) as probe gas. To determine the apparent surface area (S BET) BET equation was used and for pore volume determination the amount of gas adsorbed at the relative pressure of p/p0=0.96 was measured. The micropore volume (VDR) was calculated by applying the Dubinin-Radushkevich equation [54]. 3. Results and discussion 3.1. Acoustic Emission Analysis (AEA) Figure 4 shows the sound frequency distribution from 1-25.6 kHz for the GAC using different types of vessels: Erlenmeyer (4a), beaker (4b) and petri dish (4c). In this range, some frequency components are not related directly with the GAC bubbling sound and actually represent resonant frequencies which depend on the type and shape of the vessels used to perform the 9

experiments. Registered frequencies lower than 1 kHz are associated to unavoidable perturbations from the environment and thus digitally filtered/eliminated during the measurements. The same amount of GAC and water was used for all the experiments. According to Figure 4, the amplitude of the frequency components of the signal for the Erlenmeyer flask is significantly higher than frequency components of the other vessels. In this case, the Erlenmeyer flask acts as a sort of amplifier of the GAC bubble sound because of its cone-shape feature. Indeed, the narrower mouth can canalize all the produced acoustic vibrations directly to the microphone. As demonstrated by Figure 4, it can be deduced that around a 10 times higher sound amplitude is registered using the Erlenmeyer flask compared to the petri dish and around 40 times higher than the beaker using the frequency range of 1-25.6 kHz.

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Another important observed aspect from Figure 4 is that each vessel reveals its own characteristic frequency distribution pattern. The pattern in the range of 1-2 kHz for the petri dish is quite different from the one found for the Erlenmeyer flask and the beaker. However, high frequencies are present independently of the type of vessel used and dominate the frequency distribution in the GAC bubbling process in all the cases. On the other hand, due to the shape-amplification effect, the Erlenmeyer flask is preferred to perform the experiments.

a)

Region analyzed 3.5-25.6kHz Bubbles diameter 0.2-1.56mm

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As reported in previous published papers [25], frequency band at 1.3 kHz were initially used to determine the exhaustion level of different GAC using the Erlenmeyer flask. However, according to Figure 4, it is found that this frequency represents rather indirect information of the GAC bubbling sound, which can be attributed to resonant frequency, formed during the bubbling process and transmitted through the vessel body. Therefore, considering that the higher registered frequency bands are invariantly present in the entire vessel used, high-frequency bands constitute a better representative region of the actual physical phenomenon of bubbling process.

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Figure 4. Frequency distribution from 1 to 25.6 kHz for GAC-Virgin using different vessels: a) Erlenmeyer, b) Beaker, and c) Petri dish.

𝑓=

1

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3𝛾𝑃 2 ( ) 2𝜋 𝜌𝑟2

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Indeed, the fact that the formed bubbles sound is represented at the higher frequency range can be explained by the Minnaert model given by equation (11) [55]: (11)

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Where 𝑓:frequency, Hz 𝛾:ratio of specific heats of gas inside the bubble, dimensionless 𝑃:absolute liquid pressure, kPa 𝜌:liquid density, kg.m-3 𝑟: bubble radius, m

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According to a previous publication [56], the diameter distribution of the bubbles coming from GAC using glycerol as immersing liquid was in the range of 1.56 to 0.15 mm. The fitted Gaussian distribution model showed a maximum bubbles distribution of diameter around 0.3mm. Considering physical-chemical differences between water and glycerol, more bubbles with different diameter distribution can be found using water as immersing liquid. Taking glycerol as the first approximation to the bubbles size distribution in water and using the Minnaert equation, the analyzed frequency region of interest must be 3.5-25.6 kHz (25.6 kHz is the maximum frequency that can be detected using NI MI-9234 data acquisition card). According to equation 11, for 1.3 kHz bubbles of around 5mm in diameter are formed, thus referring to a coalescence process of the original formed small bubbles. According to the above discussion, the frequency range now selected is from 3.5 to 26.5 kHz (allocated in Figure 4 (a) for an Erlenmeyer vessel) corresponding to bubbles smaller than 1.56 mm in diameter and original formed. Figure 5a shows the acoustic emission signals generated by the GAC samples at different layers in the water filter bed (Figure 1) and the GAC-Virgin. It can be observed that the sound amplitude increases from the samples GAC-Top to the GAC-Bottom, also the amplitude registered at different frequencies (Figure 5b). The GAC-Virgin presents the highest signal intensity as expected for a virgin material with the highest porosity degree. This is in line with the amount and size of bubbles produced which change the sound pattern, the frequency and increase the energy, the power and the amplitude of the signal in comparison with the other studied GACs [24]. Based on the Minnaert equation (equation 11), the smaller the bubble is, the higher the frequency. The peak at 1.7 kHz corresponds to bigger bubbles formed during the bubbling 11

process by coalescence. The amount of these bigger bubbles increases in amplitude from GACTop to GAC-Bottom reaching the highest value for the GAC-Virgin. Apart of the coalescence effect during the chaotic bubbling process, bigger bubbles can also be formed due to air displaced from wider pores, slits and cracks in the carbon particle. The power spectra of the signal in the studied frequency range (Figure 5c) also presents the same trend between the GAC samples at different layers in terms of amplitude, indicating that the GAC-Top is the most exhausted GAC layer in the water filter. 3

x 10

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a)

b)

c)

Figure 5. Evolution of AE signal generated by GAC samples at different layers in the water filter (filtered range 3.5-25.6 kHz) (a) AE signals; (b) Frequency component distribution and (c) Power spectra. Figure 6a shows the signal envelopes for the extreme layers in the carbon bed: GAC-Top and GAC-Bottom and for GAC-Virgin for five replicate experiments. It can be observed that the signal envelope shape is similar between samples. The GAC-Virgin presents more free/available pores which correspond to an increment of bubbles produced. These massive bubbles formation changes the sound pattern, thus producing a stronger sound and therefore increasing the signal parameters such as integral area under the signal envelope curve (sound surface (SS)), envelope maximal peak (EMP), energy (E) and power (P).

-p

ro of

The more produced bubbles, the higher are the signal parameters. GAC-Virgin presents the highest EMP, being around 10 times higher than for the most exhausted GAC (GAC-Top). The GAC-Bottom presents an EMP higher than GAC-Top which can be attributed to its less exhausted degree in terms of water-accessing pores, thus producing more bubbles than the GAC-Top sample. Additionally, the signal envelope for the GAC-Top presents a lower and relatively wider peak (Figure 6a) in comparison with the other samples, which suggests a less and “delayed” bubbling process. This behavior of the signal envelope of the GAC-Top can be explained by the exhausted condition of this GAC where the amount of blocked/occupied pores is higher and consequently, not only less available pores are present but also more difficult to water intrusion.

b) ))

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a)

Figure 6. Signal envelopes curves of: a) five experiments for the GAC-Top, GAC-Bottom and GAC-Virgin samples and b) all GAC layer samples. (Signal filtered from 3.5 to 25.6 kHz).

Jo

ur

Figure 6b displays the signal envelopes of the GAC sound filtered from 3.5 to 25.6 kHz of all GAC layer samples. Analyzing the shape of the signal envelopes two main stages can be identified. First, the sudden water flooding process produces a huge amount of bubbles of different sizes (high signal amplitude) which corresponds to the main peak amplitude in the curve. Afterwards, gradually the amplitude decreases, less bubbles are produced trending to an asymptotical behavior in the time. For the GAC-Top sample (most exhausted), the signal envelope curve practically describes a flat trend after 20s, indicating a lack of bubbling activity and thus confirming its exhaustion degree. The other samples remain bubbling for longer (asymptotical trend after 40s) suggesting a lower exhaustion degree. The peak amplitude in Figure 6b clearly suggests a systematic growing order in the exhaustion level from GACBottom to GAC-Top which is in line with the fixed bed adsorption mechanism. Based on AEA, the revealed exhaustion order is the following: GAC-Top> GAC-0.2> GAC-0.4> GAC-0.6> GAC-Bottom>> GAC-Virgin. Similar behavior is observed in Figure 7 which represents integral area under the signal envelope curves (SS) for all analyzed samples. It is noticeable, that from the GAC-Top the SS 13

parameter increases to the highest value found for GAC-Virgin where the maximal peak value is observed within the first 10s for all cases, this is associated with the sudden bubbling process in a fast air removal from the pores and cracks nearby the GAC surface. From 10 to 40s the SS values decrease as a consequence of a drastic reduction of formed bubbles because of less available superficial pores and just the more difficult accessible pores and cracks remaining bubbling. When all the air is displaced by the water from the GAC pores and cracks, a similar trend for all the GAC samples can be noticed in the SS curves describing a practically constant value after 40s. 1.6 Top 0.2m 0.4m 0.6m Bottom Virgin

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Figure 7. Integral area under the envelope curve (SS) as a function of time for all the studied samples.

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Figure 8a shows the averaged energy of the signals for the GAC-Top, GAC-Bottom and GACVirgin samples for five replicated experiments. The shape of the energy curves for each sample obeys a similar trend. It can be observed that the highest energy value is obtained for the GACVirgin sound, followed by GAC-Bottom and GAC-Top with acoustic energy values around 10 times higher for the GAC-Bottom and 60 times higher than GAC-Top (the most exhausted).

b)

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a)

Figure 8. Signals energy of: a) five similar experiments for the GAC-Top, Bottom and Virgin samples and b) all GAC layer samples. Signals were filtered in the range of 3.5 to 25.6 kHz. Figure 8b displays the energies associated to the sound produced by all GAC layer samples. It is clearly noticeable how energy gradually increases from the GAC-Top to GAC-Bottom. GACBottom presents the highest sound energy in comparison with all the other GAC samples, thus suggesting more available pores. GAC-Bottom revealed an acoustic energy eight times higher than the energy produced by GAC-Top. Figure 9 displays the signal power distribution of different bubble diameter ranges calculated by applying the Minnaert equation from the frequency distribution spectra in the range of 3.5-26.5 14

kHz (Figure 4) for the different GAC layer samples. As observed in Figure 9, there is an increasing trend in signal power from GAC-Top to GAC-Bottom at all bubble sizes but with a quite similar bubble diameter distribution. The bubbles with diameter of 0.25-0.35mm dominate the bubble size distribution in all the cases. Almost 70% of the produced air bubbles are smaller than 0.43mm in diameter. According to the signal power distribution, the amounts of produced bubbles confirm the following exhaustion order in the water filter GAC-Top> GAC-0.2> GAC0.4> GAC-0.6> GAC-Bottom. GAC-Top GAC-0.2m GAC-0.4m GAC-0.6m GAC-Bottom

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57

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Diameter Range, mm

Figure 9. Signal power distribution of different bubbles diameter ranges according to AEA for the different GAC layer samples (signal was filtered in the range 3.5-25.6 kHz).

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Table 2 summarizes the averaged signal parameters at each GAC layer sample. Integral area under the signal envelope curve (SS), amplitude of the envelope maximal peak of the signal (EMP), sound energy (E) and signal power (P). All the parameters were obtained after signal filtering within the selected frequency range of 3.5-25.6 kHz. Table 2. SS, EMP, E and P values of the signal found with the AEA. GAC-0.2m 1.439 0.052 3.61 0.073 0.015 20.5 3.4 0.2 5.88 3.75 0.24 6.40

GAC-0.4m 2.165 0.076 3.51 0.087 0.013 14.9 6.3 0.3 4.76 6.98 0.22 3.15

na

GAC-Top 0.855 0.096 11.2 0.043 0.004 9.30 1.2 0.08 6.67 1.31 0.09 6.87

ur

Sample ̅̅̅(V.s) SS σ(SS) VC (%) ̅̅̅̅̅̅(V) EMP σ(EMP ) VC (%) ̅(V2.s) 102 ∙ E 2 10 ∙ σ(E) VC (%) 104 ∙ ̅ P ̅) 104 ∙ σ(P VC (%)

GAC-0.6m 2.255 0.078 3.46 0.110 0.009 8.18 7.3 0.4 5.48 8.12 0.46 5.67

GAC-Bottom 2.308 0.123 5.33 0.158 0.008 5.06 8.8 0.3 3.41 9.83 0.48 4.88

GAC-Virgin 4.667 0.113 2.42 0.445 0.036 8.09 74.8 2.7 3.61 84.1 0.47 5.59

Jo

Method: 95.0 percent Lower Significant Difference (LSD)/SS, EMP, E and P are the mean of SS, EMP, E and P values of five independent experiments; σ (standard deviation), CV(%): coefficient of variation.

Based on statistical analysis of the Multiple Comparison Method (Fisher’s Lower Significant Difference (LSD)), significant differences between averaged acoustic parameters of GAC samples were found. In all cases, there is an increasing trend from GAC-Top to GAC-Bottom. The GAC-Virgin presents the highest values for all the parameters due to more free available water accessible pores and cracks resulting in a more intense bubbling sound. Figure 10 shows the exhaustion profile of the GAC layer samples based on the AEA results considering SS and EMP (Figure 10a) and E and P (Figure10b) as acoustic parameters. Each acoustic parameter used to evaluate the exhaustion level of the different GAC samples was fitted to a linear or polynomial second order model according to the layer depth. The fitting 15

parameters are shown in Table 3. A regression coefficient higher than 96% was found for all the explored models. The signal envelope curve SS and amplitude of the envelope maximal peak of the signal (EMP) obey satisfactorily a second order polynomial and a linear fitting respectively. The signal energy E and power P plots are quite good described by the linear models (regression coefficient over 99 %). Therefore, the robustness of E and P as acoustic parameters to describe the exhaustion degree of the GAC sample is confirmed and to be preferred above the other two parameters. 0.10

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b)

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Figure 10. Exhaustion profile in the water treatment filter based on acoustic parameters: a) SS and EMP; and b) E and P. Table 3. Fitting parameters for SS, EMP, E and P curves as function of the layer depth found in the water filter.

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Model Polynomial Linear Parameter SS; V.s EMP; V E; V2.s 0.82(±0.12) A 1.50. 10−2 (±0.15. 10−2) 1.37. 10−2 (±0.39. 10−2 ) 4.34 (±0.81) 0.19(±0.03) 0.11 (±0.01) B1 -3.16(±0.03) B2 0.981 0.960 0.991 R2 𝑦 = 𝐴 + 𝐵1 𝑥 and 𝑦 = 𝐴 + 𝐵1 𝑥 + 𝐵2 𝑥 2 ; uncertainty between brackets

P; W 1.48 .10−4(±0. 04 .10−4 ) 1.19 .10−3(±0.09 .10−3) 0.991

na

3.2. Color segmentation over frequency spectra

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In Figure 11a, the recorded acoustic spectrograms for all the studied GAC samples are shown. The yellow color (yellow contains red color according to equation 9) was selected from the spectrograms to characterize the bubbling potential coming from the GAC sound. More yellow in the image, corresponds to a higher sound intensity. From the original image of the acoustic spectrogram (Figure 11 a), a grey scale segmentation was deduced (Figure11b). x 10

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Figure 11. Acoustic spectrograms evolution of AE signal generated by GAC samples: a) original acoustic frequency spectra, b) grey segmentation (image obtained after applying the yellow color segmentation) and c) binary images (by applying Otsu method).

Jo

The selection of the most adequate method for the black and white segmentation was developed according the fitting parameters between the data obtained in the total amount of pixels in “1” (white pixels) (Pw), taken from the binary image histograms (referred to black and white segmentation) after the yellow segmentation (calculated by equation 12), and using E and P as more suitable acoustic parameters. Table 4 shows the linear fitting values found for each method used to obtain binary images from the original yellow spectrograms in correspondence with the energy and power of the acoustic signal. 𝑁−1 𝑃𝑤 = ∑𝑀−1 𝑚=0 ∑𝑛=0 𝑘(𝑚, 𝑛)

(12)

Where: k (m,n): grey scale intensity value of the black and white image in a pixel at the (m,n) image point. m: total number of rows in the image. n: total number of columns in the image. 17

Table 4: Fitting correlation parameters for a linear model found for the E and P of the AE signal for all samples (filtered from 3.5-25.6 kHz) and PW for each binarization method applied to the original sound spectrograms. Acoustic A parameter E 2.9 ∙ 10−3 (±0.3 ∗ 10−3) Image histogram P 2.7 ∙ 10−5 (±0.3 ∗ 10−5) E -8.4 ∙ 10−3 (±0.3 ∗ 10−3) Otsu P -9.9 ∙ 10−5 (±1.3 ∗ 10−5) E -6.4 ∙ 10−3 (±0.5 ∗ 10−3) Iterative Otsu P -7.7 ∙ 10−5 (±0.9 ∗ 10−5) 𝑦 = 𝐴𝑥 + 𝐵1 ; between brackets: Uncertainty. y = PW and x = E or P Method

B1

R2

1.5 ∙ 10−2 (±0.1 ∗ 10−2) 1.7 ∙ 10−4 (±0.7 ∗ 10−4) 1.1 ∙ 10−2 (±0.2 ∗ 10−2) 1.3∙ 10−4 (±0. 3 ∗ 10−4 ) 1.0 ∙ 10−2 (±0.2 ∗ 10−2) 1.1 ∙ 10−4 (±0.2 ∗ 10−4)

0.954 0.956 0.983 0.983 0.987 0.986

A comparison between the correlation coefficient for all applied methods over the segmentation process reveals that the Otsu and the Iterative Otsu methods show the best results with the highest regression coefficients (higher than 98%) for E and P respectively.

ro of

Otsu method is based on computing the image histogram by finding the threshold value that maximizes the variance between classes. In contrast, Iterative Otsu method uses a recursive algorithm in order to find the needed threshold to binarize the image. This results in a longer computation time and are not significantly different [51]. Hence, Otsu method was selected to perform the image analysis of the acoustic spectrograms which is presented in Figure 11c.

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Figure 12. Linear correlation between P w from the image histograms (Otsu method) and E and P as parameters of the acoustic signal (bandpass filtered in the range 3.5 to 25.6 kHz) for the different GAC layer samples. Considering the total number of yellow pixels (white color in the binary images) present in each spectrogram P w, a linear trend can be obtained for the different GAC layer samples (Figure 13). Fitting parameters are also displayed within the figure.

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Figure 13. Correlation between Pw parameter and GAC layer depth (yellow segmentation) using the Otsu method.

3.3. TGA

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Figure 14 displays the TGA results in nitrogen and oxygen atmospheres for three samples: GAC-Virgin, GAC-Top and GAC-Bottom. In the TGA curves, from 25 to 180°C weight losses of 1.12%, 4.88% and 5.42% for GAC-Virgin, GAC-Top and GAC-Bottom respectively were found, which can be attributed to the moisture content. From 180 to 600°C, almost no further loss in weight can be noticed for GAC-Virgin. Contrary to GAC-Top and GAC-Bottom larger weight losses are found between with 11.8% and 10.9% respectively. These weight losses can be associated with some thermal degradation of the used GAC oxidized during water treatment by building in C-O functionalities. Upon heating these functionalities induce further release of water, but also of CO and CO2 and the in situ formation of CaCO3. These phenomena are not observed for GAC-Virgin just revealing the lowest weight loss of 1.9%.

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Above 600°C after switching into an oxygen atmosphere, a significant but expected weight loss was found for all samples: GAC-Top, GAC-Bottom and GAC-Virgin with values of 57.7, 60.9 and 93.5% respectively, related with the oxidation of the carbon matrix. For GAC-Top and GAC-Bottom, an extra weight loss is noticed attributed to the presence of CaCO3 decomposing into CaO(s) and CO2 (g). This was not found for the GAC-Virgin sample with a final ash content of 3.45%. Additionally, a difference in the ash content was found between GAC-Top and GAC-Bottom at 750°C of 25.59% and 22.70% respectively. These differences in the ash content between virgin and exhausted GACs can be related for the last to the precipitation, adsorption and complexation of mainly Ca-ions on/at the GAC and also some other inorganic pollutants (clay, silica, Fe, Mn) present in the water which are blocking and occupying pores and slits in the GAC structure [58].

19

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Figure 14.TGA and DTG curves from 25 to 900°C for GAC-Virgin, GAC-Top and GACBottom, including the temperature profile.

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Table 5 shows the differences in ash content (%) for GAC-Top, GAC-Bottom and GAC-Virgin. Comparing the results from two different ash determination methods: ASTM E 1755 – 01 “Standard test method for ash in biomass” [52] and TGA (residual mass left at 575 and 800°C) no significant differences in results from both methods can be found.

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Table 5. Ash content (in wt.% ) obtained from TGA and ASTM E 1755 – 01 at 575 and 800°C. Sample Method Temperature GAC-Top GAC-Bottom GAC-Virgin TGA 37.81 29.04 3.45 575°C ASTM E 1755 – 01 37.96 28.95 3.39 TGA 25.59 22.70 3.39 800°C ASTM E 1755 – 01 25.65 22.39 3.33

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Using ASTM E 1755 – 01 method, the ash content at both temperatures (575 and 800°C) for GAC-0.2m, GAC-0.4m and GAC-0.6m was also determined: 35.55, 33.88 and 30.29 wt.% at 575°C and 25.03, 24.16 and 22.76 wt.% at 800°C respectively. Based on the ash content of the samples, the same trend in the GAC exhaustion profile can be found. According to the ash content, GAC-Top is the most exhausted AC with the highest amount of ash thus indicating the presence of more inorganic ions removed during the water purification process. Figure 15 and Table 5 show the ash content at 575 and 800oC using ASTM E 1755 – 01 method versus the GAC layer depth. A linear fitting is found in both cases (correlation coefficients over 99%). The same linear trend is observed in Figures 10 and 13 pointing to a close relationship between the AEA and ash content, which can indeed be used to quantify the exhaustion level of the used GAC.

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Figure 15. Ash content versus GAC layer depth obtained at 575 and 800 oC using ASTM E 1755 – 01 method. Table 6. Fitting parameters of ash content versus GAC layer depth obtained from ASTM E 1755 – 01 at 575 and 800°C. Temperature

A 38.21(±0.42) 575 C 25.85(±0.19) 800oC 𝑦 = 𝐴 + 𝐵1 𝑥 ; uncertainty between brackets

B1 -12.86(±0.92) -4.87(±0.40)

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R2 0.992 0.990

%𝐶𝑎𝑂 =

%𝐶𝑂2 ∙ 𝑀𝐶𝑎𝐶𝑂3 𝑀𝐶𝑂2

(13)

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%𝐶𝑎𝐶𝑂3 =

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Figure 16 a and 16 b show the found correlation between the ash content at 575 and 800oC with the %CaCO3 and the %CaO respectively calculated by loss of %CO2 (obtained by ASTM E 1755 – 01) according equations 13 and 14. Linear fitting parameters are presented in Table 7.

%𝐶𝑂2 ∙ 𝑀𝐶𝑎𝑂 𝑀𝐶𝑂2

(14)

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In both cases, an increasing trend in the concentration of calcium compounds from GACBottom to GAC-Top was observed being in line with the expected GAC exhaustion profiles.

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Ash content (575 C)

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22

Bottom

23

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25

26

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Ash content (800 C)

Figure 16. Correlation between ash content: a) at 575oC and CaCO3, b) at 800oC and CaO. 21

Table 7. Fitting parameters of ash content (ASTM E 1755 – 01) at 575 and 800°C and the % of CaCO3 and CaO in the GAC layer samples. Temperature

A -26.0 (±1.47) CaCO3 -37.80 (±.019) CaO 𝑦 = 𝐴 + 𝐵1 𝑥 ; uncertainty between brackets

B1 1.42(±0.04) 2.07 (±0.17)

R2 0.9986 0.9904

At 575°C, Ca2+ is present as well as CaCO3 and complexed Ca2+. This behavior matches with the FTIR spectra from both GAC-Top and GAC-Bottom as will be discussed later. At the beginning of the water treatment process, Ca2+ precipitates as carbonate at the GAC-Top layer, thus, higher concentration of this ion is found in the top sample compared to the bottom sample. But also complexation reactions take place due to the initial oxidation of the different layers of the GAC by residual HOCl. As a result, a mixture of CaCO3 and sequestrated Ca (complex) in a changing ratio with different concentrations in all layers is found.

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Since adsorption is a dynamic process in which a chemical equilibrium depends on the composition of the input water, the initial precipitated CaCO 3 of the upper layers can partly solve again in this continuous process depending on the water composition and pH. At lower GAC layers this will have an impact on the calcium ion behavior in view of new and changing complexation/precipitation equilibrium reactions.

Ca2+ + C-O-functionalities ; Ca2+ complexes

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e.g.: CaCO3 + CO2 + H2O ; Ca(HCO3)2 ; Ca2+ + 2 HCO3-

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From the other side, calcium precipitation in the top GAC layer will prevent further oxidation by residual HOCl and complexation is more dominant in the profounder layers. This observation is also in agreement with the different slopes found in linear models presented in Figure 15 and Table 7.

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The mass losses in the temperature range 170-575°C are almost the same for GAC-Top and GAC-Bottom sample (see Figure 14). Therefore, the conversion products of Ca2+complexes after thermal treatment can be associated to CaO instead of CaCO 3.

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Ca2+ complexes can be estimated from the difference between the amount in ash content at 800°C (Delta = % ash - % CaO) (all Ca2+ is converted into CaO independent of the original species) and the CaO calculated from equation 14 (in this case the other ions were omitted because these are present in a quite lower concentration). Figure 17 presents the ash content at 800 °C, the CaO content and its Delta-values for all the GAC layer samples. The highest concentration of complexed Ca2+ is indeed found at GAC-Bottom.

22

40

o

Ash content, 800 C CaO content Delta

30 25.65

25.03

24.16

%

22.76

22.39

20 15.69 13.41 11.62

9.96

9.60

0

0.2 0.2m

0.0 Top

8.36

0.4 0.6 0.6m 0.4m GAC Layer

0.8 Bottom

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10

14.03

13.16

12.39 11.77

Figure 17. Ash content at 800oC, CaO content and Delta-value (for all the GAC layer samples).

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According to the plots presented in Figure 18 and the correspondent fitting parameters shown in Table 8, a polynomial correlation between the acoustic parameters E and P with the ash content (800oC) of the GAC samples can be noticed. The found correlation can be used to fairly estimate the exhaustion degree of the adsorbent material and to rapidly monitor the AC exploitation process.

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Table 8. Fitting parameters for̅̅̅ E and ̅P found with the AEA and the ash content obtained by TGA. 800oC Parameter

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̅𝐄; V2.s ̅𝐏; W -2.7(±0.2) A -2.9.10−2(±0.2. 10−2 ) 0.25(±0.002) B1 2.6. 10−3(±0.7. 10−3 ) B2 -6.1. 10−5(±0.4. 10−5 ) −3 −3 5.6.10 (±0.3.10 ) 0.9728 0.9724 R2 𝑦 = 𝐴 + 𝐵1 𝑥 + 𝐵2 𝑥 2 ; error of the parameter between brackets

9

Jo

2

0.04

12

6

0.00

Top

-4

Energy; V .s

Bottom

Power (10 ); W

E P

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0.08

15

3

-0.04 22

23

24

25

0 26

o

Ash content (800 C)

23

Figure 18. Correlation plots between the ash content (800oC) with acoustic parameters Energy ̅) and Power (P ̅) for all the GAC layer samples. (Acoustic signal was filtered from 3.5 to 25.6 (E kHz) 3.4. SEM In the SEM images of the grain topography of GAC-Top (Figure 19a) it is noticeable that the GAC surface is covered by deposits of adsorbed/precipitated compounds (white spots) which are blocking the pores, the slits and the cracks of the material. In contrast, in the case of GACBottom (Figure 19b) it is noticeable that just fewer pores are occupied/blocked by compound deposits compared with the GAC-Top. For GAC-Bottom, a higher number of unoccupied external pores are still visible presenting a variable morphology and size. However, the GACVirgin (Figure 19c) shows a clean surface and more porous structure. b)

c)

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Figure 19. SEM images of the external grain topography of different GAC samples a) GACTop; b) GAC-Bottom; and c) GAC-Virgin.

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An analysis of the total pixels in the SEM images through a histogram in a grey-scale for the GAC samples (Figure 20) was performed in order to quantitatively compare the loading and textural differences in the GAC samples from the SEM images using dedicated digital images processing tools already reported in [59]. Based on histograms of Figure 20, the GAC-Top sample presents the largest amount of pixels at higher normalized grey scale indicating the highest concentration of adsorbed/complexed compounds thus producing changes in the reflected energy during the SEM analysis. Therefore, the higher the concentration of blocked pores and cracks due to adsorbed/complexed compounds in the GAC, the higher value in the grey scale histogram peak is obtained [59]. In the case of GAC-Bottom, the total of pixels of the histogram peak is lower and is displaced in the grey scale to the black zone (0) in comparison with the GAC-Top sample, indicating a lower exhaustion level and hence less compounds are adsorbed/complexed in/on the GAC [59]. However, a high amount of total pixels (first peak) at a very low value in the normalized grey scale (near to 0) were found in GAC-Virgin, thus indicating a clean/unblocked structure and well developed porosity, as is expected for an unused material. In addition, the peaks are not located at the same grey-scale region. The GAC-Top is distributed around 0.5 with a lighter grey value due to the presence of more adsorbed/complexed compounds thus featuring its higher exhaustion level. Nevertheless, GACBottom has a value around 0.4 followed by the GAC-Virgin with its distinctive/intense peak in the black “0” region with practically empty pores and cracks. These results are completely in line with findings using acoustic and TGA methods.

24

Total of pixels

1400 Image Histogram SEM image (GAC-Top) Image Histogram SEM image (GAC-Bottom) Image Histogram SEM image (GAC-Virgin)

1200 1000 800 600 400 200 0 0

0.2

0.4

0.6

0.8

1

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Grey scale normalized Figure 20. Image histograms for the GAC-Top, GAC-Bottom and GAC-Virgin. 3.5. FTIR analysis

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The FTIR spectra of the GAC-Top, GAC-Bottom and GAC-Virgin are presented in Figure 21. In the case of the GAC-Top, its FTIR spectrum shows a band at 1546 cm-1 that can be associated to organic carboxylate groups and aromatic compounds. Peaks at 1409 and 870 cm-1 can be assigned to CaCO3. In addition to that, the spectrum shows also a peak at 1011cm-1 which is characteristic for SiO2/silicates [60].

Figure 21. FTIR spectra for GAC-Top, GAC-Bottom and GAC-Virgin. The FTIR spectrum of GAC-Bottom shows a band at 1580 cm-1, that can be assigned (as for GAC-Top) to organic carboxylate groups and aromatic compounds. Two bands are observed at 1421 and 873 cm-1 corresponding to C-O stretching and bending vibrations in the carbonate group of CaCO3 [61]. In this case, also two bands at 1046 and 794 cm-1 were found which are consistent with the presence of SiO2/silicates [62]. There is no indication for the presence of significant amounts of organics because of the absence of C-H stretching vibrations (2800-3000 cm-1) [63].

25

The FTIR spectrum of GAC-Virgin reveals only weak absorption bands in contrast to the used GAC layer samples confirming the absence of significant amount of inorganic compounds and in agreement with XRF results (discussed further on). With FTIR analysis, the removal of Ca 2+ as CaCO3 by precipitation or after sequestration/complexation during the water treatment was confirmed. The presence of SiO2 in the GAC can be attributed to the sand filter located before the AC filter according to the technological scheme of the water treatment plant. After passing through the sand filter, the water can dissolve/drag and transfer silicate compounds to the GAC filter being then adsorbed. 3.6. XRF analysis

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In the GAC-Top, GAC-Bottom and GAC-Virgin samples Ca, Si, Fe, Al, Mg, S, Mn, Ba, I, K and Br were detected and their concentration as oxides, except for Ca, are presented in Table 9. According to the XRF results Ca, Si, Fe and Al are the major elements present in the GAC layer samples. The other detected elements are present in minor (~ 0.4-0.1%) and trace (< 0.1%) amounts. K is the major element in the GAC-Virgin sample (2.25%). (For all other samples < 0.4% is noticed) which was likely leached out from the used material during the water treatment process, generating its low concentration (< 0.1%) found in the GAC-Top and GAC-Bottom samples.

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Some elements decreased from the top to bottom of the filter (which are marked with “*”). Due to the accumulation of adsorbed/complexed ions in upper GAC layers and also because of possible ion exchange mechanisms, a migration process to lower GAC layers of certain ions could take place, interfering with a competition for active sites in the GAC. This could explain the higher concentration of some ions in the GAC-Bottom compared to the GAC-Top. In the case of Si, the higher concentration in the GAC-Bottom can be associated to the technological characteristics of the filter; a layer of sand is also placed after the last GAC layer (Bottom) to support the carbon filter bed and for GAC retention. In addition, during the filter back washing process, part of the sand is mixed with the GAC-Bottom layer thus increasing the amount of SiO2.

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The presence of high concentration of CaCO3 at the GAC-Top is in line with the results obtained by AEA, TGA, FTIR and XRF methods confirming the differences found between this sample and GAC-Virgin and GAC-Bottom. The amount of Ca-ions is directly proportional to the water hardness which in turn is an important contributor in the formation of incrustation. Based on XRF, FTIR and TGA results, it is confirmed that the Ca2+ ions are precipitated as CaCO3 and adsorbed via complexation by the GAC improving the quality of the water for cooling systems. Therefore, this result matches with several publications which refer to one of the characteristics of AC in reducing the water hardness [14, 15].

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Table 9: Concentration of the detected oxides in the GAC-Top, GAC-Bottom and GAC-Virgin using XRF method. (ND: not detected: C is calculated from TGA) Compound

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C CaCO3* SiO2 Fe2O3* Al2O3 MgO* SO3 MnO* BaO* I* Br* K2O

Concentration (%) Top 0.2m 57.3 58.9 31.3 29.26 2.71 2.93 1.038 0.789 0.966 1.05 0.414 0.402 0.315 0.248 0.136 0.084 0.135 0.11 0.132 0.101 0.017 0.011 0.060 0.065

0.4m 60.6 28.06 1.52 0.402 0.515 0.396 0.272 0.049 0.11 0.100 0.010 0.052

0.6m 64.6 26.07 1.84 0.414 0.554 0.383 0.359 0.037 0.087 0.103 0.010 0.070

Bottom 65.5 22.3 3.30 0.676 1.09 0.369 0.551 0.036 0.096 0.105 0.011 0.125

Virgin 95.4 0.342 0.243 0.124 0.031 0.112 0.080 0.003 ND ND ND 2.25

Ca is expressed as CaCO3 and not as CaO; I and Br are expressed as elements; “*”: elements decreasing in amount from top to bottom.

26

3.7. BET analysis Figure 23 presents the N2 sorption isotherms at 77 K for all the GAC samples. Found isotherms exhibited type I classification with a hysteresis loop type H4 for all samples according to IUPAC classification. Isotherms Type I are revealing the microporous nature of the samples and the H4 hysteresis loop is associated with the micropores filling process [54]. Differences in adsorbed nitrogen volume (presented in Table 10) and found isotherms trajectories give the first evidence of textural differences in the samples. For GAC-Virgin, the highest apparent surface area was found, as is expected from a virgin material, being around 1288 m2/g. The total pore volume (bubbling formation parameter) presents also the highest value of 0.536 cm3/g which is in line with the results obtained from AEA, displaying for SS, EMP, E and P the highest values.

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In contrast, GAC-Top presents the smallest apparent surface area and total pore volume of 196 m2/g and 0.103 cm3/g respectively, indicating a high degree of surface covering, and pores and cracks filling behavior. Based on gas sorption results, GAC-Virgin apparent surface area is almost ten times larger than GAC-Top which is in agreement with AEA results (Figure 6 and Table 2) and TGA data (Figure 14). In this case, the less exhausted condition of GAC-Bottom is again confirmed. However, in terms of N2 (77 K) sorption results, the GAC-Bottom has lost more than three times its total pore volume compared to GAC-Virgin.

Figure 23. N2 sorption isotherms at 77K of GAC-Virgin and different GAC layer samples.

Sample

SBET (m2/g)

VT (cm3/g)

VDR(cm3/g)

VDR/VT

E0 (kJ/mol)

L0 (nm)

196

0.103

0.077

0.07

21.761

1.04

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Top

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Table 10. Characterization parameters of porous structure of GACs using N2 (77K)

0.2m

316

0.167

0.119

0.71

23.46

0.9

0.4m

329

0.173

0.118

0.68

23.14

0.9

0.144

0.83

23.18

0.9

0.153

0.79

22.68

1.0

0.453

0.85

24.20

0.84

0.6m

Bottom Virgin

377 406

1288

0.176 0.195 0.536

It has been demonstrated that porosity is directly related with the bubbling process and the intensity of the sound produced by a GAC. However, the water molecules do not have complete access to the total porosity of the GAC compared with N2 at cryogenic temperature. Therefore, some pores (in a limited narrower region are excluded for water molecules due to their size and 27

perhaps polarity from ultra-micro and fine micro pores) do not contribute to the formation of bubbles. Therefore, it is expected that BET parameters such as pore volume and apparent surface area measured with N2 as probe molecule cannot be linearly correlated with the AEA parameters like energy and power from the GAC acoustic signals. Further studies must be addressed in order to phenomenologically modeling and accurately correlate and interpret the acoustic signals with the GAC structure characteristics. Figure 24 and Table 11 display the correlation and the fitting parameters (exponential model) between the total volume of pores (by N2 at 77K) and the energy and the power (by AEA at 25°C°) of the GAC acoustic signal for each GAC layer sample. 0.10

15 Bottom E P

9

0.04

6 3

Top

0.00

0

0.10

0.12

0.14

0.16

0.18

0.20

3

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VT, cm /g

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0.02

-4

0.06

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2

12

Power (10 ), W

Energy, V .s

0.08

lP

̅) and Power (P ̅) Figure 24. Correlation between the total pore volume (N2 at 77K) and Energy (E as acoustic parameters (at 25°C) for all GAC layer samples (acoustic signal was filtered from 3.5 to 25.6 kHz). Table 11. Fitting parameters for the total pore volume with E and P found with the AEA for the different GAC layer samples. Acoustic parameter E P

a

b

R2

1.3 ∙ 10−3 (±0.5 ∙ 10−3 ) 1.00 ∙ 10−5 (±0.05 ∙ 10−5)

21.7(±2.2) 23.8 (±2.3)

0.879 0.877

na

Property VT

𝑦 = 𝑦0 + 𝑎𝑒 𝑏𝑥 ; error of the parameter between brackets. Where y0=0.

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Experimental conditions and probe molecules used are different for gas sorption and acoustic method. Therefore, effects of molecular size and surficial chemical interactions jointly with the incomplete understanding of the correlation between acoustic signal parameters with GAC textural properties give no optimal correlation coefficient values between conventional BET and AEA methods. Although not strongly math-linked so far (empirical models by combining different acoustic parameters are under study), the exhaustion degree of the GAC samples are equally described for both methods as follows: GAC-Top> GAC-0.2> GAC-0.4> GAC-0.6> GAC-Bottom>> GAC-Virgin. Based on the found results, the reduction in the overall porosity of the GAC layer samples compared with GAC-Virgin is confirmed by AEA and the applied classical chemical methods (determination of ash content, TGA, XRF, FTIR) and physical methods (SEM, BET). Thus, AEA can be satisfactorily used as complementary method to texturally characterize and to determine the exhausted degree of GAC used in water treatment processes.

4. Conclusions 28

The sound produced by bubbles formed during the GAC flooded with water when the signal is digitally band-pass filtered from 3.5 to 25.6 kHz is a sensitive technique for quantifying the exhaustion level of the different layers in the GAC filter used in a water treatment in a fast and accurate way. AEA is also an alternative method for nitrogen adsorption (BET analysis) to determine differences in porosity, which is a key parameter in judging the exhaustion degree and performance of a GAC filter at layer level. The energy and the power of the signal are more suitable acoustic parameters to determine the exhaustion level of the different GAC used in water treatment process compared with the other signal parameters such as integral area under the signal envelope curve or envelope peak.

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Different used analytical methods confirmed not only the presence of Ca as CaCO3 but also gave clear indication of adsorption/complexation reactions with oxygen functional groups within the GAC carbon structure. A reduction of the overall porosity and an increment of the Ca adsorbed/precipitated from the GAC-Bottom (less exhausted GAC layer sample) to the GACTop were confirmed not only by conventional methods but also by the acoustic method.

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AEA method can be satisfactorily used as complementary method to texturally characterize GAC layer samples. This new acoustic method appears as an alternative, economic and more suitable method for the engine power plants in rural regions of Cuba. Additional it helps to improve the current GAC management in these plants with the correspondent derived economic and environmental benefits.

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Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Credit author statement

Thayset Mariño Peacok: prepared the samples, performed the acoustic experiments, did the analyses and wrote the paper

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Harold Crespo Sariol: did the analyses of acoustic experiments and wrote the paper Jan Yperman: did some analyses, wrote and revised the paper Ángel Sánchez Roca: did the analyses of the acoustic experiments and wrote the paper

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Robert Carleer: did some analyses and revised the paper Jeamichel Puente Torres: did the analyses of the acoustic experiments and wrote the paper

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Guy Reggers: performed the TGA and XRF experiments and the analyses Tom Haeldermans: assist in preparing and analysing the samples for different experiments, except the acoustic one. Elsy Thijssen: performed the AT-FTIR experiments and did the analyses. Pieter Samyn: performed the SEM experiments and did the analyses Grazyna Gryglewicz: performed the BET experiment, interpreted the data and revised the paper. Lissette Salomón García: samples acquisition and did the analyses of the experiments. 29

Acknowledgments The authors would like to thanks the VLIR-UOS project between Belgium and Cuba for providing funding and granting the support of the current and future studies. References

7. 8. 9.

10.

11. 12. 13. 14.

15.

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16.

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6.

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Marsh H, R.-R.F., Activated carbon. 2006, Great Britain: Elsevier Science & Technology Books. Danish, M. and T. Ahmad, A review on utilization of wood biomass as a sustainable precursor for activated carbon production and application. Renewable and Sustainable Energy Reviews, 2018. 87: p. 1-21. Çeçen, F. and Ö. Aktas, Activated carbon for water and wastewater treatment: Integration of adsorption and biological treatment. 2011, Istambul, Turkey: Wiley-VCH Verlag & Co. KGaA. Bhatnagar, A., et al., An overview of the modification methods of activated carbon for its water treatment applications. Chemical Engineering Journal, 2013. 219: p. 499-511. Bandosz, T.J., Activated carbon surfaces in environmental remediation. Vol. 7. 2006, United Kingdom: Elsevier Ltd. Baker, F.S., et al., Activated carbon, in Kirk‐Othmer Encyclopedia of Chemical Technology. 2000, John Wiley & Sons, Inc. p. 741-761. Rivera-Utrilla, J., et al., Activated carbon modifications to enhance its water treatment applications. An overview. Journal of hazardous materials, 2011. 187(1): p. 1-23. DeSilva, F.J., Exploring the Multifunctional Nature of Activated Carbon Filtration. Water quality products, 2000: p. 16-17. Deborde, M. and U. Von Gunten, Reactions of chlorine with inorganic and organic compounds during water treatment—kinetics and mechanisms: a critical review. Water research, 2008. 42(1-2): p. 13-51. Guo, Y. and E. Du, The effects of thermal regeneration conditions and inorganic compounds on the characteristics of activated carbon used in power plant. Energy Procedia, 2012. 17: p. 444-449. Habbart, L., Treatment of cooling water. 2009, Heidelberg, Berlin, Germany: Springer Science & Business Media. 7-10. Cohen-Adad, R. and J.W. Lorimer, Alkali metal and ammonium chlorides in water and heavy water (binary systems). 1991, New York, NC : Academic Science INC: Elsevier. Okoniewska, E., et al., The removal of manganese, iron and ammonium nitrogen on impregnated activated carbon. Desalination, 2007. 206(1-3): p. 251-258. Rolence, C., Adsorption studies on water hardness removal by using cashewnut shell activated carbon as an adsorbent. African Journal of Science and Research, 2016. 5: p. 78-81. Rolence, C., R.L. Machunda, and K.N. Njau, Water hardness removal by coconut shell activated carbon. International Journal of Science, Technology and Society, 2014. 2(5): p. 97-102. Potwora, R., Chlorine and chloramine removal with activated carbon. Water Conditioning and Purification 2009. Drinking water -Sanitary requirements. NC 827: 2010. Cuban National Bureau of Standards, 2010. Aljerf, L., Advanced highly polluted rainwater treatment process. Journal of Urban and Environmental Engineering, 2018. 12(1): p. 50-58. Shah, I.K., P. Pre, and B.J. Alappat, Steam regeneration of adsorbents: an experimental and technical review. Chem. Sci. Trans, 2013. 2(4): p. 1078-1088. Marsh, H. and F.R. Reinoso, Activated carbon. 2006: Elsevier. Nasruddin, et al., Adsorption Isotherms of Hydrogen on Granular Activated Carbon Derived From Coal and Derived From Coconut Shell. Heat Transfer Engineering, 2017. 38(4): p. 403-408.

ur

1.

17. 18. 19. 20. 21.

30

28. 29.

30. 31. 32. 33.

34.

35. 36.

37.

38.

Jo

39.

ro of

27.

-p

26.

re

25.

lP

24.

na

23.

Sigmund, G., et al., Biochar total surface area and total pore volume determined by N 2 and CO2 physisorption are strongly influenced by degassing temperature. Science of the Total Environment, 2017. 580: p. 770-775. Boyd, J.W. and J. Varley, The uses of passive measurement of acoustic emissions from chemical engineering processes. Chemical Engineering Science, 2001. 56(5): p. 17491767. Crespo Sariol, H., et al., A novel acoustic approach for the characterization of granular activated carbons used in the rum production. Ultrasonics, 2016. 70: p. 53-63. Crespo Sariol, H., et al., Comparative Study between Acoustic Emission Analysis and Immersion Bubble-Metric Technique, TGA and TD-GC/MS in View of the Characterization of Granular Activated Carbons Used in Rum Production. Beverages, 2017. 3(1): p. 12. Langlois, T.R., C. Zheng, and D.L. James, Toward animating water with complex acoustic bubbles. ACM Transactions on Graphics (TOG), 2016. 35(4): p. 95. Zheng, C. and D.L. James, Harmonic fluids. ACM Transactions on Graphics (TOG), 2009. 28(3): p. 37. Jirarungsatian, C. and A. Prateepasen, Pitting and uniform corrosion source recognition using acoustic emission parameters. Corrosion Science, 2010. 52(1): p. 187-197. Boczar, T. and D. Zmarzły, Application of the short time Fourier transform in evaluation of the acoustic emision signals generated by partial discharges. The Molecular and Quantum Acoustics Journal, 2004. 25: p. 45-68. Babgi, B., et al., Initial growth of sonochemically active and sonoluminescence bubbles at various frequencies. Ultrasonics sonochemistry, 2016. 29: p. 55-59. Tzanakis, I., et al., Characterizing the cavitation development and acoustic spectrum in various liquids. Ultrasonics sonochemistry, 2017. 34: p. 651-662. Alhashan, T., et al., Monitoring of bubble formation during the boiling process using acoustic emission signals. Int J Eng Res Sci, 2016. 2(4): p. 66-72. Crespo Sariol, H., et al., Characterization of thermally regenerated activated carbons used in rum production by acoustic emission analysis, N2 and Ar gas adsorption. Journal of advanced chemical engineering, 2017. 7(1): p. 1-10. Dennis, J., H.D. Tran, and H. Li, Spectrogram image feature for sound event classification in mismatched conditions. IEEE signal processing letters, 2011. 18(2): p. 130-133. Dennis, J.W., Sound event recognition in unstructured environments using spectrogram image processing. 2014, Nanyang Technological University: Singapore. Gavrovska, A.M., M.P. Paskaš, and I.S. Reljin, Determination of morphologically characteristic PCG segments from spectrogram image. Telfor Journal, 2010. 2(2): p. 7477. Macías, E.J., et al., Time–frequency diagram applied to stability analysis in gas metal arc welding based on acoustic emission. Science and Technology of Welding and Joining, 2010. 15(3): p. 226-232. ASTM, Standard Test Methods for Moisture in Activated Carbon, D 2867. ASTM International. 2011, West Conshohocken, PA, USA. Feldman, M., Hilbert transform in vibration analysis. Mechanical systems and signal processing, 2011. 25(3): p. 735-802. Bala, Ş.-R., Correlation of electroencephalographic potentials in biofeedback techniques with subjective patient's reactions in elaboration of wellness patterns for medical analysis, in 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). 2016, IEEE. p. 1-6. Hema, C.R., M. Paulraj, and S. Rankumar, Classification of eye movements using electrooculography and neural networks. International Journal of Human Computer Interaction (IJHCI), 2014. 5(4): p. 51-63. Haworth, K.J., et al., Quantitative frequency-domain passive cavitation imaging. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2017. 64(1): p. 177191.

ur

22.

40.

41.

42.

31

49. 50. 51. 52. 53.

54.

55.

56.

57.

58.

Jo

59.

ro of

48.

-p

47.

re

46.

lP

45.

na

44.

Kaewwaewnoi, W., A. Prateepasen, and P. Kaewtrakulpong, Investigation of the relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage. Measurement, 2010. 43(2): p. 274-282. Ferguson, M., et al., A Generalized Method for Featurization of Manufacturing Signals, With Application to Tool Condition Monitoring, in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. 2017, American Society of Mechanical Engineers. p. 1-10. Barnett, J.T., R.B. Clough, and B. Kedem, Power considerations in acoustic emission. The Journal of the Acoustical Society of America, 1995. 98(4): p. 2070-2081. Burgos-Artizzu, X.P., et al., Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 2011. 75(2): p. 337-346. Guijarro, M., et al., Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, 2011. 75(1): p. 75-83. Montalvo, M., et al., Automatic expert system for weeds/crops identification in images from maize fields. Expert Systems with Applications, 2013. 40(1): p. 75-82. Callejo, M.I.R., Segmentación automática de texturas en imágenes agrícolas. 2015, Universidad Complutense de Madrid: Spain. Gonzalez, R.C., R.E. Woods, and S.L. Eddins, Digital Image Processing. 2002, New Jersey, U.S.A: Prentice-Hall, Inc. Gonzalez, R.C., R.E. Woods, and S.L. Eddins, Digital Image Processing Using MATLAB. 2004, New Jersey, U.S.A: Prentice-Hall, Inc. ASTM, Standard test method for ash in biomass, E 1755-01. ASTM International. ASTM International: USA. 2007, West Conshohocken, PA, USA. Torres, J.P., et al., A novel X-ray radiography approach for the characterization of granular activated carbons used in the rum production. Journal of Analytical Science and Technology, 2018. 9(1): p. 1-15. Thommes, M., et al., Physisorption of gases, with special reference to the evaluation of surface area and pore size distribution (IUPAC Technical Report). Pure and Applied Chemistry, 2015. 87(9-10): p. 1051-1069. Minnaert, M., On musical air-bubbles and the sounds of running water. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1933. 16(104): p. 235-248. Crespo Sariol, H., et al., Characterization of Granular Activated Carbons Used in Rum Production by Immersion “Bubblemetry” in a Pure Liquid. J Food Processing & Beverages, 2016. 4(2): p. 1-10. Moore, B.C., et al., Changes in GAC pore structure during full-scale water treatment at Cincinnati: a comparison between virgin and thermally reactivated GAC. Carbon, 2001. 39(6): p. 789-807. Torres, J.P., et al., X-ray absorption as an alternative method to determine the exhausting degree of activated carbon layers in water treatment system for medical services. Talanta, 2019. 205: p. 120058. Lee, E.L. and I.E. Wachs, In situ Raman spectroscopy of SiO2-supported transition metal oxide catalysts: an isotopic 18O− 16O exchange study. The Journal of Physical Chemistry C, 2008. 112(16): p. 6487-6498. Gunasekaran, S., G. Anbalagan, and S. Pandi, Raman and infrared spectra of carbonates of calcite structure. Journal of Raman Spectroscopy: An International Journal for Original Work in all Aspects of Raman Spectroscopy, Including Higher Order Processes, and also Brillouin and Rayleigh Scattering, 2006. 37(9): p. 892-899. Taylor, W.R., Application of infrared spectroscopy to studies of silicate glass structure: Examples from the melilite glasses and the systems Na 2O-SiO2 and Na2O-Al2O3-SiO2. Proceedings of the Indian Academy of Sciences-Earth and Planetary Sciences, 1990. 99(1): p. 99-117. Aljerf, L., High-efficiency extraction of bromocresol purple dye and heavy metals as chromium from industrial effluent by adsorption onto a modified surface of zeolite:

ur

43.

60.

61.

62.

32

Jo

ur

na

lP

re

-p

ro of

kinetics and equilibrium study. Journal of environmental management, 2018. 225: p. 120-132.

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