Monitoring a lab-scale fluidized bed dryer: A comparison between pressure transducers, passive acoustic emissions and vibration measurements

Powder Technology 197 (2010) 36–48

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Monitoring a lab-scale fluidized bed dryer: A comparison between pressure transducers, passive acoustic emissions and vibration measurements D. Vervloet ⁎, J. Nijenhuis, J.R. van Ommen Delft University of Technology, Julianalaan 136, 2628 BL, Delft, The Netherlands

a r t i c l e

i n f o

Article history: Received 17 November 2008 Received in revised form 13 July 2009 Accepted 17 August 2009 Available online 23 August 2009 Keywords: Fluidized bed dryer Pressure fluctuations Acoustics Monitoring

a b s t r a c t The potential of both passive acoustic emission and vibration measurements for monitoring gradual process changes in comparison to pressure fluctuation measurements is investigated. Fluidized bed drying experiments of wetted pharmaceutical placebo granule were conducted and monitored by measuring the temperature-, humidity-, and pressure fluctuations. Next to that, the granule moisture content was determined. To test the proposed monitoring technique based on passive acoustic emission and vibration measurements, two types of microphones and an accelerometer were used. Consequently, several data analysis techniques were used to investigate the analogies between the recorded signals and the granule moisture content. All signals were recorded at a frequency of 400 Hz. The results demonstrate that one type of microphone and the accelerometer have some potential as nonintrusive alternatives for the purpose of monitoring transient processes compared to pressure fluctuation measurements. The recordings of another type of microphone did not show any correlation to changing process hydrodynamics. Time- and frequency domain analyses on the measured signals were found to be not suitable for monitoring the process hydrodynamics. The S-statistic, calculated by the attractor comparison method, was demonstrated to be capable of detecting gradual changes in the hydrodynamics of the fluidized bed. However, it is also shown that the investigated alternative measurement techniques are not as robust as pressure fluctuation measurements, which were found to be much better reproducible. © 2009 Elsevier B.V. All rights reserved.

1. Introduction The behaviour of fluidized beds can be observed and quantified through various (statistical) computations on data-sets of measured properties of the hydrodynamics [1–3]. The hydrodynamics of fluidized beds are determined by both process parameters (dimensions and conditions) and particle properties (particle size distribution and compound properties). Not only different fluidization regimes, e.g. bubbling or slugging, can be distinguished from one and another through various techniques [4], but also more gradual process changes that influence the hydrodynamics, e.g. particle agglomeration, attrition or drying, can be monitored [1]. Several examples of such techniques have been applied successfully on both laboratory- and plant scale [5]. It is believed that both acoustic emissions and vibrations exist in a fluidized bed as a consequence of the pressure waves in the bed and that they reflect characteristic properties of the fluidization hydrodynamics as well. Therefore, the usage of either of these measurements could provide an alternative method of fluidized bed monitoring. The advantage of these sensors over pressure sensors is that they do not

⁎ Corresponding author. Tel.: +31 15 27 84685; fax: +31 15 27 88267. E-mail address: [email protected] (D. Vervloet). 0032-5910/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.powtec.2009.08.015

have to be placed within the process itself and hence little unit modifications are required. In earlier work it has been shown that, amongst others, passive acoustic emission measurements do contain information on fluidization hydrodynamics and can distinguish between fluidization regimes [2,4,6]. Also, accelerometers have been found to contain information on fluidization hydrodynamics [7,8]. However, commercial production facilities generally still rely on conservative methods and/or experience to assess the process hydrodynamics, due to a lack of knowledge and understanding of these alternative measurement techniques. In order to be able to quantify and control fluidization processes there is a need for development, improvement and understanding of measurement techniques that can monitor these processes. Some complications are expected: (i) a low(er) signal intensity due to sensor sensitivity (e.g. the signal/noise ratio of an ordinary microphone is not very high at low frequencies), (ii) possible loss of signal intensity due to dampening by bed- and reactor material, and (iii) easy signal pollution due to background noise and vibrations from other equipment. Through various data analysis procedures it is aimed to describe and/or determine “the state” of the process hydrodynamics, which provides the operator with valuable information on properties like product quality, process stability or process safety. This, in turn, gives rise to opportunities to not only improve product quality and/or

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process conditions, but also potentially minimize losses through a decreased number of unscheduled shutdowns, a decreased fraction of off-spec material, and/or decreased safety margins. These objectives have generated an interest for research into new methods of reactor monitoring and data analysis, especially in fluidized bed reactors, where complex process hydrodynamics govern the reactor performance and ordinary ‘averaged’ pressure (drop) and/or temperature measurements do not provide sufficient information to prevent unscheduled shutdowns from, for example, agglomeration [9].

allowed because of potential growth of bacteria or build-up of material, e.g. in the food and/or the pharmaceutical industry. A potential weakness of acoustic and acceleration measurement techniques is that the noise level of the signal is expected to be much higher than that of pressure measurements. Background noises and vibrations of nearby equipment could easily pollute the measurements. Filtering of this background noise might be necessary. The sensors have to be placed in close contact to the apparatus, and shielded from signal pollution as much as possible.

1.1. Methods for observation

1.2. Methods for analysis

Visual observations of fluidized bed hydrodynamics are mostly regarded as subjective and can generally not be obtained due to the non-transparent nature of the column wall material, especially in industrial applications. Several techniques are proposed in literature for monitoring and quantifying fluidized bed hydrodynamics, e.g. pressure (drop) fluctuations [9,10], acoustic measurements [2,4,6], three dimensional X-ray imaging [11] and electrostatic measurements [12,13]. Of these methods especially the measurement of pressure (drop) fluctuations combined with a suitable treatment of the measured data has been shown to be a powerful technique for monitoring fluidized beds, because this technique is sensitive, accurate, fast, relatively easy to implement and relatively cheap. A somewhat dated overview of measurement techniques of fluidization processes is given by Yates et al. [14]. The use of acoustic measurement techniques is not mentioned, however. Multiple examples can be found in literature where acoustic measurements have been applied to monitor (flow regime) changes in multiphase systems, e.g. flooding point detection in absorption column and trickle beds [15], drying of granules in a rotary dryer [16], end-point detection in high shear granulation [17], hydrodynamics of several gas–liquid and gas–solid systems [18] and acoustic signatures of flow regimes in packed beds [19]. In this particular case we use passive acoustic emission, i.e. sound, measurements. The technique relies on the measurement of acoustic emissions from an apparatus, which are generated by the processes that take place within. The frequency spectra of these acoustic emissions are generally complex, because they consist of a mixture of all acoustic emission phenomena, stretching over several orders of magnitude. Relevant information is found in specific frequency ranges, depending on the phenomena of interest. Different acoustic sensors are available for different frequency ranges. However, low frequency phenomena may also be detected by fluctuations in higher frequency intensities. It is believed that the acoustic emissions and vibrations are closely linked to the pressure fluctuations, and therefore could provide an alternative method of fluidized bed monitoring. The physical interpretation of this link between pressure fluctuations, acoustic emissions and process vibrations is straightforward. The transport of gas pockets (or, for the same matter, particle clusters), i.e. bubbles and slugs, causes sounds through particle (cluster) collisions and sliding motions. This same movement of solids causes vibrations in the process equipment through the momentum transport of the collisions into the reactor wall. The generated sound and vibration intensities over the measured frequency domain will be dependent on the particle properties, the reactor wall material, the eigenfrequencies of the process equipment, and the impulse of the colliding particle clusters. The latter one is directly correlated to the process hydrodynamics. Typical phenomena that determine fluidized bed dynamics, e.g. void frequency, -coalescence, and -break up, take place in the frequency range of 0– 200 Hz. Therefore, this is the region of interest. The main advantage of acoustic or vibration measurements over pressure fluctuation measurements is that the sensors do not have to be placed inside the process itself. This could be advantageous in situations of harsh process conditions (high temperature, high pressure, corrosive substances), but also in situations where process modifications are not

Analysis methods can be divided in three categories: (i) timedomain analysis, (ii) frequency domain analysis and (iii) state-space analysis. An extensive review of various time-series analysis techniques applied to pressure fluctuations in fluidized beds can be found in [3]. The time-domain analysis techniques that will be used in this research are limited to observation of the raw data series and an inspection of the absolute average deviation (AAD) of the signal over time. Time-domain analysis techniques are generally insightful to generate an overall impression of the data over time, but it is often impossible to observe (small) hydrodynamic changes in the signal from the raw data alone. By performing a frequency domain analysis (through fast Fourier transformation, FFT) one has the ability to inspect the frequency distribution over a domain of half of the sampling frequency of the measured signals, i.e. the Nyquist criterion. This is not only useful for determining the dominant frequencies of the measured signals, but also for inspection of changes in the (dominant) frequency pattern over time (the transient power spectrum), since changes in hydrodynamic behavior will be reflected in the frequency distribution of the signal. Dominant frequencies of bubbling fluidized bed hydrodynamics are generally found in the frequency range of 0–20 Hz. A sufficiently high frequency of 400 Hz is chosen as the sampling frequency. Non-linear time-series analysis techniques are applied in many different fields, among which monitoring of fluidized bed dynamics. A particular category of non-linear analysis techniques are state-space analysis techniques, which typically rely on the construction of a multidimensional attractor for a given reference period. In the technique of Van Ommen et al. [1] the attractor is then compared to another attractor that was constructed from a certain evaluation period in the data-set, through a statistical test. If the attractors are found to be similar, then it can be said that the hydrodynamic properties of the systems for the given evaluation period and the reference period are alike, taking a certain confidence interval into account. The degree of similarity is expressed by the S-statistic. When S exceeds a value of 3 there is a significant difference between the reference and the evaluation period. The attractor comparison method has been shown to be a powerful method to detect changing hydrodynamics in fluidized bed reactors. Various successful test-cases, on both small and large scale, can be found in literature: general fluidization diagnosis [2,3], agglomeration detection [1,5], several works by Chaplin and Pugsley on drying of pharmaceutical granules [20–23] and detection of hydrodynamic instabilities in a slurry-loop reactor [24]. Because of the intricate nature and the amount of parameters that can be varied, the attractor comparison method is not only relatively complex to apply, but is also relatively difficult to interpret [1]. Obviously the measurement methods have to be suitable for the data analysis techniques that will be used. Frequency domain analysis and state-space analysis can only be applied if the sampling frequency is sufficiently high. 1.3. Goal of this research This research investigates the potential of both passive acoustic emission and vibration measurements for the detection of a gradual

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process change in comparison to pressure fluctuation measurements. The gradually changing hydrodynamics will be governed by continuous evaporation of moisture from the fluidized particles. Fluidized bed drying experiments of wetted pharmaceutical placebo granule were conducted and monitored by measuring the temperature-, humidity-, and pressure fluctuations. To test the proposed monitoring technique based on passive acoustic emission and vibration measurements, two types of microphones and an accelerometer were used to monitor the drying process. These experiments will demonstrate whether this technique is a robust non-intrusive alternative for monitoring techniques based on pressure measurements. In this research the low frequency range (0–20 Hz) is of main interest, i.e. the range in which the main hydrodynamic properties of fluidization take place. Several analysis techniques will be used, which are based on both frequency domain and state-space methods. 1.4. Previous work Although no previous work has been found that incorporates the comparison of passive acoustics and vibrations with pressure measurements for the purpose of monitoring gradual changes in fluidized bed reactors, several relevant studies exist. Finney et al. [4] researched the acoustic emission of various sizes of slugging fluidized beds loaded with various types of particles in the Geldart B and D ranges. They measured the sound level at frequencies up to 44.1 kHz and consecutively resampled the data-set. After comparison with pressure fluctuation measurements they concluded that the acoustic signal contained significant information about the slugging behaviour, and furthermore that the technique is useful for determining the slugging characteristics and online reactor monitoring. Villa Briongos et al. [2] investigated a bed filled with ballotini of several size ranges (Geldart B and D regimes) at several fluidization velocities, of which they recorded both pressure fluctuations and acoustic emissions. They report that passive acoustic emissions are not able to fully characterize the fluidized bed hydrodynamics by time- or frequency domain analysis, although some signal intensity was found at low frequencies with an ordinary microphone. It is believed that this low frequency originates from fluidized bed hydrodynamics. They showed that state-space analysis by means of correlation dimension analysis can identify a slugging regime under certain conditions. The microphone has a reported frequency range of 70–14,000 Hz. No information on the performance below 60 Hz is documented, since this microphone was not designed for this low frequency range. Nevertheless, the same type of microphone will be used in this research. Whitaker et al. [25] have shown that acoustic emission sensors can be applied successfully in online monitoring and end-point determination of a granulation process of a pharmaceutical filler material in a high shear mixer granulator. Although the article reports on the use of high frequency measurements (50–450 kHz), and more research was recommended, their conclusions are promising for this work in terms of gradual process changes. Halstensen et al. [7] report that accelerometers attached to several locations on the outside of a semi-industrial size pilot plant granulator are capable of detecting process conditions that deviate from a reference state using a principle component analysis. The monitoring technique is in principle developed as an early warning system for undesirable process conditions. They found that under certain conditions the obtained measurements could also be used to predict the granule moisture content. The calibration data-sets to obtain the acoustic fingerprints of the process with respect to the granulate moisture content extended over a period of several months. Tsujimoto et al. [6] studied, amongst others, the addition of moisture to a dry granule of cellulose in a fluidized bed granulator. The measured signal consisted of a high frequency acoustic sensor

attached to the reactor wall. They found that the mean acoustic emission amplitude gives an early warning before total defluidization through a moisture overload in the bed. However, they do not report any other, more sophisticated, data analysis techniques. Previously mentioned studies by Chaplin et al. [20,22,23] and Pugsley et al. [21] show that the S-statistic, performed on pressure fluctuation measurements, is capable of identifying changing hydrodynamics of a pharmaceutical placebo material in a conical, lab-scale fluidized bed dryer. They conclude that the moisture content of the granulate has the most significant impact on the fluidization hydrodynamics during the drying process. They also conclude that additional research is needed in order to fully connect the obtained values from S-statistic to the moisture content. Recently, Wormsbecker and Pugsley [26] concluded that at high moisture contents (>20%) porous pharmaceutical granules show fluidization behaviour similar to Geldart C powder, whereas at low moisture contents (<10%) the material exhibits the characteristics of Geldart B powder. They connect this transition to a decrease of the interparticle forces, which are present as a consequence of the moisture, during the drying process. 2. Experimental 2.1. Equipment A cylindrical, acrylic glass column of 30 cm in diameter was used as fluidized bed column. The column was equipped with a perforated distributor plate, sensor mounting points flush to the wall and a conical freeboard. A schematic overview is given in Fig. 1. The pressure sensors are Kistler piezo-electric pressure transducers (type 7261), from which the signal is amplified through a Kistler amplifier (type 5011). Relative humidity sensor 1 is a Testo 6744 Dewpoint transmitter, and relative humidity sensor 2 is an E + E Elektronik humidity/temperature transmitter of series EE23. Thermocouples 1 and 2 are type K. The pressure difference sensor is a Validyne model DP 15-26. Microphone 1 is an Audio-Technica model PRO 42, which was chosen based on the results by Villa Briongos et al. [2], where it was shown that this microphone can be used to monitor low frequency (0–20 Hz) fluidized bed dynamics. Microphone 2 is made in-house from a computer speaker and a battery powered LM741 amplifier circuit. A low-pass filter was integrated on the circuit by adding a capacitor (10 µF) and a resistance (1 kΩ), resulting in a cut-

Fig. 1. Schematic overview of the column and sensor locations. P denotes pressure sensor, T denotes thermocouple, RH denotes relative humidity sensor, M denotes microphone and A denotes accelerometer.

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off frequency of 1000 Hz. Both microphones are covered by styrofoam to prevent pollution of the signals by surrounding noise as much as possible, and placed directly against the outside of the column wall. The Audio-Technica and custom-made microphones were powered with signals from a double stabilised electrical network and a 9 V battery respectively to prevent signal pollution from the electrical network. The accelerometer is a miniature DeltaTron of type 4508 B 001, which measures one-dimensional vibrations. It is applied in direct contact with the column wall on the outside. All signals are recorded at a frequency of 400 Hz with a Scadas III data acquisition system from LMS. The locations of the relevant sensors are given in Table 1. The temperature sensors penetrate the bed to the centre of the column. The pressure transducer probes are placed flush with the column wall. A wire gauze at the end of the probe prevents particulates from entering. Furthermore, a small purge flow is passed through the probe to prevent blockage of the pressure probe. The length of the probes is 10 cm and the internal diameter is 4 mm. There is no signal distortion as a consequence of these dimensions or the presence of the wire gauze [27]. Relative humidity sensor 1 is placed in the stream before heating and relative humidity sensor 2 is placed above the bed, the microphones and the accelerometer are placed in direct contact with the column on the outside of the reactor wall. All equipment and reactor material was grounded. The settled bed height of the granulate was approximately 20 cm. The bed height during fluidization was approximately 40 cm.

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Table 2 Placebo granulate composition. Component

Weight (g)

w/w (%) (dry)

w/w (%) (wet)

Lactose monohydrate Microcrystalline cellulose HPMC Croscarmellose USP distilled water

375.0 ± 0.1 330.0 ± 0.1 30.0 ± 0.05 15.0 ± 0.05 315 ± 5

50 44 4 2 –

35 31 3 1 30

down the drying procedure. Samples of the granulate bed material were taken regularly by scooping out material from the top of the bed. The samples are weighed and dried for several days at 80 °C in an oven. The moisture content of the samples is calculated by the weight difference of the samples before and after drying. 3. Results and discussion Three similar experiments were performed with 8 kg of wetted material. After every experiment the pressure probes were checked. None were found plugged. By visual observation the fluidization behaviour was found uniform around the transparent column at all times during the experiment. In the initial operating period of 10 min the fluidization behaviour was found to be slugging for several minutes while the initial moisture content was evaporated. At all times the entire observable part of the bed was seen in motion.

2.2. Material 3.1. Time-domain analysis The granulate material consists of a pharmaceutical placebo material with the composition displayed in Table 2. The lactose monohydrate is of type M200 by DMV, the microcrystalline cellulose (Avicel) is type ph.102 by FMC BioPolymer, the hydroxypropyl methylcellulose (HPMC) is of type E5 by Colorcon and the croscarmellose (carboxymethylcellulose) is also manufactured by Colorcon. The granulation procedure involves mixing of the dry powders material powder in a granulator for 3 min, after which water is added through a spray nozzle for 5 min at a rate of 63 mL/min while the mixture continues to be mixed. Then, granulation takes place for another 2 min. The wetted material is then sieved to filter out all agglomerates larger than 3.36 mm (sieve size 6). One batch makes in total 1065 ± 5 g of material, of which approximately 1 kg of wetted granulated material remains after sieving. The properties of the granulated material strongly depend on mixing times and amount of added water. Regular calibration of the flow nozzle is required, since blocking can change the flowrate. The product quality is nevertheless very reproducible when the procedure is followed precisely. 2.3. Drying procedure After heating up the inlet flow (air) to 65 °C at the inlet (sensor T1) and the empty column, the flow was stopped and the wetted material was added to the column. The ingoing temperature was kept constant during the experiment. The flow was raised to a velocity of 0.60 m/s for approximately 10 min to make sure no wetted material was sticking to the bottom or the wall of the column. After this initial period the material was found to be dry enough for less vigorous fluidization and the air flow was lowered to 0.40 m/s in order to slow Table 1 Sensor heights above the distributor plate. Sensor

Height (cm)

P1 P2, T1, M1, M2, A P3

Windbox 7.2 13.3

Drying data of the thermocouples (T1 and T2) and the relative humidity sensor (RH2) of an experiment is displayed in Fig. 2. A typical drying curve can be observed; i.e. the (surface) moisture evaporates from the granulate, which keeps the bed temperature more or less constant (from minute 10, after which the fluidization velocity was kept constant, to roughly minute 40 in the experiment), until moisture diffusion in the particles becomes a mass transfer limiting factor, after which the bed temperature starts to increase. Similarly, the relative humidity of the exit-air of the column increases rapidly to almost full saturation, consequently stays constant for some time until all moisture is evaporated and then decreases again. This clearly illustrates the difficulties that are encountered during drying experiments. It is not easy to predict the moisture content of the granulate in the bed both online and reasonably accurate. After approximately 60 min almost all moisture has been removed from the granulate. This point shall be referred to as the drying end-point of the experiment. This particular experiment was ended after around 150 min. In Fig. 3 the raw data of a pressure transducer (P2), the two microphones (M1 and M2) and the accelerator (A) can be seen. The data is given in arbitrary units. The raw data series of the pressure transducer is representative for the other pressure measurements. From the raw data series of the pressure transducer no specific trend can be observed. Around t = 40 min a very small increase in amplitude can be seen and around t = 80 min a clear decrease in amplitude can be observed. Sensor M1 does not show a specific trend or significantly different behaviour in any point of time during the experiment. Sensor M2 does not show a clear trend over time, although a small change in the signal amplitude can be observed around approximately t = 60 min, which corresponds to the drying end-point in Fig. 2. The accelerometer displays a very small increase until approximately t = 50 min, after which the signal intensity decreases until approximately t = 80 min. From this point onward the signal remains constant. None of the raw signals displays a trend that can be linked directly to the process hydrodynamics or the moisture content of the particulates.

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Fig. 2. Raw data overview of the temperature, relative humidity and the granulate moisture content during the drying experiment. Clearly all properties are coupled.

Fig. 4 gives an overview of the calculated average absolute deviations of the sensors, which is a measure that sometimes can be used for the detection of transitions between different fluidization regimes, although it cannot be used alone, and can be misleading in some cases for various reasons, such as its strong sensitivity to the gas velocity [9]. No clear connection can be observed in the AAD of any of the signals and the decreasing granulate moisture content. The AAD of sensor M2 displays a slight decrease (roughly from 10 to 60 min), which is followed by an increase in amplitude. Similar results were obtained for the duplicate experiments.

3.2. Frequency domain analysis The power spectral density (PSD) plots of the pressure fluctuation measurements show a common trend for fluidized bed hydrodynamics. Fig. 5 shows an overview of the PSDs of all pressure fluctuation sensors for three time-periods of 5 min during the experiment, i.e. at the beginning, in the middle and at the end of the drying period. For sake of overview, the plots of all sensors over half of the entire frequency range are given in the left of this Figure. The close-ups of the low frequency range for the pressure fluctuation sensors are given

Fig. 3. Raw data overview of the measurements of pressure transducer 2 (P2), microphone 1 (M1), microphone 2 (M2) and the accelerometer (A) during the drying experiment.

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Fig. 4. Average absolute deviations of the signals during the experiment, taken over time intervals of 30 s. No clear connection can be observed between any of the AADs and the moisture content of the granulate. However, the AAD of sensor M2 shows a significant increase after minute 60, which corresponds to the drying end-point of the experiment.

Fig. 5. The complete PSDs of the signals of the three pressure sensors P1, P2 and P3 for three periods of 5 min during the drying experiment, and the close-ups of the lower frequency range of 0–10 Hz of the same graphs.

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in Fig. 5 on the right hand side. It can be observed in the close-ups that the dominant frequency of the hydrodynamics shifts from approximately 2.5 Hz to approximately 4 Hz. However, this is far less clear for the much more complex PSDs of the microphones and the accelerometer. Fig. 6 shows the full range PSDs and a close-up of the PSDs of sensors M1, M2 and A, from which it is not directly clear that changes take place. Only the accelerometer shows a clearly visible disappearing peak at around 2.5 Hz. To investigate the changes in the frequency distribution over time better, the transient PSDs of the pressure fluctuations are given in Fig. 7. Only the close-up (0–10 Hz) is given here. The spectrum of the complete frequency range does not provide any additional insights. The signal is divided into 30 s periods, for which each PSD is plotted individually. The intensity of the frequency distribution is given in Bell (B) and denoted by the color scheme to the right of the graph. Again a clear shift in the frequency pattern can be observed during the experiment, specifically with sensors P1 and P2. No interesting shifts take place at the high frequency part of the spectrum, i.e. >10 Hz. A similar overview is given in Fig. 8, in which the transient PSDs of sensors M1, M2 and A are given. The signal of sensor M1 does not display any trend in any part of the spectrum. Also, no clear trend is visible in the signal of sensor M2, although some changes seem to occur around the drying end-point. From the overall plot it is not clear whether sensor A picks up any changes, but some signal intensity is observed in the low frequency range. A close-up of the region of 0–20 Hz is displayed in Fig. 9. Again, the signal of sensor M1 does not display any trends. A very weak trend can be observed in the transient PSD of sensor M2. Sensor A seems to show some abrupt changes to different states, instead of displaying a gradual change.

3.3. State-space analysis Fig. 10 shows an overview of the granule moisture content of an experiment as well as the calculated values of the S-statistic for the pressure fluctuation time-series of the pressure probes. The settings of the attractor comparison calculation have been optimized for sensor P2, and are given in the caption. The trends in the analysed signals of the pressure probes show a similar shape, but the sensitivities are different because the probes are located at different positions in the bed. The S-value calculations for sensor P2, i.e. the sensor in the middle of the bed, will be used throughout of the paper. A clear decreasing trend can be observed in the calculated S-values from the pressure fluctuation measurements of sensor P2 as the drying experiment advances from the start of the experiment towards the reference period, i.e. 60–62 min after the start, indicating that the evaluated data periods are resembling the reference period more and more as the experiment reaches the drying end-point. As the experiment advances beyond the reference period the resemblance between the reference period and evaluation period disappears again, which can be noticed by an increase in the S-value. The other pressure probes show similar behaviour, as do the measurements in the duplicate experiments. This result is in agreement with the results obtained by Chaplin et al. [20]. It is interesting to see that the S-value keeps changing after the drying end-point, indicating that that the fluidization dynamics do not stay constant. This effect could not be observed in the results by Chaplin et al., since the published figures do not show any data shortly after the drying end-point. From the granule moisture content curve in Fig. 10 it can be seen that no significant drying takes place after approximately 1 h. Therefore, changes in the particulate moisture

Fig. 6. The complete PSDs of the signals of sensors M1, M2 and A for three periods of 5 min during the drying experiment, and the close-ups of the lower frequency range of 0–20 Hz of the same graphs.

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Fig. 7. Close-ups of the low frequency ranges of the transient PSDs of the pressure fluctuations of a drying experiment. The PSDs are constructed with 30 s periods. The signal intensity is given in Bell, which is denoted by the color scheme on the right.

content cannot be the cause of these fluctuations in dynamics. Moreover, no changes in the fluidization behaviour were visually observed. Possible explanations for the variations in S after the drying end-point are the presence of other processes that influence the fluidization dynamics, e.g. attrition, the loss of the smallest fraction of particles which are blown out of the column and electrostatic effects.

Some of these processes also influence each other and therefore might not occur until certain process/product conditions arise; for example: the attrition rates will likely differ at various granule moisture contents, and therefore the attrition rate will also change throughout the experiment. Nieuwmeyer et al. [28,29] have recently shown that attrition rates of lactose granulate as a function of moisture content in

Fig. 8. Transient PSDs of the signals of sensors M1, M2 and A of a drying experiment. The PSDs are constructed with 30 s periods. Note that the colorbars of the three sensors differ, i.e. the intensity range is very different for every sensor. The signal intensity is given in Bell. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 9. Transient PSDs of sensors M1, M2 and A of a drying experiment. The PSDs are constructed with 30 s periods. Note that the colorbars of the three sensors differ, i.e. the intensity range is very different for every sensor. The signal intensity is given in Bell. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

fluid bed dryers play a significant role during the drying process. The results illustrate both the power and the complexity of the attractor comparison method, which is sensitive to many different variations. In Fig. 11 the calculated S-values of sensors M1 (Fig. 11a), M2 and A (Fig. 11b) are displayed; note that the setting for the embedding dimension is different from that of the pressure fluctuation data analysis, which was found to produce more sensitive results for these sensors. The Audio-Technica microphone (M1) data-set does not display any trend, as can be seen in Fig. 11a. Both the custom-made microphone (M2) and the accelerometer (A), which are displayed in Fig. 11b, show some similarities with the trends that were observed

from the pressure probes. However, no clear decreasing trend from the start of the experiment until the reference period is visible. In the above example of data analysis, the state-space condition of the process hydrodynamics is chosen at the drying end-point, which is only known when the experiment has finished and the samples have been dried and weighed to determine the moisture content. Another approach to assess the signal is to use a moving reference period. This incorporates the comparison of the process hydrodynamics with the situation of a fixed time difference before the evaluation period. In other words, the reference period is moving along with the evaluation period, always trailing a fixed time. This method allows the detection of

Fig. 10. Moisture content curve of a drying experiment and the calculated S-values of the pressure sensors. The S-calculation settings have been optimized for P2: reference period 60–62 min, evaluation period 2 min, embedding dimension 40, segment length 3 s, bandwidth 0.5.

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Fig. 11. a (top): Moisture content curve of a drying experiment and the calculated S-values of microphone M1. S-calculation settings: reference period 60–62 min, evaluation period 2 min, embedding dimension 10, segment length 3 s, bandwidth 0.5. Fig. 11b (bottom): Moisture content curve of a drying experiment and the calculated S-values of microphone M2 and accelerometer A. S-calculation settings: reference period 60–62 min, evaluation period 2 min, embedding dimension 10, segment length 3 s, bandwidth 0.5.

significant changes in process hydrodynamics, while not noticing small changes. The sensitivity depends on the chosen settings. Fig. 12 shows the calculated S-values with a moving reference. It can be seen that the end-point is clearly detected. The potential for the other sensors was also investigated and is displayed in Fig. 13. Again, M1 did not display any trend and was omitted. It can be seen from the figure that microphone M2 is slightly early in comparison to P2 and that accelerometer A is even earlier. Ideally, the recorded signals should be evaluated with a reference situation that is known a priori. In Fig. 14 the signal of P2 is evaluated with reference periods of the drying end-points of two other experiments. From this figure one can clearly observe that the reference periods of the drying end-points show good similarities in process hydrodynamics, making it possible to use a reference period of a previous duplicate experiment. Furthermore, it is also demonstrated that the obtained signals from the pressure transducers show a good reproducibility.

Figs. 15 and 16 show the results for the cross-referenced signals of M2 and A. Again, M1 was not included due to the lack of any reasonable trend. Clearly, the S-value stays above three in all cases, throughout the entire experiment. This indicates that at no point in time during the experiment the constructed attractors from the measured signals resemble that of the reference period in the duplicate experiments. This illustrates that the measured signals of sensors M2 and A are not as robust as the signals that are obtained from the pressure transducers.

3.4. Discussion One of the reasons for the poor results that were obtained with the Audio-Technica microphone in comparison to the previous work by Villa Briongos et al. [2] might be that the investigated material of this study does not generate as much acoustic emissions as the ballotini in

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Fig. 12. Moisture content curve of a drying experiment and the calculated S-values of the pressure sensor P2 with a moving reference. S-calculation settings: moving reference, reference period length 2 min, Δt = 4 min, evaluation period 2 min, embedding dimension 40, segment length 3 s, bandwidth 0.5.

their study. Therefore, it seems impossible to make a direct comparison with that work. A problem of the microphones is that parts of the electrical circuits are not (well) shielded from electrical and/or magnetic fields. Besides acoustic noise, it is expected that some electrical noise will be picked up from the surroundings. Also, the use of this particular microphone can be argued, because it is designed for recording a frequency range that is not useful for this purpose. The reported frequency range of the microphone is 70–14,000 Hz, and not the desired lower frequency range. It is unknown what the signal-to-noise ratio at the lower frequencies is. The microphone was found to function properly both before and after the experiments for the recording of human voice. Possibly, significant sensitivity improvements can be made by investigating various locations for the microphones and the accelerometer, since the local sound propagation and vibrations might be damped due to the column material. Also, the use of multiple sensors

and/or combinations of different sensors boosts signal sensitivity, and should therefore be the preferred situation in a set-up. Material sampling was done from the top of the bed. In this part of the bed are generally smaller and lighter particles located. Therefore, it is possible that the samples are not completely representative of the moisture content of the whole bed. In that case, continuous drying of larger particles in the bottom of the bed can take place, which would not be noticed from the moisture content of the particle samples. As mentioned before, several other gradual processes can take place during and after drying (e.g. attrition, agglomeration, loss of small particles from the bed) that all change the process hydrodynamics. It is expected that some of these processes play a role, although it is uncertain to what extent. The accelerometer is possibly not able to detect gradual process changes very well, since the signal is generated by the local vibrations of the column, which are highly dependant on the eigenfrequencies of

Fig. 13. Moisture content curve of a drying experiment and the calculated S-values of M2 and A with a moving reference. S-calculation settings: moving reference, reference period length 2 min, Δt = 4 min, evaluation period 2 min, embedding dimension 10, segment length 3 s, bandwidth 0.5.

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Fig. 14. Moisture content curve of a drying experiment and the calculated S-values of the pressure sensor P2 referenced with the duplicate experiments. S-calculation settings: original reference period 60–62 min, evaluation period 2 min, embedding dimension 40, segment length 3 s, bandwidth 0.5.

the set-up. These eigenfrequencies are typically located at fixed frequency values, and the transition of one frequency to another is not necessarily smooth, but might be discrete. The sharp peaks in the graphs of the accelerometer in Figs. 6, 8 and 9 show that there are indeed several dominant frequencies present, and furthermore that there are several moments during the experiment at which the frequency pattern seems to jump from one state to another. This was not investigated in detail. 4. Conclusions Conventional, time-domain based, data analysis methods are often insufficient for the detection of gradual process changes in fluidized beds. In this case the drying of a pharmaceutical placebo granule was investigated. Time-domain analysis (e.g. on temperature or pressure), does not provide sufficient information on the product and process properties, which are reflected through the process hydrodynamics. Frequency analysis of the acoustic and acceleration measurements show that these methods are not as sensitive as pressure fluctuation

measurements in the detection of changes in process hydrodynamics. However, certain changes were discovered in the transient power spectrum of the custom-made microphone and the accelerometer, although these often did not result in a clear display of gradual process changes. The attractor comparison method, a state-space data analysis method, has shown to be capable of detecting changes in process hydrodynamics, where conventional methods, like time-domain and frequency based analysis methods, either fail to detect changes, or are unclear. The application of this data analysis technique on the pressure measurements has shown that the technique is accurate and that the experiments were reproducible. The application of this analysis method on acoustic and acceleration data series has shown to be successful for both the custom-made microphone and the accelerometer. However, these results also show that the microphones and the accelerometer are not consistent for duplicate experiments and these applications should therefore undergo more development in order to use them for monitoring purposes. The signal that was generated by the Audio-Technica microphone did not yield

Fig. 15. Moisture content curve of a drying experiment and the calculated S-values of the microphone M2 referenced with the duplicate experiments. S-calculation settings: original reference period 60–62 min, evaluation period 2 min, embedding dimension 10, segment length 3 s, bandwidth 0.5.

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Fig. 16. Moisture content curve of a drying experiment and the calculated S-values of the accelerometer A referenced with the duplicate experiments. S-calculation settings: original reference period 60–62 min, evaluation period 2 min, embedding dimension 10, segment length 3 s, bandwidth 0.5.

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