Phase space structure and multi-resolution analysis of gas–solid fluidized bed hydrodynamics: Part II: Dynamic analysis

Briongos, Javier Villa; Aragón, José M.; Palancar, María C.

doi:10.1016/j.ces.2007.02.019

Cited by 23 publications

(19 citation statements)

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“…That fact leads to the use of different techniques focused on identifying and distin guishing those contributions from the measured signal. Thus, the multi scale nature of fluidized bed dynamics has drawn con siderable attention in the last years (Li and Kwauk, 2001;Lu and Li, 1999;Zhao and Yang, 2003;Briongos et al, 2006aBriongos et al, , 2007Abbasi et al, 2009). However, due that the causes and sources of accelerometer signals are not yet understood, the Coherent Output Power, COP, and the Incoherent Output Power, IOP, analysis used in van der Schaaf et al (2002) is applied here as a first approach to analyze the different dynamical components appearing in the envelope process, comparing the local and global information stored in both pressure and envelope accelerometer signals.…”

Section: Frequency Domainmentioning

confidence: 99%

Can low frequency accelerometry replace pressure measurements for monitoring gas-solid fluidized beds?

Martín

Briongos

Aragón

et al. 2010

Chemical Engineering Science

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a b s t r a c tThis paper is addressed to introduce a methodology based on low frequency accelerometry that can be used for gas solid fluidized bed monitoring, and for dynamic diagnosis purposes. The proposed methodology consists on extracting the low frequency information encoded within an accelerometry signal, by means of the Hilbert transform method. The time and the frequency domain analysis show how this low frequency information is directly related to the conventional pressure fluctuation measurements, providing useful information on bulk and bubble dynamics. The cross correlation and the coherence function analysis between the pressure and the envelope process of the measured accelerometer signals ''E'', exhibit values approaching unity for frequencies ranging between 2 and 4 Hz. This reveals that pressure signals and accelerometry envelope are related processes. The results from the Coherent Output Power, COP, and the Incoherent Output Power, IOP, analysis confirms that both pressure and envelope time series exhibit the same global and local features open the possibility of using low frequency accelerometers instead of conventional pressure transducers for monitoring and for dynamic diagnosis purposes.

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Section: Frequency Domainmentioning

confidence: 99%

Can low frequency accelerometry replace pressure measurements for monitoring gas-solid fluidized beds?

Martín

Briongos

Aragón

et al. 2010

Chemical Engineering Science

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“…18. Consequently, the bulk dynamics (Briongos et al 2006b) is responsible for the differences within the short-term temporal structure between both the measured, and the simulated pressure time series observed in Figure 6. Complementarily to the frequency domain and mutual information analysis, the state space analysis has been used to validate the model.…”

Section: The Influence Of Pressure Drop Across the Air Distributormentioning

confidence: 99%

“…Table 2 shows the averaged instantaneous frequencies estimated according to Briongos et al (2006b), for the five modes corresponding to the simulated and measured MBR extracted through the EMD process. The stopping criterion has been set by the way in which the sifting process is stopped when the averaged instantaneous frequency of the current mode is smaller than the characteristic bubble exploding frequency (0.5 Hz).…”

Section: The Influence Of Pressure Drop Across the Air Distributormentioning

confidence: 99%

“…Later, a detailed dynamical comparison between the dynamics characterizing the simulated time series, and the Kistler pressure fluctuation data reported in Johnsson et al (2000), is performed to study the different dynamical scales appearing in the model. Thus, frequency domain, stated space analysis, mutual information function, and multi-resolution analysis by applying the Empirical Method of Decomposition, EMD (Briongos et al 2006b), are complementarily used to perform the comparison between both the simulated and the measured pressure fluctuation data. The simulated cold rig has a cross-section of 0.12 x 0.70 m having five different perforated plates (Table 1) (Pannala et al, 2004).…”

Section: Perforated Plate Distributormentioning

confidence: 99%

“…Subsequently, in order to elucidate the dynamical processes occurring within the FB system, the time dependent behaviour of the measured signals is often analyzed by time and frequency domain analysis (Jonhsson et al 2000). However, due to the inherent nonlinear features of gas-solid FB dynamics, a non linear approach including a multiscale analysis and tools derived from the deterministic chaos theory, can be reliably applied complementarily to the time and frequency analysis methods to account for those nonlinear interactions (Briongos et al 2006b). Thus, dynamical aspects characteristics of gas-solid fluidization, are identified by establishing a direct relation between the physical phenomena driving the dynamics, and the measured signals.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach for modeling bubbling gas-solid fluidized beds

et al. 2010

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A phenomenological discrete bubble model is proposed to help in the design and dynamic diagnosis of bubbling fluidized beds. An activation region mechanism is presented for bubble formation, making it possible to model large beds in a timely manner. The bubbles are modelled as spherical-cap discrete elements that rise through the emulsion phase that is considered as a continuum. The model accounts for the simultaneous interaction of neighbouring bubbles by including the trailing effects due to the wake acceleration force. The coalescence process is not irreversible and therefore, the coalescing bubble pair is free to interact with other rising bubbles originating the splitting phenomena. To validate the model, the simulated dynamics are compared to both experimental and literature data. Time, frequency and state space analysis are complementarily used with a multiresolution approach based on the empirical method of decomposition, EMD, to explore the different dynamic scales appearing in both the simulated time series and those obtained from experimental runs. It is concluded that the proposed model matches the main features of bubble dynamics being a useful tool to aid in the design and dynamic diagnosis of those systems.Keywords: Fluidization, Multiscale modeling, Bubble phenomena; Chaos; Multiresolution analysis. 3 IntroductionAmong the huge number of industrial applications of bubbling gas-solid fluidized beds, FB's, those related to their use in energy conversion have recently gained attention due to the current energy policies. Thus, bubbling gas-solid FB's are broadly applied in thermochemical energy conversion processes such as combustion and gasification. The fluidization process offers a high heat transfer rate, good gas-solid mixing and solid handling, and provides a uniform and controllable temperature. Moreover, its ability to process low grade-fuels with low pollutant emission makes the use of bubbling FB´s a very promising technology for the necessary valorization of biomass and wastes in energy conversion processes (Jonhsson, 2007). However, when dealing with biomass fluidized bed processes, for instance, the high complexity characterizing conventional gas-solid FB's dynamics increases due to the limited research reported on biomass fluidization hydrodynamics. According to that, recently it has been pointed out the necessity of improving the characterization of biomass fluidization hydrodynamics to understand the influence of the biomass particles on the fluidization phenomena (Cui and Grace, 2007). The characterization of gas-solid FB dynamics is currently addressed by monitoring the local time evolution of some variables such as pressure, capacitance, temperature, etc. (Werther, 1999). Moreover, some global techniques addressed to characterize the overall fluidized bed dynamics have been also reported (Briongos and Guardiola, 2003;Dyakowski et al. 2000;Briongos et al. 2006a; Van Ommen and Muddle, 2007). Subsequently, in order to elucidate the dynamical processes occurring within the FB s...

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Identification of multiscale nature and multiple dynamics of the blast furnace system from operating data

2011

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Very often, there are several kinds of different scale structures, such as atomic/molecular microscale, device/boundary mesoscale and working procedure/operation unit macroscale, etc., referred in the operation of a blast furnace system. However, not enough attention has been paid to the multiscale feature of the blast furnace system by traditional analytic methods which are the average ones over a fixed scale. For this reason, the current work performs a multiscale identification and further makes a dynamical analysis on the blast furnace system from the time series sets of two important components in the blast furnace hot metal, i.e., silicon content and sulfur content, collected from a pint-sized blast furnace. The results render a strong indication of multiscale characteristic and multiple dynamics, i.e., randomicity, chaos, and limit cycle but with different rates of contribution to the whole system, contained in the blast furnace system. Furthermore, compared with the original blast furnace system, every subscale structure has lower complexity. All of these can serve as guidelines to carry out modeling, control and optimization on complex blast furnace system from the viewpoint of multiscale in the future, and may throw more light on understanding and characterizing its complex dynamics.

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Phase space structure and multi-resolution analysis of gas–solid fluidized bed hydrodynamics: Part II: Dynamic analysis

Cited by 23 publications

References 50 publications

Can low frequency accelerometry replace pressure measurements for monitoring gas-solid fluidized beds?

Can low frequency accelerometry replace pressure measurements for monitoring gas-solid fluidized beds?

A novel approach for modeling bubbling gas-solid fluidized beds

Identification of multiscale nature and multiple dynamics of the blast furnace system from operating data

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