Stochastic Inferential Control of the Fluidized State via Differential Pressure Measurements

Clough, David E.; Gyure, Dale C.

doi:10.1080/00986448408940121

Cited by 8 publications

(2 citation statements)

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“…A simple proportional feedback control strategy was used based on the measurement of differential pressure fluctuations, of which the variance is used as the operational setpoint. Clough and Gyure (1984) used differential pressure across some portion of a fluidized bed to control its state of fluidization. The fluctuations of differential pressure were modeled as a time series (viz., described by an autoregressive model) and related to the degree of mixing of gas and solids within the bed.…”

Section: Aiche Journalmentioning

confidence: 99%

Monitoring the quality of fluidization using the short‐term predictability of pressure fluctuations

1998

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In uarious industrial applications of bubbling fluidized beds, defluidizing parts of the Fluidization QualityGas-solid fluidized beds are among the most important reactor systems in the chemical industry, because they offer excellent possibilities for dealing with chemical reactants in reaction systems in which good fluid mixing, high heat and mass-transfer rates, and low pressure drops are required. Typical examples of industrial applications of gas-solid fluidized beds include synthesis and catalytic reactions, chlorination of metal oxides, catalyst regeneration, and combustion and gasification of coal. Fluidized beds are also used for physical processes in which the excellent heat and masstransfer characteristics of these reactors are exploited. Typical examples include drying of particles, cooling of particles,

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Section: Aiche Journalmentioning

confidence: 99%

Monitoring the quality of fluidization using the short‐term predictability of pressure fluctuations

1998

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“…The MIMO system, comprising interactions between temperature and humidity, was decoupled into two independent control loops, with a regulator designed by Jaksoo et al [61]. Clough and Gyure [62] measured differential pressure in the fluidized bed and correlated it with the degree of mixing and turbulence within the bed. The stochastic model parameters were estimated by a recursive least squares method.…”

Section: Control Of Fluidized Bed Dryersmentioning

confidence: 99%

Fluidized bed dryers: Dynamic modelling and control

Panda

Rao

1991

Chem Eng & Technol

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Fluidized bed drying finds important applications in the chemical industry on account of the following advantages: a) rapid exchange of heat and mass between drying media and particles yields the desired product quality and reduces the overall drying time and b) easy handling of feed and product. There is much scope for improving the existing modelling techniques as applied to predicting the performance characteristics of the dryers. This should provide a sound basis for the implementation of appropriate control strategies. This report is a review of prevailing modelling, identification techniques and control strategies. Hopefully, the up to date information in this paper will be found

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Stochastic analysis of fluctuations in the local void fraction of a gas-solids fluidized bed

Yutani¹,

Ototake²,

Fan³

1986

Powder Technology

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Stochastic Inferential Control of the Fluidized State via Differential Pressure Measurements

Cited by 8 publications

References 1 publication

Monitoring the quality of fluidization using the short‐term predictability of pressure fluctuations

Monitoring the quality of fluidization using the short‐term predictability of pressure fluctuations

Fluidized bed dryers: Dynamic modelling and control

Stochastic analysis of fluctuations in the local void fraction of a gas-solids fluidized bed

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