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RESEARCH PAPERS

Early Agglomeration Recognition System (EARS)

[+] Author and Article Information
R. Korbee

 Energy Research Centre of the Netherlands (ECN), P.O. Box 1, 1755 ZG Petten, The Netherlandskorbee@ecn.nl

J. R. van Ommen

Reactor and Catalysis Engineering, Delft University of Technology, Julianalaan 136, 2628 BL Delft, The Netherlandsj.r.vanommen@tnw.tudelft.nl

J. Lensselink

 Energy Research Centre of the Netherlands (ECN), P.O. Box 1, 1755 ZG Petten, The Netherlands

J. Nijenhuis

Reactor and Catalysis Engineering, Delft University of Technology, Julianalaan 136, 2628 BL Delft, The Netherlands

J. H. Kiel

 Energy Research Centre of the Netherlands (ECN), P.O. Box 1, 1755 ZG Petten, The Netherlands

C. M. van den Bleek

Reactor and Catalysis Engineering, Delft University of Technology, Julianalaan 136, 2628 BL Delft, The Netherlands

J. Energy Resour. Technol 128(2), 143-149 (Feb 03, 2006) (7 pages) doi:10.1115/1.2191505 History: Received June 11, 2004; Revised February 03, 2006

In fluidized-bed combustion and gasification of biomass and waste, agglomeration of bed/ash particles is a major problem area. This paper deals with a new method for monitoring and controlling fluidized-bed hydrodynamics, which enables the recognition of agglomeration in an early stage and provides control measures to prevent further agglomeration and defluidization. The method, called early agglomeration recognition system (EARS), is based on recognizing significant differences between reference time series of pressure fluctuations and successive time series measured during prolonged plant operation. The early recognition provides a time interval for taking dedicated actions to counteract the agglomeration. Thus, EARS can be a tool to help plant operators prevent agglomeration-induced plant shutdowns and minimize bed material makeup and residue production. Results are presented of small-scale experiments showing the effectiveness and selectivity of the early agglomeration recognition. Subsequently, the development of control strategies is discussed.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Reconstruction of an attractor in the m-dimensional state space from a pressure time series

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Figure 2

Schematic representation of the EARS monitoring method

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Figure 3

Schematic representation of the 1kg∕h bubbling-fluidized-bed gasifier/combustor; sizes in mm

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Figure 4

Effectiveness of EARS for bench-scale gasification of (a) miscanthus, (b) mixture chicken manure and beech wood, (c) waste wood

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Figure 5

Influence of the superficial gas velocity on the S value

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Figure 6

Influence of the bed mass on the S value

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Figure 7

Sand particle showing biomass ash-derived coating and internal cracks

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Figure 8

Minimum fluidizing velocity determined as a function of temperature. The lines represent calculated values.

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Figure 9

S value before, during, and after the addition of water to a bed of coke particles. The dashed rectangle indicates the period of water addition.

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