VGB POWERTECH 10 (2020) - International Journal for Generation and Storage of Electricity and Heat

VGB PowerTech 10 l 2020
Implementation of a slagging prediction tool to lignite blend fired boilers
Implementation of the next
generation slagging prediction tool
to a large scale pf boiler fired
with lignite blends
Piotr Plaza, Bernhard Schopfer, Jörg Maier, Thomas Brunne and Günter Scheffknecht
Kurzfassung
Einsatz des Verschlackungsvorhersage-
Tools der nächsten Generation für einen
in großem Maßstab staubbefeuerten
Kessel mit Braunkohlemischungen
Eine schwankende Brennstoffqualität kann den
Betrieb von modernen, mit Braunkohle befeuerten
Großkesseln erheblich beeinträchtigen. Eines
der Hauptrisiken bei abnehmender Aschequalität,
ist mit dem Auftreten von erhöhter Verschlackung/Verschmutzung
in einem Kessel verbunden.
Die Befeuerung mit einer geeigneten Braunkohlemischung
und/oder die Änderung der Kesselbetriebsbedingungen
können das Risiko mindern.
Eine Vorhersage mit herkömmlichen Verschlackungsindizes
ist jedoch für komplexe Brennstoffmischungen
nicht geeignet und berücksichtigt den
Kesselbetrieb nicht.
In diesem Beitrag werden relevante Ergebnisse
zum Einsatz und zur Validierung eines Verschlackungsvorhersagetools
der nächsten Generation
im Kraftwerk Boxberg (Block Q), welches mit Mischungen
aus Nochtener und Reichwalder Braunkohle
befeuert wird, vorgestellt.
Darüber hinaus wurden die Ergebnisse der entwickelten
Online-Depositionsratesonde im Vergleich
zu den Betriebsdaten des Kessels und den vom Verschlackungsüberwachungssystem
während einer
zweiwöchigen Messkampagne im Kraftwerk Boxberg
gesammelten Daten bewertet.
Der Einsatz des entwickelten Verschlackungsvorhersagetools
in Kombination mit einer Überwachung
durch die Online-Depositionsratesonde
kann den Betreibern helfen, einen Großteil der
Unsicherheiten zu vermeiden, die mit Entscheidungen
im Zusammenhang mit der Brennstoffauswahl
und den Betriebsverfahren verbunden
sind, um einen hocheffizienten Kesselbetrieb ohne
erhöhtes Verschlackungsrisiko zu erreichen. l
Authors
Piotr Plaza
Bernhard Schopfer
Jörg Maier
Günter Scheffknecht
Stuttgart University
Institute of Combustion and Power Plant
Technology, Stuttgart, Germany
Thomas Brunne
Lausitz Energie Kraftwerke AG
Cottbus, Germany
The changing quality of lignite may significantly
affect the operation of modern largescale
lignite-fired pulverised fuel boilers. One
of the main risks is associated with a varying
ash quality and the occurrence of elevated
slagging/fouling in a boiler. Firing a proper
mixture of lignite coals or/and changing
boiler operational conditions can mitigate
the risk. However, the conventional predictive
slagging indices are not adequate for
complex fuel blends and they do not consider
boiler operations. This paper presents relevant
results from the implementation and
validation of the next generation slagging
prediction tool in the Boxberg power plant
(Unit Q) fired with blends of Nochten and
Reichwalde lignite. In addition, the performance
of the developed online ash deposition
probe was evaluated against boiler operational
data and data collected from the
slagging monitoring system during a twoweek
measurement campaign performed in
the Boxberg power plant. The use of the slagging
prediction tool developed combined
with the online ash deposition monitoring
probe can help operators avoide much of
the uncertainty associated with decisions related
to fuel selection and operating procedures
in order to achieve a highly efficient
boiler operation without an elevated slagging
risk.
Introduction
The quality of lignite can vary significantly
within the same opencast mine, which
may impact the continuous operation of
lignite-fired pulverised fuel boilers optimised
to operate properly for certain
coal qualities. Nowadays, modern power
plants are equipped with a quantity and
quality management system that ensure
the correct range of coal quality is supplied.
Such a fuel quality management system
is used in a Boxberg lignite-fired power
plant in Germany. It has to ensure that
during continuous operation the coal supplied
to the Boxberg power plant has to
be mixed at a ratio of 50 wt% to wt70 %
Nochten coal to 30 wt% to 50 wt% Reichwalde
coal from the two East Saxon opencast
mines. Nevertheless, due to a constantly
changing quality of coals that may
lead to increased slagging issues, this ratio
is recently being optimised. However, the
conventional slagging predictive indices
used, such as base-to-acid ratio, developed
for coals based on a known ash oxide composition,
are less valid for coals or coal
blends with increased iron (mostly originated
from pyrite) and calcium contents in
the ash due to complex, non-linear molten
phase formation behaviours. For such complex
chemical systems the predictive models
should be based on multicomponent
thermodynamic phase equilibrium calculations
and include specific boiler operating
conditions. The impact of a reducing
atmosphere and temperature is of high importance
especially for lignite pf fired boilers,
which are operated under deep air
staging conditions in order to reduce NO x
formation. In order to maintain high efficiency
of the boiler and continuous operation,
advanced “smart” ash deposition
monitoring and cleaning systems are used.
Such systems are crucial for adopting appropriate,
cost-effective cleaning patterns
and soot-blowers optimisation procedures.
In addition, data derived from the monitoring
tools can also bring valuable information
for validation of the slagging predictive
models.
In this paper, the results obtained from
the testing and validation of the slagging
prediction tool with the IFK online
deposition monitoring probe in the lignitefired
Boxberg power plant (Unit Q) are presented.
The performance of the developed
tools was evaluated against boiler
operational data and data collected from
the slagging monitoring system (SMART
InfraScan) during a two-week measurement
campaign. The presented activities
were part of the VerSi project coordinated
by Stuttgart University, IFK and funded
by BMWi (Federal Ministry for Economic
Affairs and Energy) and Industrial
Partners involved in the project realization.
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