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

Boiler opseration change
Implementation of a slagging prediction tool to lignite blend fired boilers VGB PowerTech 10 l 2020
Fuel Data
C, H, N, S, Ash, H 2 0
HHV, Ash oxide
composition
Fuel Database
Module
Methodology
Boiler Geometry Data
Boiler dimensions,
HE surface arrangement,
Burners and OFA elevations
Slagging/Fouling Prediction Tool
Boiler Database &
Model Implementation
Boiler thermodynamic
Zone-based Model
Ash Deposition Model
Slagging/Fouling Indices
Slagging/Fouling Risk
Boiler Operation Data
Boiler load,
Mills operation, Excess Air,
Soot blowers activity
Results
Database
Module
LOW MEDIUM HIGH
Fig. 1. Structure of the developed slagging prediction tool [2].
Phase Eq. Module
Thermochemical
database
ChemApp solver
Changes in fuel composition
deposit removal technologies like soot
blowing, wall blowing and/or water cannons
could be employed, depending on the
type of deposits. A comprehensive list of
solutions to mitigate deposition problems
are discussed in Ref. [3] and [4].
In summary, the implementation of the
slagging prediction tool to a new unit involves
data collection that includes boiler
geometry, heat transfer surface arrangement,
water/steam cycle information as
well as day-to-day boiler operational/process
data. The collected data is used to develop
the 1-D zone-based model, which is
then integrated with output from the fuel
database for the final analysis.
In this study, the model was implemented
to the Boxberg Unit Q supercritical pulverized
fuel boiler that fires lignite blends
sourced from Nochten and Reichwalde
open cast mines. It was commissioned in
2000, and currently delivers an electrical
output of 907 MW el with a net electrical efficiency
of 42 %. It is a tower type boiler
with a height of 143 m (last heat exchange
surface, boiler roof is 166 m), and spiralwater
furnace walls of the evaporator up to
a height of 68 m. At the furnace outlet (approximately
97 m) the platen type superheater
2 is located. In the convective section
a total of four superheaters, two reheaters,
as well as the economiser are
accommodated. At an elevation of 76 m,
there are a total of eight flue gas recirculation
inlets, two on each boiler wall. The
recirculated flue gas is used to dry the raw
lignite in the eight mills. The 32 burners
installed are mounted around the boiler,
eight on each wall, aligned four in a row on
two levels. The advantage of this type of
based compounds that are formed in the
atmosphere of the high-temperature boiler.
Implementation of the slagging
The developed model indicates the propensity
of a given fuel to slag or foul by
prediction tool
analyzing the proportion of the ash that is
The developed predictive methodology integrates
a one-dimensional zone model of der various boiler operating conditions.
in the solid and molten (liquid) phases un-
a boiler to determine the heat transfer conditions
and furnace temperature profiles sition risk by the model, proactive options
Based on the extent of the estimated depo-
[1]. This is coupled with a comprehensive can be explored to minimize impacts due to
mechanistic ash deposition model that utilises
thermochemical and ash melting could include changing the boiler opera-
slagging or fouling. For instance, options
data. The noted approach allows an early tional parameters like controlled load
assessment of slagging and fouling risk change, adjusting the air-to-fuel ratio or
across different boiler sections (divided the possibility of altering fuel blends with a
into several zones) as a function of fuel lower risk potential using the model generated
fuel flexibility windows. Alternatively,
composition and combustion conditions,
such as boiler load, fuel/air distribution
and/or NO x control. The final platform is
structured as an iterative process that utilizes
inputs from three specific modules
listed below and shown in F i g u r e 1 [2]:
––
Fuel database module: Stores all information
from fuel analysis including
proximate, ultimate and ash analysis;
Information stored in this module is used
to calculate ash melting characteristics
for the range of fuels and/or blends considered.
––
Boiler module: This module includes
boiler geometry information and heat
transfer surface arrangement as well as
day-to-day operational data.
––
Results database & indices module: This
module serves as a repository of input/
output data from multiple sources and
also performs data analysis and comparative
evaluations.
Alongside the physical modeling of the
boiler, a complementary development of a
thermo-chemical phase equilibrium module
that utilises a GTOx based thermochemical
project database and phase equilibrium
solver (“ChemApp”) was used. This
was designed to investigate the phase distribution
of the complex mélange of ash-
Fig. 2. Interface of the Slagging Predictor with focus on the Boxberg boiler layout.
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