industrial systems and control Project: emrsn0001
A model of the process is used to provide future predictions on the main steam flow and fuel
set-point, based on least cost, with respect to constraints. The constraints can include the
flue gas O2 and CO levels, the ID fan status, the steam header pressure and the boiler drum
level. Minimisation of the cost function gives the optimal fuel demands which are then
passed to the individual fuel and air masters. The use of energy-based control provides
automatic online corrections for changes in fuel moisture content, availability of fuel and
other factors that might affect the consistency of combustion sufficiently quickly.
Figure 15: SmartProcess Boiler strategy rearranged in a feedback control
formulation
4.2.1 Fuel Prioritisation
Fuel and air demands are computed after the fuel optimisation stage. Consider the case
where natural gas is the most expensive fuel. Its use is controlled by the available amount
of calculated air that remains after considering the burning of low cost fuels. Should this
amount exceed a certain value (e.g. due to limited supply of waste gas or biomass fuel
which leads to an increase in excess air), only then is that path enabled and natural gas can
contribute to the combustion. The same arrangement applies to all fuels and an air leading
fuel relationship is established.
industrial systems and control Project: emrsn0001
4.2.2 Excess Oxygen and Air Flow Control
The total air demand is then determined from the target excess air and the individual
air/fuel to fuel demand ratios as seen in Equation 4, for a three fuel scenario (i.e. natural
gas, waste fuel and fuel oil).
3
Air
Air Demandtotal ( Fueldemand ) Excess Airdemand ,
Fuel
n 1
(4)
n = {nat. gas, waste fuel, oil}
Two feedback controllers in cascade are used to regulate the air flow into the combustion
chamber as seen in Figure 16. A header steam demand measurement is translated into a
demand in excess oxygen which is the set-point for the corresponding controller. A trim is
used to provide a measurement on the current levels of oxygen and compared against the
set-point, gives the error that drives the PI controller. In turn the excess oxygen controller
provides the set-point for the inner final air flow controller.
Figure 16: SmartProcess Boiler Air flow control and cross-limiting
The air flow set-point is adjusted via feedforward action with respect to load variations
which the excess O2 feedback loop maybe too slow to follow (it is common for oxygen
measurements to be relatively slow, resulting in a slow overall feedback response. Another
adjustment comes from the relation between fuel flow and fuel demand. Considering the
time lag between a fuel demand and the actual resulting fuel flow the higher of the two is
selected to create an air leading fuel relationship ensuring that there is always sufficient air
for combustion (cross-limiting) during transient operations. This mechanism mostly applies
to gas or liquid fuels, for solid fuels the air and fuel relationship is managed using
calculations to provide a more flexible and robust strategy.
industrial systems and control Project: emrsn0001
4.2.3 Calorific Value Compensation
As mentioned earlier, alternative fuels like waste gases or biomass exhibit inconsistency in
the energy released upon burning. Variations in pressure and temperature caused by this
effect may compromise stability of operation. Conventional methods to tackle this issue
would see this drop in energy output in temperature and pressure measurements. The
feedback control loops then would react to compensate for these variations by adjusting the
fuel supply accordingly. This approach means that the time before the system actually
responds to this disturbance can be substantial.
A key feature of SmartProcess Boiler utilises the total airflow and excess air measurements
into a simple equation (Equation 5) parameterised with respect to one particular fuel. This
equation is then used to calculate the air that is consumed in combustion and consequently
obtain a more accurate estimate of the energy release. Factors in the equation are
converted from % percentages to a multiplier relative to 1.0.
Actual Airflowtotal Target Excess Airfuel type
Consumed Air Ratio ,
Target Air Demandtotal Actual Excess Air (5)
For example, a consumed air value of 0.95 would mean that the boiler has experienced a 5%
reduction in heat input. This information is then applied as a trim control to the relative fuel
to automatically compensate for the drop, so that the heat release in the boiler remains
more consistent for any given load.
industrial systems and control Project: emrsn0001
5. Conclusions about SmartProcess Boiler
The advantages from using the SmartProcess Boiler application are evident in different
aspects of a combustion process. From an implementation point of view, it is a technology
that integrates well with pre-installed equipment with few requirements, mostly concerning
the operability and configuration of lower regulatory control loops and access to necessary
instrumentation (sensors etc.).
The main feature is fuel delivery optimisation with the use of true Energy control. With only
three measurements (air flow, fuel flow and excess oxygen on the boiler outlet), a
stoichiometric algorithm calculates the energy release from combustion and this is then fed
back to a model predictive controller which rapidly compensates for load variations. In
addition constrained optimisation (available through the predictive action) prioritises
available fuels ensuring the low cost ones are used at up to 60-70%, whereas the use of
auxiliary fuel is restricted to situations when the alternative fuels are limited. As presented
in Figure 6 fuel for a full load steam boiler can rise up to 96% of through life costs when
compared against all other costs like maintenance and capital investment.
A typical paper industry boiler of capacity 105GJ/hr can have an annual natural gas
consumption of 316x103 GJ. Assuming a taxed price of $5 per 1GJ of natural gas from
Figure 7, an annual fuel cost of $1.5M is obtained9. A reduction of 70% in natural gas and
the use of biomass instead to meet the same calorific demand could give a total fuel cost of
$0.6M ($0.4M of natural gas - 30% and $0.2M of biomass - 70%), corresponding to an
annual profit of $900K. Obviously these numbers will vary depending on the exact
application, boiler size and fuel costs. There would be an even higher gain if the specific
fossil fuel was related to high CO2 taxation. In a similar process site in North Carolina an
equivalent reduction of 70% in fossil fuel use was achieved by installing the SmartProcess
Boiler application. That along with improvements in efficiency and steam variability resulted
in $1.2M of annual savings.
By avoiding air/fuel characteristic curves and the fidelity of measurements through the use
of an energy release algorithm, lower excess O2 levels are achievable. Figure 4 shows that
for large scale boilers even a 2% decrease in excess O2 can bring substantial economic
savings.
The quality and supply of fuels is monitored online and output energy deviations due to
moisture content and/or supply variations are directly compensated. This effectively reduces
variability of steam generation and allows process set-points to move closer to certain limits
where maximum efficiency is achieved. An example of this can be found in Johnman ET al19
for a boiler final steam temperature control loop. Due to high variability of the process,
steam temperature is set below the maximum achievable value. That study estimated that
by reducing variability through improved control, the steam temperature set-point could be
increased by 6 °C giving a total annual benefit of approximately $1M from improved process
efficiency.
Advanced control has much to offer in this application area and potential benefits are easy
to determine, however transforming and upgrading existing control systems can involve
considerable effort and require both expertise and experience. Further minor changes to
existing classical controls are unlikely to lead to meaningful improvements and hence a
commercial product that is easy to apply in many different situations is valuable.
From the review completed the SmartProcess Boiler application has significant benefit and
integrates well with the plant and can be extended with other optimisation packages like the
SmartProcess Header, SmartProcess Energy and the Delta V Burner Management Systems
industrial systems and control Project: emrsn0001
(BMS) (not examined in this report). It is based upon proven predictive control and can
ultimately deliver maximum efficiency and coordinate combustion of up to 7 boilers on any
combination of fuels and availability.
industrial systems and control Project: emrsn0001
References
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[12] http://www.bbc.co.uk/news/uk-england-15493889
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http://www.tebs.uk.com/Biomass%20Case%20Study.pdf
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[15] R. Sabin, “Optimize Multi-Fuel Boiler Operation with Modern Control”, Control
Engineering, Dec. 2012.
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http://www.isa.org/Template.cfm?Section=Technical_Paper_Collections&template=
/Ecommerce/ProductDisplay.cfm&ProductID=10951#
