5. Robust adaptive control of co-combustion process, using optical signals

For the proper boiler's power operation, the opportunity to assess the quality of combustion is critical [49]. The combustion flow in layers influences on the speed of chemical reactions, heat transfer efficiency, flame stability and the generation of NOx and CO. According to sources [49–51], the type of burner, fuel type and the control method have the crucial effect on the formation of combustion aerodynamics.

A potential problem of complex control systems, for example, the combustion process, is difficult (and thus is not full) measuring the physical-chemical quantities. In the proposed solution, a classical approach is supplemented with information about the image parameters

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As a result, the analyses highlighted the relationship between the parameters that describe the variation of the flame and the temperature of the exhaust gas in the chamber or the amount of air flow in the secondary factor. Thus, if the temperature is slowly varying size, having an inert nature, the synthesis of the controller can be used quick-picture (actually a parameter or group

Primary air is used mainly for delivering pulverised coal to the burner nozzle, whereas secondary air is used for regulation purposes. Input parameters, such as the coal-biomass mixture and air flows, were changed several times during the tests to create various combus-

Due to the incomplete knowledge of the control object or its rapid changes in performance, the

The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network. The NARX model is based on the linear ARX model, which is commonly used in time-series modelling. The

where the next value of the dependent output signal y(t) is regressed on previous values of the output signal and previous values of an independent (exogenous) input signal. The NARX model can be implemented using a feedforward neural network to approximate the function f. This implementation also allows for a vector ARX model, where the input and output can be

The output of the NARX network can be considered as an estimate of the output of the modelled nonlinear dynamic system. The output is fed back to the input of the feedforward neural network as part of the standard NARX architecture. Regarding to the fact that the true output is available during the training of the network, it is possible to create a series-parallel architecture (see [56]), in which the true output is used instead of feeding back the estimated

The custom architecture used for further analyses is the model reference adaptive control (MRAC) system. Such a model reference control architecture has two subnetworks (see Figure 4). One subnetwork is the model of the plant to be controlled. The other subnetwork is the controller. Obtaining the trained NARX plant model, it is possible to create the total MRAC system and insert the NARX model inside and then add the feedback connections to the feedforward

network. The next stage was focused on training of controller subnetwork.

 ; u tð Þ � <sup>1</sup> ;…; y tð Þ � nu , (9)

flame, registered a high-speed camera.

adaptive control seems to be a reasonable approach.

defining equation for the NARX model is as follows:

y tðÞ¼ f ytð Þ � 1 ;…; y t � ny

of the image parameters).

tion states.

multidimensional.

output.

Low-emission burners use the reducing properties of enriched flame by the organisation of under stoichiometric combustion zones using air or fuel staging. However, it should be noted that dust excess conditions may deteriorate and increase the unburnt loss.

Considering both environmental aspects and factors mentioned above, there is a need for a novel combustion process control system. Its specific requirements are based on the use of combustion information obtained both from conventional instrumentation and innovative techniques.

Ensuring the flame stability and the fault states, detection seems to be the most important parameters from the technological point of view. It affected the use of video technology and fibre-optic probes to complete the diagnostic information about the flame for the control system. In order to provide online, normative, emission constraints, the quantitative information on the concentration of nitrogen oxides (NOx), carbon oxides (CO) and sulphur dioxide (SO2) is equally important. Apart from the importance of the process state appropriate parameters selection, the selection and placement of measuring devices in such difficult industrial conditions stand a separate issue.

The change of the co-combustion process organisation stands the most popular NOx emissions reducing method. However, it causes negative consequences for the boiler operation. This results in the higher unburnt loss, increased CO emissions, the increased slagging, evaporator corrosion and instability of the flame.

Due to the fact that these phenomena are undesirable or even dangerous for the boiler, it is very difficult to achieve NOx reduction at an appropriate level. Introducing the appropriate monitoring and control system can be a solution to the problem. The advanced combustion control systems introduce additional structural modifications and signals in the form of separate air flow to individual burners, OFA nozzles and mill load or additional signals from the exhaust gas analysers such as NOx, CO, and SO2. Due to the fact that the excess air determines the amount of NOx generated in the coal boiler energy [49, 52], the combustion process control in a single burner would be the advantage.

The combustion process occurring in chemical reactions and physical processes can be reflected via radiation emitted by the flame. In the current state of the art, non-delayed and spatially selective additional information about the ongoing combustion process can be delivered non-invasively only using optical or acoustic diagnostic methods. It is possible to include determination of the air-fuel ratio, the quantity of heat release and temperature regarding the spectrum of flames in the visible emission. The image processing-based approach seems to be particularly important, because still and the apparent position of the flame stands the result of a dynamic equilibrium between the local flame propagation speed and the speed of the incoming fuel mixture. On this basis, it is assumed that the flame front position changes may be an indicator of this balance imminent distortion, occurring under certain conditions [53–56]. A potential problem of complex control systems, for example, the combustion process, is difficult (and thus is not full) measuring the physical-chemical quantities. In the proposed solution, a classical approach is supplemented with information about the image parameters flame, registered a high-speed camera.

transfer efficiency, flame stability and the generation of NOx and CO. According to sources [49–51], the type of burner, fuel type and the control method have the crucial effect on the

Low-emission burners use the reducing properties of enriched flame by the organisation of under stoichiometric combustion zones using air or fuel staging. However, it should be noted

Considering both environmental aspects and factors mentioned above, there is a need for a novel combustion process control system. Its specific requirements are based on the use of combustion information obtained both from conventional instrumentation and innovative

Ensuring the flame stability and the fault states, detection seems to be the most important parameters from the technological point of view. It affected the use of video technology and fibre-optic probes to complete the diagnostic information about the flame for the control system. In order to provide online, normative, emission constraints, the quantitative information on the concentration of nitrogen oxides (NOx), carbon oxides (CO) and sulphur dioxide (SO2) is equally important. Apart from the importance of the process state appropriate parameters selection, the selection and placement of measuring devices in such difficult industrial

The change of the co-combustion process organisation stands the most popular NOx emissions reducing method. However, it causes negative consequences for the boiler operation. This results in the higher unburnt loss, increased CO emissions, the increased slagging, evaporator

Due to the fact that these phenomena are undesirable or even dangerous for the boiler, it is very difficult to achieve NOx reduction at an appropriate level. Introducing the appropriate monitoring and control system can be a solution to the problem. The advanced combustion control systems introduce additional structural modifications and signals in the form of separate air flow to individual burners, OFA nozzles and mill load or additional signals from the exhaust gas analysers such as NOx, CO, and SO2. Due to the fact that the excess air determines the amount of NOx generated in the coal boiler energy [49, 52], the combustion process control

The combustion process occurring in chemical reactions and physical processes can be reflected via radiation emitted by the flame. In the current state of the art, non-delayed and spatially selective additional information about the ongoing combustion process can be delivered non-invasively only using optical or acoustic diagnostic methods. It is possible to include determination of the air-fuel ratio, the quantity of heat release and temperature regarding the spectrum of flames in the visible emission. The image processing-based approach seems to be particularly important, because still and the apparent position of the flame stands the result of a dynamic equilibrium between the local flame propagation speed and the speed of the incoming fuel mixture. On this basis, it is assumed that the flame front position changes may be an indicator of this balance imminent distortion, occurring under certain conditions [53–56].

that dust excess conditions may deteriorate and increase the unburnt loss.

formation of combustion aerodynamics.

246 Adaptive Robust Control Systems

conditions stand a separate issue.

corrosion and instability of the flame.

in a single burner would be the advantage.

techniques.

As a result, the analyses highlighted the relationship between the parameters that describe the variation of the flame and the temperature of the exhaust gas in the chamber or the amount of air flow in the secondary factor. Thus, if the temperature is slowly varying size, having an inert nature, the synthesis of the controller can be used quick-picture (actually a parameter or group of the image parameters).

Primary air is used mainly for delivering pulverised coal to the burner nozzle, whereas secondary air is used for regulation purposes. Input parameters, such as the coal-biomass mixture and air flows, were changed several times during the tests to create various combustion states.

Due to the incomplete knowledge of the control object or its rapid changes in performance, the adaptive control seems to be a reasonable approach.

The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network. The NARX model is based on the linear ARX model, which is commonly used in time-series modelling. The defining equation for the NARX model is as follows:

$$y(t) = f\left(y(t-1), \dots, y(t-n\_{\mathcal{Y}}), u(t-1), \dots, y(t-n\_{u})\right),\tag{9}$$

where the next value of the dependent output signal y(t) is regressed on previous values of the output signal and previous values of an independent (exogenous) input signal. The NARX model can be implemented using a feedforward neural network to approximate the function f. This implementation also allows for a vector ARX model, where the input and output can be multidimensional.

The output of the NARX network can be considered as an estimate of the output of the modelled nonlinear dynamic system. The output is fed back to the input of the feedforward neural network as part of the standard NARX architecture. Regarding to the fact that the true output is available during the training of the network, it is possible to create a series-parallel architecture (see [56]), in which the true output is used instead of feeding back the estimated output.

The custom architecture used for further analyses is the model reference adaptive control (MRAC) system. Such a model reference control architecture has two subnetworks (see Figure 4). One subnetwork is the model of the plant to be controlled. The other subnetwork is the controller. Obtaining the trained NARX plant model, it is possible to create the total MRAC system and insert the NARX model inside and then add the feedback connections to the feedforward network. The next stage was focused on training of controller subnetwork.

Figure 4. MRAC control scheme.

In order to make the closed-loop MRAC system responding in the same way as the reference model (used to generate data), the weights from the trained plant model network ought to be inserted into the appropriate location of the MRAC system. Then to achieve plant an initial input of zero, the output weights of the controller network were set to zero.

The training of the MRAC system took much longer than the training of the NARX plant model regarding to the fact that the network is recurrent and dynamic backpropagation was used. After the network was trained, it was tested by applying a test input to the MRAC network.

There were two MRAC systems designed and compared. The first one used non-optic, measurement-based set of input vectors, respectively quantitatively describe the flow of secondary air, fuel expense and vectors describing respectively exhaust temperature in the chamber, recorded in the first measurement point. The second scheme used secondary air flow control signal and chosen flame image descriptors (Otsu's method based - flame surface area and contour length).

Figure 5 shows system response to the system reference input in both cases: with classic measurements (a) and when a flame image descriptor contour length vector was applied (b).

Simulation results shown in Figure 5 reveal that the plant model output does follow the reference input with the correct critically damped response, even though the input sequence was not the same as the input sequence in the training data. The steady state response is not perfect for each step, but this could be improved with a larger training set and perhaps more hidden neurons. From the obtained results of the proposed neural adaptive controls, it can be concluded that control signals are bounded, abrupt changes of system parameters involve sudden changes of amplitudes of command laws and the outputs of the controlled system.

As mentioned before, imposing constraints can be a way of guaranteeing robustness. The analysed control system was evaluated by simulating a sudden step change of the load request. This test replicates the critical situation that occurs when an unexpected change of power and NOx radicals takes place. The results are presented in Figure 6.

6. Conclusion

The flame area can be one of the crucial pointers of combustion process state [43–49, 57]. Therefore, it can be easily estimated in a series of images and it could be used in real-time applications regardless the place of camera mounting. Investigated factors used for combustion

Figure 6. MIMO controller response to sudden change of power load regarding to the relationship between the concen-

Figure 5. MRAC system response to the system reference input: (a) without additional information from optical signals

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and (b) with flame image descriptor signal included in the control scheme.

trations of NOx, CO, flue gas temperature in the combustion chamber for two reference models m1 and m2.

The constraints are satisfied because algorithm checked all possible values of the uncertainties.

Adaptive Robust Control of Biomass Fuel Co-Combustion Process http://dx.doi.org/10.5772/intechopen.71576 249

Figure 5. MRAC system response to the system reference input: (a) without additional information from optical signals and (b) with flame image descriptor signal included in the control scheme.

Figure 6. MIMO controller response to sudden change of power load regarding to the relationship between the concentrations of NOx, CO, flue gas temperature in the combustion chamber for two reference models m1 and m2.

#### 6. Conclusion

In order to make the closed-loop MRAC system responding in the same way as the reference model (used to generate data), the weights from the trained plant model network ought to be inserted into the appropriate location of the MRAC system. Then to achieve plant an initial

The training of the MRAC system took much longer than the training of the NARX plant model regarding to the fact that the network is recurrent and dynamic backpropagation was used. After the network was trained, it was tested by applying a test input to the MRAC network.

There were two MRAC systems designed and compared. The first one used non-optic, measurement-based set of input vectors, respectively quantitatively describe the flow of secondary air, fuel expense and vectors describing respectively exhaust temperature in the chamber, recorded in the first measurement point. The second scheme used secondary air flow control signal and chosen flame image descriptors (Otsu's method based - flame surface area and

Figure 5 shows system response to the system reference input in both cases: with classic measurements (a) and when a flame image descriptor contour length vector was applied (b). Simulation results shown in Figure 5 reveal that the plant model output does follow the reference input with the correct critically damped response, even though the input sequence was not the same as the input sequence in the training data. The steady state response is not perfect for each step, but this could be improved with a larger training set and perhaps more hidden neurons. From the obtained results of the proposed neural adaptive controls, it can be concluded that control signals are bounded, abrupt changes of system parameters involve sudden changes of amplitudes of command laws and the outputs of the controlled system.

As mentioned before, imposing constraints can be a way of guaranteeing robustness. The analysed control system was evaluated by simulating a sudden step change of the load request. This test replicates the critical situation that occurs when an unexpected change of

The constraints are satisfied because algorithm checked all possible values of the uncertainties.

power and NOx radicals takes place. The results are presented in Figure 6.

input of zero, the output weights of the controller network were set to zero.

contour length).

Figure 4. MRAC control scheme.

248 Adaptive Robust Control Systems

The flame area can be one of the crucial pointers of combustion process state [43–49, 57]. Therefore, it can be easily estimated in a series of images and it could be used in real-time applications regardless the place of camera mounting. Investigated factors used for combustion process assessment cannot be used directly in full-scale combustion facilities, due to the fact that they strongly depend on the burner type and size of combustion chamber.

[2] Hein K, Bemtgen J. EU clean coal technology—Co-combustion of coal and biomass. Fuel

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Radicals (NOx, CO and SO2) emission requirements are becoming more restrictive. As a result, the optimum control of combustion using low-carbon technologies seems to be very important.

The paper covered the conditions for the development of the combustion process control system as well as elaborated optimal algorithm. The aim of the algorithm was to optimise the boiler operation based on information obtained from conventional instrumentation and incorporate innovative techniques to assess the quality of the process.

The correction signals introduced by the optimising algorithm are indeed small. The proposed simulation of the MIMO controller results in better, robust performance. The evaluation of the control signals indicates a negligible change in magnitude of input signals. Consideration of uncertainties can be a considerable interest. If a model predictive controller takes into account the constraints are used, it will solve the problem, keeping the expected values of the output signals within the feasible region, but due to the external perturbations or uncertainties, this does not guarantee that any output is going to be bound. In case of uncertainties, MPC minimises the objective function for the worst situation and keeps the value of the variables within the constraint region for possible cases of uncertainties.

The increment of the prediction horizon n allows better performance since a greater prediction of the future error is possible. While applying temperature values, its error weight must be high regarding to the fact that the classical temperature regulation is slow and responsible for overall performance. Too big value of the control horizon returns undesired oscillations.

As it was mentioned before, biomass co-combustion process is difficult to control. Research on using the presented approach extends the possibilities of modern combustion processes and makes them more flexible to maintain.
