- Email: [email protected]
Control for Ecological Improvement of Small Biomass Boilers
Control for Ecological Improvement of Small Biomass Boilers Bohumil Šulc, Stanislav Vrána* Jan Hrdlička** Martin Lepold***
*Czech Technical University in Prague, Dept. Instrumentation & Control Engineering, Czech Republic (Tel: +420 2435 2531; e-mail: [email protected]; [email protected];[email protected]). **Czech Technical University in Prague, Dept. Fluid Dynamics and Power Engineering (e-mail: [email protected]) *** I&C Energo, Praha, Czech Republic, (e-mail: [email protected])} Abstract: The aim of this paper is to present various types of research on reducing emissions in the flue gases produced by low-power biomass boilers, especially when the boilers operate outside their nominal operating regime. This long-term investigation program is being carried out on a pilot boiler in the university laboratories. The program involves acquiring additional instrumentation for performing and evaluating present and future experiments, creating various types of models of the boiler for designing and testing the control algorithms using available data, and implementing and evaluating the algorithms in real operation. The pilot device enables us to experiment using non-standard instrumentation, but throughout our research it is necessary to strike a balance between ecological policy requirements and economic considerations. Keywords: biomass, boiler, flue gases, control, fuzzy model
1
INTRODUCTION
During the last decade, biomass has become the most widely used renewable energy source in the Czech Republic, mainly due to its availability and acceptable price. Biomass nowadays often replaces lignite coal and electricity in heating systems. There are various sources of biomass, e.g. from forestry or from agriculture. For local decentralized heat production, i.e. particularly for households, biomass is distributed in various forms of products, including wooden briquettes and pellets. Such fuels are often used by people who know very little about boiler operation. In addition, there are currently no valid emission limits for CO, NOx or TOC for small sources not exceeding 50 kW of power output. This situation often results in inefficient biomass combustion, with a high concentration of undesirable products in the flue gases, although biomass is considered as a “clean fuel”. (Kaltschmitt, Hartmann, 2001). However, ordinary biomass users cannot be required to check the correct operation of the boiler periodically. Because several thousand small boilers are working and there is no real valuable feedback from their operation, the situation requires a simple and cheap solution that can be introduced easily. Therefore there is a need for biomass boiler producers to promote advanced boilers that fulfil all requirements for clean, safe and user-friendly automated operation. However, due to the lack of emission limits for gaseous pollutants from small boilers, there is no impetus for producers to focus on this issue. This research project aims to offer producers a relatively cheap way to improve their boiler control units to deal with all these gaseous pollutants and efficiency issues. Our
research focuses on the control system, one of the most important elements in the successful operation of a biomass boiler. An obvious limitation on introducing advanced control systems using various sensors as the control input is the price, which must not be raised unacceptably. For this reason, small biomass boilers are equipped with simple control systems that are not fully able to control the whole combustion process actively. Such systems often control only the power output of the boiler by means of changes in fuel feeding. Due to process discontinuity, the operation of the boiler often shows sudden changes in pollutant emissions that are not handled in any way. Our aim is to find a solution for these situations, e.g. by real-time continuous operation monitoring, and to develop ways to prevent these undesirable changes in emissions. 2
PROBLEM OF EMISSION MINIMIZATION IN BIOMASS COMBUSTION DEVICES
Every combustion device produces not only a flue gas containing CO2 as a final product of oxidation of all carboncontaining fuels and water vapour, but also other components that can be dangerous (e.g. CO, NOx), or efficiencydecreasing components (e.g. CO, CxHy) as products of incomplete combustion. To decrease incomplete combustion, the process is carried out with some excess of combustion air, which is expressed by factor λ
λ=
Qa > 1 [ −] Qa min
(1)
where Qa is the flow rate of the actual combustion air and Qamin is the smallest necessary (stoichiometric) burning air.
The current value of the air factor λ in the running combustion process is acquired via oxygen concentration measurement in the flue gases at the end part of the boiler. This factor needs to be controlled to balance all influences on the combustion process, particularly to balance the CO and NOx concentrations with the combustion chamber temperature. Oxygen sensor discredibility means that that the sensor is not out of function, but that its properties have gradually changed to the extent that the sensor has started to provide biased data (Šulc, Klimánek, 2008). Fig. 1 depicts the optimal range of the air factor. If the air factor is between αmin and αmax, then the emissions of CO and NOx will not exceed the maximum acceptable level. However, the problem is that oxygen probes are vulnerable to faults. If the oxygen probe starts to provide biased information about the oxygen content in the flue gases, the emissions of CO and NOx may be excessive, and penalties can be incurred for undesirable environmental impacts. Thus it is essential to avoid any unrecognized increase in emissions, particularly of CO and NOx, by detecting possible oxygen sensor discredibility. 3
CONTROL TASKS
Most biomass boilers serving as heat sources in local heating systems are equipped with a very simple automatic function targeting automatic operation, i.e. maintaining the temperature of the heating water, automatic ignition, fuel supply, grating, etc. Until now, little attention has been paid to control, and this has had a direct impact on the environment. Unlike coal-fired boilers, biomass boilers with low power are not subject to strict emission regulations,. Producers therefore hesitate to introduce expensive control precautions that are necessary only for environmental reasons. However, oxygen probes have recently come into use to increase the efficiency of the boiler by finding the optimum fuel-air ratio. This brings a new challenge in control design – it is reasonable to equip the controller with additional features
Fig. 2. Functional scheme of the biomass boiler set-up of autotuning and detection of possible sensor discredibility (Klimánek, Šulc, 2008). Both functions can be carried by software added to the control algorithm. This means only a minor increase in price and a major reduction in harmful emissions. Fig. 2 schematically shows the arrangement of the boiler used in the experiments discussed here. This is connected with another control problem that we have detected when measuring emissions – the rapid increase of emissions in a time interval after grating. This phenomenon, together with possible control solutions to it, based on additional instrumentation (Åstrom, Hägglund, 1995, 2005), is a major point of interest for further research. 4
SPECIALIZED INSTRUMENTATION FOR CONTROL EXPERIMENTS
Instead of the pre-programmed fixed logic standardly delivered with the boiler, the new RexWinLab-8000 control system has been proposed. RexWinLab-8000 is a station based on the WinCon Programmable Automation Controller, which contains five plug-in modules that can be extended. WinCon uses Windows CE as the operating system. The choice of this programmable controller was influenced by the intention to use both REX control system software and Matlab/Simulink support (Vrána, Šulc, 2007) in developing the control algorithms. 4.1 The REX control system
Fig. 1. Optimal operating range in dependence on the fuel - air ratio
The tools for developing and monitoring the RexWinLab-8000 algorithm are the RexDraw and RexView programs. RexDraw is designed for algorithm development, organization and compilation, while RexView is designed for compiled configuration upload and for station activity monitoring. It can provide information about the activity of
Fig. 3. REX block set
4.2 The REX block set and RexLib library The RexLib library contains all blocks that are needed to develop the control algorithm and which are also contained in the RexDraw (Fig. 3) program. RexLib library is designated for use with Simulink (Fig. 4). The algorithm configuration blocks are also included, so the whole algorithm can be simulated even in situations when the algorithm is divided into several individual block schemes. The possibility to simulate all of the particular algorithms separately is also available. All blocks of RexLib and the Rex block set are described in Rex Controls (2007). When the algorithm designated for use outside Simulink is developed in Simulink, it is necessary to use only the blocks included in RexLib library. Other blocks can also be used, but only for simulation purposes. They cannot be a part of the algorithm that is developed. The extension of the Rex control system can be used, which enables these blocks to be left in the scheme if their name has been selected to contain the word simulation. Then it is not necessary to delete these blocks before the final compilation of the algorithm. 4.3 User defined functions Simulink itself contains the user defined function blocks MATLAB function and S-Function. RexLib library contains the RexLang block. The language of this block is derived from ANSI C language, but it has some restrictions and, of course, some extensions that compensate the restrictions. The advantage of the RexLang block in comparison to the MATLAB function block is the simpler initial statement definition, which facilitates algorithm portability into another device. The advantage in comparison to the block S-Function is that internal Simulink states are not used, so it is not necessary to take into account Simulink restrictions. 4.4 Communication block
Fig. 4. RexLib library as a part of the Simulink block library the station and about the activity of individual algorithms. The parameters of the algorithm can be changed in the RexView program. The supporting part of the software is the function block set library RexLib, which allows us to use the Simulink extension of the Matlab program for algorithm development. The advantage of developing the algorithm in Simulink is that the algorithm can be simulated. Simulation is not possible when using RexDraw or RexView. On the other hand, compilation is not possible in Simulink, so it is necessary to use RexDraw.
The Rex control system contains the RDC communication block. This can be used as a communication tool among devices in the distributed control loop, or as a communication tool with a superior device. Of course, this block can also be used to connect more Simulink schemes. The communication is based on the UDP/IP internet protocol. One RDC block can transfer at most 16 variables into another device. If it is required to transfer more than 16 variables, more RDC blocks must be used. 5
EMISSION INCREASE AFTER GRATING
The following figures display selected parts of a data record from the experiment, in which changes in the composition of the flue gases emitted at two levels of the boiler power output were tested. The courses depicted in Fig. 5 for all three monitored variables are quite acceptably balanced (in this case the tested boiler was adjusted correctly for the nominal operating regime, as required by the producer).
However, after a power cut down to 70 percent of the nominal value, the originally unimportant peaks visible in Fig. 5 at the courses of NOx and O2 change in size rapidly some time after grating (Fig. 6). The power was lowered by reducing the fuel feeding rate. The reduction in performance is manifested by a gradual decrease in the temperature of the flue gases.
grating also appear in carbon monoxide (not shown in the figure). This means the peaks of the gas components that have to be measured with gas analysers can be detected on the basis of information obtainable from a lambda probe. It is also seen that the size of the peak changes gradually grows, and thus long term operation of the boiler in a lowered power regime without any control action is not supportable. 6
'ominal power 300
200 Flue Gases Temperature [°C] λ [%] 100 NOx [ppm] Grating 0 2
3
4 5 6 7 8 9 10 11 Minutes elapsed from the start of experiment
12
13
Fig. 5. Selected part of the process data recorded in nominal power output Automatic grating, performed every ten minutes, is at the present time generated by the electronic control system delivered by the producer together with the boilers. The time instants of grating are marked in both figures. Shortly after grating, the peaks appear. Data on oxygen excess (lambda value) can be obtained without expensive gas analysers. The other components in the flue gases are measured by means of analysers, i.e. with a certain delay, which is a part of the delay in peak occurrence. Nitrogen oxides react to grating in a very similar way to the oxygen excess. However, in this case there is a distinct drop instead of a peak increase. Strong peaks as a reaction to 70 % of nominal power 300
λ [%]
MODELLING THE BOILER
Dynamic mathematical models in the form of equations derived from classical balance procedures are very difficult to obtain, due to the extreme complexity of a description of the exact combustion process. Instead of this approach, we have started to develop a block mass-heat-temperature model, in which relations that are mathematically difficult to define are simplified and expressed via experimentally obtained dependencies. However, for the design of control precautions against an increase in harmful emissions after grading, such types of model are too detailed, and a sufficient amount of data must be available for model verification. It seems much more reasonable to constitute this design on the behaviour derived from some experiments testing the influence of the input variables on outputs such as CO, CO2, NOx emissions and the flow rate of the flue gases. We therefore decided to develop the control algorithm on fuzzy logic principles, and we decided to use models of a similar kind for testing such algorithms before implementation. It is expected that they will show which of the various combinations of inputs used in modelling can simulate desired values reliably and in real time. 6.1 Model type selection We decided to use fuzzy modelling to test various combinations of control actions. Special sensors were installed on our testing boiler for measuring the concentration of CO, CO2, NOx emissions and for measuring the velocity of the flue gases. These sensors are not a part of the standard equipment of the boiler. Then we made some test measurements for various heat rate levels, and we obtained a set of process measurements, which we used for tuning the fuzzy models of the controlled variables. Emissions of CO, CO2, NOx, flue gas temperature and flue gas velocity were chosen as the key values for boiler operation in terms of environmental and economic considerations. For these output values we designed simple fuzzy models to test the optimal control possibilities.
200
6.2 Creating a fuzzy model 100 NOx [ppm] Grating 0 14
16
18
20 22 24 27 29 31 33 35 Minutes elapsed from the start of experiment
37
39
Fig. 6. Selected part of the process data recorded at 70 % of nominal power output
The Matlab Fuzzy Toolbox and the ANFIS command were used for creating fuzzy models. ANFIS stands for "AdaptiveNetwork-based Fuzzy Inference Systems". ANFIS applies two techniques in updating the parameters. For premise parameters that define membership functions, ANFIS employs gradient descent to fine-tune them. For consequent parameters that define the coefficients of each output equation, ANFIS uses the least-squares method to identify them. This approach is called the hybrid learning method,
6.3 Fuzzy model testing Fig. 7 shows a comparison of the measured CO2 emissions in the biomass boiler and the CO2 emissions simulated by a simple fuzzy model. It can be seen that the fuzzy model in the range of CO2 emission for which the fuzzy rules are designed is quite well capable of simulating the emissions (Brandejský et al., 1999). The measured NOx and O2 concentration values, the boiler heat rate and the period of fuel feeding were used as the input values for designing the fuzzy model. The problem is on what principles to establish the function of the algorithms for reducing the peaks occurring in the harmful components in the flue gases. It has been confirmed that there is a strong correlation with measurement of oxygen excess. Using a relatively cheap lambda sensor, this can be exploited in reducing those flue gas components that would otherwise be difficult and expensive to determine. 6.4
neural based boiler model was therefore also tested. The neural model consists of a double layer neural network with a multilayer perceptron architecture. A comparison between measured emissions of CO and emissions simulated by means of this model is shown in Fig. 8. This neural model can be used in a search for control algorithms that reduce the dynamic impacts on the flue gas composition caused by grate sweeping, because preliminary tests with the model have confirmed reduced emissions (Fig. 9). Fig. 9 shows the reduction even when, instead of the unmeasured combustion, the air flow rate concentration of O2 is changed and the impact on CO emissions in the flue gases is observed. Simulation results reflecting a decrease in O2 concentration by 20 % are shown in Fig. 9. The peaks of CO emissions after reducing the O2 concentration during grate sweeping are evidently smaller. By controlling the combustion air inlet flow rate into the boiler, the total amount of O2 in the combustion chamber can be changed. Simulation experiments performed with a neural model of the biomass boiler set-up proved that manipulating the air delivery can reduce CO emissions. This was confirmed by testing the influence of control actions temporarily lowering the air delivery on a real boiler. In this way, emissions can be decreased and boiler efficiency can also be improved.
0.6
simulated data measured data
0.5 0.4
CO [%]
since it combines gradient descent and the least-squares method. The GENFIS1 command from the Matlab Fuzzy Toolbox was used for designing of the fuzzy rules that generate the output values from the measured input values. It applies grid partitioning, and it generates rules by enumerating all possible combinations of the membership functions of all inputs; this leads to an exponential explosion even when the number of inputs is only moderately large. For example, for a fuzzy inference system with 10 inputs, each with two membership functions, grid partitioning leads to 1024 (=210) rules, which is prohibitively large for any practical learning methods. It was therefore necessary to choose only a limited number of inputs for designing fuzzy models for GENFIS1, in order not to run out of memory (Novák, 2000).
0.3
The number of fuzzy rules obtained for this fuzzy model is very high, and it is very computationally demanding for utilization in a real time control algorithm. The design of a
0.2 0.1
14 simulated data
13.5
12.5
CO2 [%]
13
0
measured data
10
20
30
40
50
60
70
Sample 90
Fig. 8. Comparison of measured data and data computed by a neural model 7
12 11.5 11 10.5 10
0
Sample 10
20
30
40
50
60
Fig. 7. Comparison of measured data and data computed by a fuzzy model
CIRCUIT COOLING HEATED WATER
During reconstruction of the laboratory where the tested boiler is located, and installing a more powerful boiler there, another task arose. It is very uneconomical to use drinking water for heating without recirculation. Colleagues from the Department of Fluid Dynamics and Power Engineering therefore decided to build a brand new closed loop cooling circuit with a unique design. It was shown that that the behaviour and the dynamic properties of such a cooler, and its influence on the properties of connected boilers, are not easy to describe. A simple simulation model using
ACKNOWLEDGMENTS simulated CO emissions with control
1
This research project has been supported by grant MSM6840770035 Development of Ecological Decentralized Power Plants.
original measured CO emissions
0.8
REFERENCES
0.6 0.4 0.2 0 300
320
340
360
380
400
420
Sample 460
Fig. 9. Test of the neural model, confirming its usability for developing a strategy for emission peak reduction Matlab/Simulink was developed and used to check the transient states in the operating conditions. Verifying and optimising this model will be a task to be solved together with the biomass boiler model. 8
PROPOSED CONTROL SOLUTIONS AND CONCLUSIONS
The occurrence of the peak after grating provides an easy solution – control actions can be carried out after the time instants of grating. Fixed pre-programmed control actions limit the universal use of such control algorithms which 0.7
0.5 Fan revolutions CO [ppm/200 ]
0.3
grating instants
λ [%]
0.1 0 60
65
70 75 80 85 90 95 Minutes elapsed from the start of experiment
Fig. 10. Test of emission peak reduction otherwise had proved sufficient effectiveness in simple tests (Fig. 10). Fuzzy logic controllers provide better recognition of all possible operating situations. Together with the same type of process modelling these controllers are first to be tested. The proposed instrumentation will allow a control to be performed in PLC at a higher level than the existing simple logic controller. It will not be difficult to change the period of grating, to change the revolutions of the fan, and to assess several quantities simultaneously for a control action.
Åstrom, K. J. and Hägglund, T. (2005). Advanced PID control. ISA - The Instrumentation, System, and Automation Society, Research Triangle Park, USA. Åström, K. J. and Hägglund, T. (1995). PID Controllers: Theory Design, and Tuning. 2nd edition, ISA, Research Triangle Park, NC, USA. Brandejský, T., Bíla, J., and Brož, K. (1999). Fuzzy qualitative modelling of distributed energy and heat supply complex. In Helena Szczerbicka (ed.), Proc. of 13th European Simulation Multiconference on Modelling and Simulation: A Tool for the
*Czech Technical University in Prague, Dept. Instrumentation & Control Engineering, Czech Republic (Tel: +420 2435 2531; e-mail: [email protected]; [email protected];[email protected]). **Czech Technical University in Prague, Dept. Fluid Dynamics and Power Engineering (e-mail: [email protected]) *** I&C Energo, Praha, Czech Republic, (e-mail: [email protected])} Abstract: The aim of this paper is to present various types of research on reducing emissions in the flue gases produced by low-power biomass boilers, especially when the boilers operate outside their nominal operating regime. This long-term investigation program is being carried out on a pilot boiler in the university laboratories. The program involves acquiring additional instrumentation for performing and evaluating present and future experiments, creating various types of models of the boiler for designing and testing the control algorithms using available data, and implementing and evaluating the algorithms in real operation. The pilot device enables us to experiment using non-standard instrumentation, but throughout our research it is necessary to strike a balance between ecological policy requirements and economic considerations. Keywords: biomass, boiler, flue gases, control, fuzzy model
1
INTRODUCTION
During the last decade, biomass has become the most widely used renewable energy source in the Czech Republic, mainly due to its availability and acceptable price. Biomass nowadays often replaces lignite coal and electricity in heating systems. There are various sources of biomass, e.g. from forestry or from agriculture. For local decentralized heat production, i.e. particularly for households, biomass is distributed in various forms of products, including wooden briquettes and pellets. Such fuels are often used by people who know very little about boiler operation. In addition, there are currently no valid emission limits for CO, NOx or TOC for small sources not exceeding 50 kW of power output. This situation often results in inefficient biomass combustion, with a high concentration of undesirable products in the flue gases, although biomass is considered as a “clean fuel”. (Kaltschmitt, Hartmann, 2001). However, ordinary biomass users cannot be required to check the correct operation of the boiler periodically. Because several thousand small boilers are working and there is no real valuable feedback from their operation, the situation requires a simple and cheap solution that can be introduced easily. Therefore there is a need for biomass boiler producers to promote advanced boilers that fulfil all requirements for clean, safe and user-friendly automated operation. However, due to the lack of emission limits for gaseous pollutants from small boilers, there is no impetus for producers to focus on this issue. This research project aims to offer producers a relatively cheap way to improve their boiler control units to deal with all these gaseous pollutants and efficiency issues. Our
research focuses on the control system, one of the most important elements in the successful operation of a biomass boiler. An obvious limitation on introducing advanced control systems using various sensors as the control input is the price, which must not be raised unacceptably. For this reason, small biomass boilers are equipped with simple control systems that are not fully able to control the whole combustion process actively. Such systems often control only the power output of the boiler by means of changes in fuel feeding. Due to process discontinuity, the operation of the boiler often shows sudden changes in pollutant emissions that are not handled in any way. Our aim is to find a solution for these situations, e.g. by real-time continuous operation monitoring, and to develop ways to prevent these undesirable changes in emissions. 2
PROBLEM OF EMISSION MINIMIZATION IN BIOMASS COMBUSTION DEVICES
Every combustion device produces not only a flue gas containing CO2 as a final product of oxidation of all carboncontaining fuels and water vapour, but also other components that can be dangerous (e.g. CO, NOx), or efficiencydecreasing components (e.g. CO, CxHy) as products of incomplete combustion. To decrease incomplete combustion, the process is carried out with some excess of combustion air, which is expressed by factor λ
λ=
Qa > 1 [ −] Qa min
(1)
where Qa is the flow rate of the actual combustion air and Qamin is the smallest necessary (stoichiometric) burning air.
The current value of the air factor λ in the running combustion process is acquired via oxygen concentration measurement in the flue gases at the end part of the boiler. This factor needs to be controlled to balance all influences on the combustion process, particularly to balance the CO and NOx concentrations with the combustion chamber temperature. Oxygen sensor discredibility means that that the sensor is not out of function, but that its properties have gradually changed to the extent that the sensor has started to provide biased data (Šulc, Klimánek, 2008). Fig. 1 depicts the optimal range of the air factor. If the air factor is between αmin and αmax, then the emissions of CO and NOx will not exceed the maximum acceptable level. However, the problem is that oxygen probes are vulnerable to faults. If the oxygen probe starts to provide biased information about the oxygen content in the flue gases, the emissions of CO and NOx may be excessive, and penalties can be incurred for undesirable environmental impacts. Thus it is essential to avoid any unrecognized increase in emissions, particularly of CO and NOx, by detecting possible oxygen sensor discredibility. 3
CONTROL TASKS
Most biomass boilers serving as heat sources in local heating systems are equipped with a very simple automatic function targeting automatic operation, i.e. maintaining the temperature of the heating water, automatic ignition, fuel supply, grating, etc. Until now, little attention has been paid to control, and this has had a direct impact on the environment. Unlike coal-fired boilers, biomass boilers with low power are not subject to strict emission regulations,. Producers therefore hesitate to introduce expensive control precautions that are necessary only for environmental reasons. However, oxygen probes have recently come into use to increase the efficiency of the boiler by finding the optimum fuel-air ratio. This brings a new challenge in control design – it is reasonable to equip the controller with additional features
Fig. 2. Functional scheme of the biomass boiler set-up of autotuning and detection of possible sensor discredibility (Klimánek, Šulc, 2008). Both functions can be carried by software added to the control algorithm. This means only a minor increase in price and a major reduction in harmful emissions. Fig. 2 schematically shows the arrangement of the boiler used in the experiments discussed here. This is connected with another control problem that we have detected when measuring emissions – the rapid increase of emissions in a time interval after grating. This phenomenon, together with possible control solutions to it, based on additional instrumentation (Åstrom, Hägglund, 1995, 2005), is a major point of interest for further research. 4
SPECIALIZED INSTRUMENTATION FOR CONTROL EXPERIMENTS
Instead of the pre-programmed fixed logic standardly delivered with the boiler, the new RexWinLab-8000 control system has been proposed. RexWinLab-8000 is a station based on the WinCon Programmable Automation Controller, which contains five plug-in modules that can be extended. WinCon uses Windows CE as the operating system. The choice of this programmable controller was influenced by the intention to use both REX control system software and Matlab/Simulink support (Vrána, Šulc, 2007) in developing the control algorithms. 4.1 The REX control system
Fig. 1. Optimal operating range in dependence on the fuel - air ratio
The tools for developing and monitoring the RexWinLab-8000 algorithm are the RexDraw and RexView programs. RexDraw is designed for algorithm development, organization and compilation, while RexView is designed for compiled configuration upload and for station activity monitoring. It can provide information about the activity of
Fig. 3. REX block set
4.2 The REX block set and RexLib library The RexLib library contains all blocks that are needed to develop the control algorithm and which are also contained in the RexDraw (Fig. 3) program. RexLib library is designated for use with Simulink (Fig. 4). The algorithm configuration blocks are also included, so the whole algorithm can be simulated even in situations when the algorithm is divided into several individual block schemes. The possibility to simulate all of the particular algorithms separately is also available. All blocks of RexLib and the Rex block set are described in Rex Controls (2007). When the algorithm designated for use outside Simulink is developed in Simulink, it is necessary to use only the blocks included in RexLib library. Other blocks can also be used, but only for simulation purposes. They cannot be a part of the algorithm that is developed. The extension of the Rex control system can be used, which enables these blocks to be left in the scheme if their name has been selected to contain the word simulation. Then it is not necessary to delete these blocks before the final compilation of the algorithm. 4.3 User defined functions Simulink itself contains the user defined function blocks MATLAB function and S-Function. RexLib library contains the RexLang block. The language of this block is derived from ANSI C language, but it has some restrictions and, of course, some extensions that compensate the restrictions. The advantage of the RexLang block in comparison to the MATLAB function block is the simpler initial statement definition, which facilitates algorithm portability into another device. The advantage in comparison to the block S-Function is that internal Simulink states are not used, so it is not necessary to take into account Simulink restrictions. 4.4 Communication block
Fig. 4. RexLib library as a part of the Simulink block library the station and about the activity of individual algorithms. The parameters of the algorithm can be changed in the RexView program. The supporting part of the software is the function block set library RexLib, which allows us to use the Simulink extension of the Matlab program for algorithm development. The advantage of developing the algorithm in Simulink is that the algorithm can be simulated. Simulation is not possible when using RexDraw or RexView. On the other hand, compilation is not possible in Simulink, so it is necessary to use RexDraw.
The Rex control system contains the RDC communication block. This can be used as a communication tool among devices in the distributed control loop, or as a communication tool with a superior device. Of course, this block can also be used to connect more Simulink schemes. The communication is based on the UDP/IP internet protocol. One RDC block can transfer at most 16 variables into another device. If it is required to transfer more than 16 variables, more RDC blocks must be used. 5
EMISSION INCREASE AFTER GRATING
The following figures display selected parts of a data record from the experiment, in which changes in the composition of the flue gases emitted at two levels of the boiler power output were tested. The courses depicted in Fig. 5 for all three monitored variables are quite acceptably balanced (in this case the tested boiler was adjusted correctly for the nominal operating regime, as required by the producer).
However, after a power cut down to 70 percent of the nominal value, the originally unimportant peaks visible in Fig. 5 at the courses of NOx and O2 change in size rapidly some time after grating (Fig. 6). The power was lowered by reducing the fuel feeding rate. The reduction in performance is manifested by a gradual decrease in the temperature of the flue gases.
grating also appear in carbon monoxide (not shown in the figure). This means the peaks of the gas components that have to be measured with gas analysers can be detected on the basis of information obtainable from a lambda probe. It is also seen that the size of the peak changes gradually grows, and thus long term operation of the boiler in a lowered power regime without any control action is not supportable. 6
'ominal power 300
200 Flue Gases Temperature [°C] λ [%] 100 NOx [ppm] Grating 0 2
3
4 5 6 7 8 9 10 11 Minutes elapsed from the start of experiment
12
13
Fig. 5. Selected part of the process data recorded in nominal power output Automatic grating, performed every ten minutes, is at the present time generated by the electronic control system delivered by the producer together with the boilers. The time instants of grating are marked in both figures. Shortly after grating, the peaks appear. Data on oxygen excess (lambda value) can be obtained without expensive gas analysers. The other components in the flue gases are measured by means of analysers, i.e. with a certain delay, which is a part of the delay in peak occurrence. Nitrogen oxides react to grating in a very similar way to the oxygen excess. However, in this case there is a distinct drop instead of a peak increase. Strong peaks as a reaction to 70 % of nominal power 300
λ [%]
MODELLING THE BOILER
Dynamic mathematical models in the form of equations derived from classical balance procedures are very difficult to obtain, due to the extreme complexity of a description of the exact combustion process. Instead of this approach, we have started to develop a block mass-heat-temperature model, in which relations that are mathematically difficult to define are simplified and expressed via experimentally obtained dependencies. However, for the design of control precautions against an increase in harmful emissions after grading, such types of model are too detailed, and a sufficient amount of data must be available for model verification. It seems much more reasonable to constitute this design on the behaviour derived from some experiments testing the influence of the input variables on outputs such as CO, CO2, NOx emissions and the flow rate of the flue gases. We therefore decided to develop the control algorithm on fuzzy logic principles, and we decided to use models of a similar kind for testing such algorithms before implementation. It is expected that they will show which of the various combinations of inputs used in modelling can simulate desired values reliably and in real time. 6.1 Model type selection We decided to use fuzzy modelling to test various combinations of control actions. Special sensors were installed on our testing boiler for measuring the concentration of CO, CO2, NOx emissions and for measuring the velocity of the flue gases. These sensors are not a part of the standard equipment of the boiler. Then we made some test measurements for various heat rate levels, and we obtained a set of process measurements, which we used for tuning the fuzzy models of the controlled variables. Emissions of CO, CO2, NOx, flue gas temperature and flue gas velocity were chosen as the key values for boiler operation in terms of environmental and economic considerations. For these output values we designed simple fuzzy models to test the optimal control possibilities.
200
6.2 Creating a fuzzy model 100 NOx [ppm] Grating 0 14
16
18
20 22 24 27 29 31 33 35 Minutes elapsed from the start of experiment
37
39
Fig. 6. Selected part of the process data recorded at 70 % of nominal power output
The Matlab Fuzzy Toolbox and the ANFIS command were used for creating fuzzy models. ANFIS stands for "AdaptiveNetwork-based Fuzzy Inference Systems". ANFIS applies two techniques in updating the parameters. For premise parameters that define membership functions, ANFIS employs gradient descent to fine-tune them. For consequent parameters that define the coefficients of each output equation, ANFIS uses the least-squares method to identify them. This approach is called the hybrid learning method,
6.3 Fuzzy model testing Fig. 7 shows a comparison of the measured CO2 emissions in the biomass boiler and the CO2 emissions simulated by a simple fuzzy model. It can be seen that the fuzzy model in the range of CO2 emission for which the fuzzy rules are designed is quite well capable of simulating the emissions (Brandejský et al., 1999). The measured NOx and O2 concentration values, the boiler heat rate and the period of fuel feeding were used as the input values for designing the fuzzy model. The problem is on what principles to establish the function of the algorithms for reducing the peaks occurring in the harmful components in the flue gases. It has been confirmed that there is a strong correlation with measurement of oxygen excess. Using a relatively cheap lambda sensor, this can be exploited in reducing those flue gas components that would otherwise be difficult and expensive to determine. 6.4
neural based boiler model was therefore also tested. The neural model consists of a double layer neural network with a multilayer perceptron architecture. A comparison between measured emissions of CO and emissions simulated by means of this model is shown in Fig. 8. This neural model can be used in a search for control algorithms that reduce the dynamic impacts on the flue gas composition caused by grate sweeping, because preliminary tests with the model have confirmed reduced emissions (Fig. 9). Fig. 9 shows the reduction even when, instead of the unmeasured combustion, the air flow rate concentration of O2 is changed and the impact on CO emissions in the flue gases is observed. Simulation results reflecting a decrease in O2 concentration by 20 % are shown in Fig. 9. The peaks of CO emissions after reducing the O2 concentration during grate sweeping are evidently smaller. By controlling the combustion air inlet flow rate into the boiler, the total amount of O2 in the combustion chamber can be changed. Simulation experiments performed with a neural model of the biomass boiler set-up proved that manipulating the air delivery can reduce CO emissions. This was confirmed by testing the influence of control actions temporarily lowering the air delivery on a real boiler. In this way, emissions can be decreased and boiler efficiency can also be improved.
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since it combines gradient descent and the least-squares method. The GENFIS1 command from the Matlab Fuzzy Toolbox was used for designing of the fuzzy rules that generate the output values from the measured input values. It applies grid partitioning, and it generates rules by enumerating all possible combinations of the membership functions of all inputs; this leads to an exponential explosion even when the number of inputs is only moderately large. For example, for a fuzzy inference system with 10 inputs, each with two membership functions, grid partitioning leads to 1024 (=210) rules, which is prohibitively large for any practical learning methods. It was therefore necessary to choose only a limited number of inputs for designing fuzzy models for GENFIS1, in order not to run out of memory (Novák, 2000).
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The number of fuzzy rules obtained for this fuzzy model is very high, and it is very computationally demanding for utilization in a real time control algorithm. The design of a
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Fig. 7. Comparison of measured data and data computed by a fuzzy model
CIRCUIT COOLING HEATED WATER
During reconstruction of the laboratory where the tested boiler is located, and installing a more powerful boiler there, another task arose. It is very uneconomical to use drinking water for heating without recirculation. Colleagues from the Department of Fluid Dynamics and Power Engineering therefore decided to build a brand new closed loop cooling circuit with a unique design. It was shown that that the behaviour and the dynamic properties of such a cooler, and its influence on the properties of connected boilers, are not easy to describe. A simple simulation model using
ACKNOWLEDGMENTS simulated CO emissions with control
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This research project has been supported by grant MSM6840770035 Development of Ecological Decentralized Power Plants.
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REFERENCES
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Fig. 9. Test of the neural model, confirming its usability for developing a strategy for emission peak reduction Matlab/Simulink was developed and used to check the transient states in the operating conditions. Verifying and optimising this model will be a task to be solved together with the biomass boiler model. 8
PROPOSED CONTROL SOLUTIONS AND CONCLUSIONS
The occurrence of the peak after grating provides an easy solution – control actions can be carried out after the time instants of grating. Fixed pre-programmed control actions limit the universal use of such control algorithms which 0.7
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Fig. 10. Test of emission peak reduction otherwise had proved sufficient effectiveness in simple tests (Fig. 10). Fuzzy logic controllers provide better recognition of all possible operating situations. Together with the same type of process modelling these controllers are first to be tested. The proposed instrumentation will allow a control to be performed in PLC at a higher level than the existing simple logic controller. It will not be difficult to change the period of grating, to change the revolutions of the fan, and to assess several quantities simultaneously for a control action.
Åstrom, K. J. and Hägglund, T. (2005). Advanced PID control. ISA - The Instrumentation, System, and Automation Society, Research Triangle Park, USA. Åström, K. J. and Hägglund, T. (1995). PID Controllers: Theory Design, and Tuning. 2nd edition, ISA, Research Triangle Park, NC, USA. Brandejský, T., Bíla, J., and Brož, K. (1999). Fuzzy qualitative modelling of distributed energy and heat supply complex. In Helena Szczerbicka (ed.), Proc. of 13th European Simulation Multiconference on Modelling and Simulation: A Tool for the