Enhanced PID Controllers in Combustion Control

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1 Enhanced PID Controllers in Combustion Control Bohumil Šulc, Cyril Oswald Abstract Control of combustion is an important task mainly with respect to energy efficiency and ecological impacts. This problem has become significant with the expansion of biomass-fired boilers, even those with low power. The combustion process consists of phenomena that we have little information about for an exact mathematical description. The equations of the chemical reactions are known, but in reality the combustion of biomass is much more complicated and more variable than a mathematical model can express. It is therefore not simple to design a controller that is able to control the combustion process effectively over the whole range. Small-scale biomass-fired boilers are usually equipped with simple two-state (on/off) controllers. Attempts in many laboratories to develop a model of the combustion process suitable for controller setting and parameter adaption have not been satisfactory and generally valid until now. Thus, advanced model-based control strategies are not usable, while the use of a model-free controller is not excluded if well-designed tuning rules can be offered. For the control of combustion process in boilers preparing hot water for heating systems, the problem of controller parameter tuning is not so urgent as the economy and the environment-friendliness of its operation. Due to the great heat inertia, the deviations of the temperature of heated water as the controlled variable do not vary much from the desired value, while the boiler must often be operated under conditions that are far from those assumed in the nominal factory setting. A controller offering not only the main control function but also the ability to renew optimal combustion is presented here. S Keywords PID controller, tuning, combustion, controller agents I. INTRODUCTION INCE digital versions of the PID controller have become standard equipment for control loops, the tendency to equip controllers with some additional features has increased strongly. Support for controller parameter tuning, preferably autotuning, is one of the most desired, most developed, and most widely-offered features. However, there are some other features which give remarkable additional efficacy to standard controllers, and it is worthwhile to engage in developing these control function enhancements. The first contribution in this Manuscript received November 15, This work was supported by Czech Ministry of Education, Youth and Sport grant no. MSM The Development of Environment-friendly Decentralized Power Engineering. Bohumil V. Šulc is with the Czech Technical University in Prague, Dept. of Instrumentation and Control Engineering, bohumil.sulc@fs.cvut.cz). C. Oswald., is a PhD student at the Czech Technical University in Prague, Dept. of Instrumentation and Control Engineering, cyril.oswald@fs.cvut.cz field was controlled variable discredibility testing [3]. Our contribution to the enhancement of standard PID controllers in the framework of research on environment-friendly decentralized power engineering has focused on reducing harmful gases. It was aimed at small-scale biomass boilers. The progress in small-scale biomass boilers for heating which has taken place in recent years concerns not only the number of boilers put into operation but also the conditions under which they are run. The technical parameters achievable in small-scale boilers are not bad, especially if they are operated in the factory setting with an experienced operator. Boilers combusting wooden pellets nowadays have efficiency up to 95 % [1]. Until now, no restrictions on emissions have limited their operation. However, in the near future the advantages of biomass as a renewable energy source will have to be supported by some other features that will allow users without a technical background to use the boilers with a level of comfort similar to the accustomed case of using gas boilers. This cannot be achieved without changes in control. Until now ON-OFF temperature control of the heating water working with several preset power outputs corresponding to the selected constant fuel delivery has been used. However, this does not allow boiler modulation, i.e. a quick reaction to load changes, and it also does not take the emitted gaseous pollutants into account. This means that there are two strong motivations for developing and applying more advanced control. For economic reasons, the boiler be as efficient as possible. Then, there is strong tendency to establish tightened emission limits, even for small-scale boilers. Of course the costs of necessary instrumentation for developing such advanced control must be taken into account, because the price of the boiler must be competitive with the price of similar products. It is therefore important to design several technical solutions and to test them on a pilot boiler with rich instrumentation, so that an objective comparison can be made before starting production. One of the investigated concepts is reported here. The second controller enhancement concerns automated parameter tuning. An approach is proposed which attempts to meet most of the requirements of industrial practice. The proposed method is based on an experimentally performed evaluation of small amplitude excited frequency responses with the aim of achieving recommended values of one or more control quality indicators known from the course of the ISBN:

2 Nyquist plot, e.g. [2], [4], [5]. As regards the experimental way of obtaining the indicators, they can be evaluated in control loops involving nonlinearities even in the controller. In this sense, the method has a philosophy similar to that of the popular Ziegler and Nichols method, but no interruption of the control process is necessary and the amplitude of the excited oscillation is fully selectable. The main advantages of the method presented in [2] are: no use of any mathematical model, usability as an addition to an existing controller purely via the software, and no necessity to break the control process during controller retuning. The third group of enhancements suitable for standard PID controllers consists of added program tools, which make it possible not only to optimize the process of removing the control error but also to reflect the conditions under which the operating point is held. The focus of attention in this paper is on the chances of the concept for controlling combustion devices represented by small biomass heating boilers, a pilot sample of which was made available to us. II. CONCEPT OF ECO CONTROL The abbreviation ECO is used to express two important considerations in the control of combustion devices economy and ecology. These two considerations are not that the direct focus of control theory. In theory, a control circuit is considered to be operating optimally if, simply said, during the control process the controlled variable differs from the desired value for the shortest possible time, with the smallest deviation. Control theory is aimed at achieving an optimal course of responses of one or more variables in the control circuit, and direct attention is seldom paid to considerations such as energy consumption or ecological impacts during the control process. A good example of ECO control is offered by combustion devices, represented by boilers that prepare water for heating purposes. The temperature of the water delivered in a heating system is controlled by a controller manipulating the fuel supply. The control loop of this temperature control is designed in such a way that all requirements concerning the controlled temperature of the water are satisfactorily fulfilled. However, the combustion process may run under nonecological and uneconomical conditions, due to improper values of the combustion ratio. It is not easy to find an optimal setting of the combustion ratio by a conventional controller, because its value changes in dependence on fuel and load. The desired value cannot be set for the controller in advance. It must adapt to the changing firing conditions. This can be achieved by an algorithm cooperating with the standard PID control algorithm inside the controller. The operating strategy of such an enhanced PID can be described as follows: keep the controlled variable, whose desired values are known, oat these values by the standard PID control algorithm, and during the steady states indicated by unchanging values of the manipulated variables perform an on-line search to set another variable whose optimum is indicated indirectly by means of the extreme in a steady state dependence. The most common interest concerns the output power, the efficiency of operation, the concentration of a specific (emission) component, etc. Interpreting this general task in notions of optimal boiler operation, the control task can be formulated as follows: keep the temperature of the heating water at the desired value by manipulating the fuel supply in such a way that manipulating the combustion air carried out consequently will lead to a minimum in the necessary fuel delivery, when it is still possible to keep the temperature at the desired value. Then, it can be expected that maximum efficiency in boiler operation has been achieved. It is even possible to define a compromise between boiler efficiency and the proportion of harmful components in the flue gases by means of an enhanced weighted criterion. The solution of this control task, which combines extremal and standard control, can be based on several approaches. We choose the concept of agents, because to the best of our knowledge it has not been used yet for control tasks of this type, and we have specialized instrumentation for developing and testing this approach on a real device. The results presented in the paper so far are based on a simulation using Matlab/Simulink. However, the available small-scale biomass fired pilot boiler is equipped with instrumentation allowing both necessary identification experiments and real implementation. III. SPECIALIZED INSTRUMENTATION FOR CONTROL EXPERIMENTS Instead of the pre-programmed fixed logic standardly delivered with the boiler, the new RexWinLab-8000 control system was 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 [6] in developing the control algorithms. The possibility to use Matlab/Simulink has the advantage that all algorithm verification can be carried out on the Simulink simulation model, and verified parts of the developed algorithm can easily be transferred into WinCon using REX software. IV. MULTI-AGENT CONCEPT APPLIED TO THE ENHANCED CONTROLLER Under the term agent based PID controller we will understand a controller whose basic (main) PID algorithm has been enhanced by program modules using necessary (additional) inputs and outputs. Thus, in addition to optimal responses of the controlled variable ensured by optimal setting of the controller parameters in the main PID algorithm it will be possible, by means of these modules, to achieve another optimum according to some additionally defined criteria. These criteria usually evaluate the achieved steady states of ISBN:

3 some quantities or quality indicators that we assume to be important for the function of the controlled object, and which cannot be ensured by the standard controller. A. Motivation for using the multi-agent concept in the controller design The algorithm performing the additional optimizing function is designed as an additional feature of the main PID algorithm. It can use different inputs, and mainly, it sets outputs different from the manipulated variable generated by the main PID controller. However, mutual cooperation of the main PID control algorithm part and the proposed optimization algorithm is unavoidable. Therefore, we can regard the proposed enhanced controller as a system consisting of several mutually cooperating modules called agents. The controller can therefore be regarded as a reflex intelligent agent, all of whose interactions are limited to the mutual interlocking of the activity [7]. If the proposed optimization algorithm is aimed at optimizing more criteria, then a more sophisticated cooperation between them is required. Some communication between the proposed optimization algorithm and the main controller algorithm is needed. The whole enhanced controller can be designed as a multi-agent system, where the two algorithms are designed as standalone reflex or learning intelligent agents communicating with each other by messages. Moreover, it is possible to use multiple proposed optimization algorithms operating with different optimization tasks side by side, assuming that the controller has been designed as a multi-agent system. B. Controller designed with the optimization algorithm as the simple reflex agent The controller designed as the simple reflex agent presented above consists of the following program parts performing a main control function, an optimization function and a controller managing function. The main controller algorithm (i.e. the PID algorithm) and the optimization algorithm are components of the main controller function and the optimization function, respectively. The operation of both functions is managed by the controller managing function. It processes all controller inputs and distributes them to the managed functions. Moreover, it suppresses the operation of the individual managed functions on the basis of defined rules. It requires information about the state of the algorithm to be the output of one of the managed functions. It may be useful for the optimization algorithm to use some internal variables of the controller in the algorithm. Because the proposed concept does not expect any agent modules, communication with this internal variable must be considered as a virtual additional controller output which is fed back to the controller. It is realized by the controller manage function, too. This proposed design is depicted in Fig. 1. C. Controller designed with the optimization algorithm as a multi-agent system When more sophisticated cooperation between the main controller algorithm and the optimization algorithm is needed, or the operation of several optimization algorithms side by side is wanted, the simple intelligent agent concept is not enough. For this reason, the concept of the controller as a multi-agent system is proposed above. The proposed design of the controller enhanced by the optimization algorithm is as follows: The basic structure is similar to the concept of the controller as the reflex intelligent agent above. However, all controller functions are regarded as standalone agents except for the controller manage function, which is an interface between sensors, actuators and agents. All agents are able to communicate with each other. When the multi-agent system consists of two agents, direct communication between agents is probably enough. If it consists of several agents, it seems that a special communication agent is needed. When the communication agent is used, all messages are sent to it and the communication agent delivers it to a recipient. All messages must therefore contain a target agent identification. This proposed controller concept is depicted in Fig. 2. When the proposed multi-agent concept is used, the agent function is able to inform the others about its internal state. Fig. 1 Information flows in a block diagram of the enhanced controller, using the simple agent concept Fig. 2 Information flows in a block diagram of the controller using multiagent concept ISBN:

4 Moreover, it is not necessary for all agents to be present in the system at the beginning of the process; the agents can be started or terminated during the control process. For simulation tests of the controller concept introduced here realization of it in MATLAB environment is proposed. All agents are standalone entities. The main difference between them is in their main algorithm, while the other parts are the same (e.g. a communication function). Due to these factors, object-oriented programming can be used advantageously. The MATLAB object-oriented programming capabilities introduced in version R2008a are used. V. MODEL OF THE BOILER FOR ALGORITHM TESTING Because the aim of the work presented here is to test the feasibility of the proposed agent concept for performing the additional optimizing function, we used only a very approximative mathematical model of the combustion in a small biomass boiler. The model (Fig. 4) consists of two first-order transfer functions. One represents the relation between the boiler feed rate and the temperature in the combustion chamber. The second function represents the relation between the temperature in the combustion chamber and the temperature of the outgoing water. However, the parameters for these transfer functions were obtained by an analysis of the real boiler step responses. However inexact these models may be, we can be sure that at least the time relations are approximately correct. An example of an analysis of real boiler step responses is depicted in Fig. 5. The transfer functions used as a very rough approximation to the real behavior in the form of first order systems are: G FST cch 1450 ( s) =, G 1000s + 1 0,1 ( s) = 800s + 1 T cch T ow, where G (s) is the transfer function approximately FST cch describing the relation between the fuel supply and the temperature in the combustion chamber, and G (s) is T cch T ow Measured temperature of output water Response of e valueted transfer function Fig. 5 Example of the parameters obtained for the simulation model by an analysis of the real boiler step response the transfer function between the temperature in the combustion chamber and the temperature of the outgoing water. The simulation of the influence of the combustion ratio on combustion efficiency is included. An estimated idealized combustion efficiency function is used. In the other parts of the simulation model are also used highly idealized linear models. VI. SIMULATION OF FUEL CONSUMPTION OPTIMIZATION The basic idea of the optimizing algorithm was tested on a simplified linear model of the biomass boiler. The main controlled variable is the temperature of the outgoing water. It is controlled by the PI controller, whose manipulated variable is the setting of the fuel feed. The aim of the optimization algorithm is to minimize fuel consumption. This is achieved by finding the optimal combustion air. The optimization algorithm uses as inputs two pieces of ISBN:

5 information: one piece of information concerns detection of steady states in the main control loop based on recognition of the control error close to zero, while the second assesses the trends in the fuel feed evaluated as the difference between steady values in the previous and current steady state values of the manipulated variable. The output of the optimization algorithm is represented by the step changes in the revolutions of the blower. These changes are generated only after the control error value is close to zero. The activities described here were performed in the simulation model depicted in Fig. 3 by the PI controller agent interface and Optimization algorithm blocks. The other blocks, apart from those representing the PI controller and the plant model, have only an auxiliary function (e.g. defining Fig. 6 Lowering of the fuel supply during the simulation experiment as a consequence of optimization process, interrupted by the outgoing water flow change at s and continuing again after the desired temperature has again been reached Fig. 5 The changes in the combustion ratio during the simulation experiment in which the fuel consumption was optimized and the outgoing water flow was changed at s; the combustion ratio is influenced both by the fuel supply and by the air flow initial values), or they provide us with supplementary information about the optimization process run. The Combustion ratio block, for example, is not necessary for performing the control and optimization function, but, it helps to show in Fig. 5, that the stepwise changes of the combustion ratio caused by the step changes in the fan revolutions really do lead to an increase in the ratio up to a value of 1,8, which was introduced in the model as an optimum. At time s, when a change in the flow rate of the heating water was simulated, the PI controller had to react to a drop in the outgoing water temperature with an increase in the fuel feed. This caused an optimal combustion ratio violation, but the optimization algorithm later restored the combustion ratio to its optimal value 1,8. The results of the simulation experiment are depicted in Fig. 6 Fig. 8. They show the control responses of the variables in the standard control loop. Fig. 6 shows a decrement of the fuel feed (manipulated variable) to a minimum, which is interrupted by a disturbance at time s. In Fig. 7, changes in the revolutions of the fan are plotted, instead of the air flow rate, which is not measured. It is assumed that the airflow rate is proportional to the fan revolutions. Fig. 6 Increase of the fan revolutions up to an optimum changed by fuel consumption optimization after the outgoing water flow has been changed at s Fig. 7 Temperature of the outgoing water, controlled by the PI controller, during the simulation experiment in which the fuel consumption was optimized and influenced by a change in the outgoing water flow at s The results of the simulation experiments also demonstrate that, in reality, achieving an optimum would take quite a long time. This is because a heating boiler is dynamically a very slow device. The speed of the optimization depends on the dynamics of the controlled device and on the control quality requirements. CONCLUSION The first results of the simulation experiments show that the idea of using the agent concept in carrying out the intended optimization algorithm is feasible. However, the simulation was based on a model which is very simple. It does not contain ISBN:

6 any modeling of real phenomena such as nonlinearities, noise, complex interactions and many other influences that can be observed in a real plant. Therefore, we can anticipate that the basic optimization algorithm will have to be equipped with other precautions before it is applicable for real boilers. We have a pilot boiler available, and we assume that we will be able to create step by step more complex, but also more credible models, enabling us to prepare harder conditions in the enhanced controller tests. Successful application of controllers equipped with a combustion optimizing function can be very attractive both for producers and for users because it does not require any special knowledge and skill as far as setting is concerned, and at the same time economical and environment-friendly operation is achieved automatically and is guaranteed for different fuels. REFERENCES [1] M. Lackner, F. Winter, A. K. Agarwal, editors Handbook of Combustion, Wiley-VCH Verlag. Vol. 4., pp [2] B. Šulc, Assessment of Excited Oscillation in Controller Parameter Setting, WSEAS Transactioms on Systems and Control, 2006, vol. 1, no. 2, pp ISSN [3] B. Šulc, D. Klimánek, Evolutionary Algorithms in Supervision of Error-Free Control, Chapter 2 in Soft Computing Applications for Database Technologies. Techniques and Issues (K. Aubumani, R. Neduncheyhian, editors). Information Science Reference, Hershey.New York, ISBN [4] B. Šulc, S. Vrána, Some Observations on Development and Testing of a Simple Autotuning Algorithm for PID Controllers, WSEAS Transactions on Systems and Control, October 2009, Volume 4, Issue 10, pp ISSN [5] S. Vrána and B. Šulc, Control Quality Indicators in PID Controller Autotuning, in Proc. 4th International Conference on Cybernetics and Information Technologies, Systems and Applications: CITSA 2007 Jointly with the 5th International Conference on Computing, Communications and Control Technologies: CCCT 2007, vol. II. Orlando: IIIS International Institute of Informatics and Systemics, 2007, pp [6] B. Šulc, S. Vrána, J. Hrdlička, M. Lepold Control for ecological improvement of small biomass boilers,, IFAC Symposium Power Plants & Power Systems, 5 8 July, 2009, Tampere, Finland [7] V. Mařík, O. Štěpánková and J. Lažanský, Umělá intelligence. Prague: Academica, [8] G. Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Cambridge, MA: MIT Press, ISBN: