Industrial Boiler Modeling and Control Based on Adaptive Neuro Fuzzy Inference. System and Implementation in S7-400H PLC

Transcription

1 Research Paper International Journal of Review in Life Sciences ISSN: Volume 5 (2015), Issue 3 (Jul-Sep), Pages Industrial Boiler Modeling and Control Based on Adaptive Neuro Fuzzy Inference System and Implementation in S7-400H PLC Javad Shoorabeh 1, Feridoon Shabaninia 2, Iman Karimi 3 1 Shiraz University, Department of E-learning in Instrumentation and Automation Engineering, Iran 2 Shiraz University, Department of Electrical and Electronic Engineering 3 Bou Ali Sina Petrochemical Company, Department of Instrumentation, Mahshahr, Iran Abstract In this paper, a data driven method is employed to model a boiler. The method uses ANFIS as a fuzzy system which its parameters calculated by a neural network training algorithm. The main advantage of ANFIS is that it doesn t need complicated tuning of fuzzy system parameters and it just needs a good training dataset. Training dataset is obtained from a simulated mathematical model. However, this structure still needs PID and a good tuning of that because of the required good dataset. The proposed ANFIS shows good responses and mimic the boiler plant in acceptable manner. Furthermore, another ANFIS is designed to do a control job in boiler as a PID and it shows even better responses than a normal PID controller in some ranges. In addition, designed ANFIS is implemented in a S7 PLC to show the practical value of the method and it can be employed in industrial plant for further development and researches. Keywords: Boiler, Data driven modelling, ANFIS, PID, PLC Corresponding Author 1 ANN: Artificial Neural Network

2 Introduction Modelling and simulation has a main role in industry and process developments. Generally speaking, There are two methods of modelling. One of them is based on dynamic behaviour and relation of variables and states in physical equations and consequent differential equations from the real system (Mathematical Modelling). The other method is based on input-output data and experience or knowledge of the system (Empirical Modelling) (Boyatt et al., 2006). Mathematical modelling of chemical plant or power plant requires detailed calculation to obtain dynamics and thermodynamics equations of the system which is time consuming and tough task. In addition, in large scale plants with many complex and MIMO systems, modelling procedure becomes a nightmare. On the other side, empirical modelling is a straightforward way and just need a suitable and sufficient data set, however, the precision of this type is not as same as mathematical type and it has some deficiencies from this point of view. In addition, infrastructure of data gathering and communication is prerequisite for empirical modelling (Angell et al., 2008). In recent decays, rapid development of computers causes many industrial plants to use the benefits of computers and apply DCS and similar computerized and digital control system. As a consequence, data collection and storage is installed in many industrial plants. As a result, data driven modelling like ANN 1 modelling, Fuzzy modelling and other empirical modelling which are easier in comparison to mathematical modelling, attracts many interest. In addition, empirical modelling is easier to implement and to configure for further development and changes (Rusinowski and Stanek, 2007). Fuzzy systems and neural networks are applied in many wide range of application. A lot of publication in scientific journals and conferences shows the popularity of them. In spite of this, they have their own cons and complexity. For example, in fuzzy systems need enough experience from the system to adjust the fuzzy parameters and procedure of adjustment is time consuming. Neural network training process needs enough and suitable data set and need enough knowledge to manipulate the network. To improve this issue a combination system of Artificial Neural Networks and Fuzzy Inference System were introduced by Jang (1993) to cover the cons of these two popular structures. The proposed structure was ANFIS which stands for Adaptive NeuroFuzzy Inference System. ANFIS has a wide range of application from fault detection (Karimi and Salahshoor, 2012), to modelling (Neshat et al., 2011), and control. It has a simple structure and easy to configure. In this paper, ANFIS uses as an empirical model to mimic the behaviour of a boiler which has a significant role in power plants. Training dataset is build by a boiler model that simulated in MATLAB SIMULINK. Furthermore, another ANFIS is developed to control the boiler and becomes an alternative of a PID controller. Finally, the trained ANFIS which is used as a boiler model is implemented in a S7 PLC and the procedure is explained clearly. This part is done as a pre-process of implementation ANFIS in real world for future studies. Boiler The function of boiler is to deliver steam with a predefined pressure and temperature to a turbine or other process equipment. There are have several types of boilers such as water tube and fire tube (which is outdated), in this research water tube boilers steam-drum with saturated zone is the objective. The employed model proposed by Aström and Bell (1999) and is shown in Fig.1. In this figure, Q (Kj) is heat and causes boiling in the risers, q f (Kg/s) is feed water flow and supplied to the boiler, p(kpa) is drum pressure, q s (Kg/s) is steam flow and is taken from the drum to the super heaters, turbine or any other equipment which needs predefined quality of steam. Fig. 1. The proposed Drum model Although, in reality boiler system is much more complicated than shown in fig.1 and there is many down comer and riser tubes. But, the total behaviour is well captured by global mass and energy balances. Drum pressure is a very critical and important variable in boiler and any changes in the drum pressure cause the energy stored in steam and water is released or absorbed very rapidly. The rapid release of energy ensures that different parts

3 of the boiler change their temperature in the same way. As mentioned earlier, the operating zone in the following model is saturated zone and in this region relation of pressure and temperature is extremely nonlinear. To overcome this issue a lookup table was extracted from Engineering Toolbox web site to produce the pressure variable. The resulted pressure is processed in a set of differential equation to make the other variables. Much of the behaviour of the system is captured by global mass and energy balances.inputs to the system are the heat flow rate to the risers,q and the feed water mass flow rate, q f.furthermore, let the outputs of the system be drum pressure, p, and the steam mass flow rate, q s. This way of characterizing the system is convenient for modelling. Additional notation is needed, then, let V denotes volume, ρ denotes specific density, u specific internal energy, h specific enthalpy,t temperature and q s mass flow rate. The total mass of the metal tubes and the drum is m and the specific heat of the metal is Cp. Furthermore, let subscripts s, w, f and m refer to steam, water, feed water, and metal, respectively. Sometimes, for clarification, need a notation for the system components. For this purpose would use double subscripts where t denotes total system, d drum and r risers. With the above definition, the state space model is: dv e wt 11 dt dv e wt 21 dt + e 12 dp dt = q f q s + e 22 dp dt = Q + q fh f q s h s (1) which e 11, e 12, e 21 and e 22 are : e 11 = ρ w ρ s (2) ρ e 12 = V s + V ρ w st wt (3) e 21 = ρ w h w ρ s h s (4) ρ e 22 = V st h s + ρ h s s s ρ w h w V t + m t C p t s + V wt h w ρ w + (5) and the state variables are p and V. If only discussion interested in drum pressure, use a simplified model. If the drum level is controlled well the variations in the steam volume are small. Neglecting these variations extracts the following approximate model: e 1 dp dt = Q q f(h w h f ) q s h c (6) which ρ e 1= V s + ρ h s st sv + ρ h w st wv + m t s wt tc p V t (7) Detailed information of the above equation is presented in the Aström and Bell (1999) paper. The equations are implemented in SIMULINK and operating point and steady state values are calculated by operspec command in MATLAB. The result of simulated model is illustrated in fig.2, which pressure and temperature of the drum has been shown. As it can be seen, the changing direction and amplitude of temperature and pressure are similar that shows a validation of model DrumPressure(Kpa) Temperature(DegC) x 105 Heat(KJ) Fig.2. Drum pressure and Temperature response to input energy (Heat) The result shows that the model is suitable for making data set in our training procedure in ANFIS. ANFIS ANFIS is a Sugeno-Type fuzzy inference whose free parameters in membership functions (MFs) are adjusted via the learning methods being employed in Neural Networks. Sugeno FIS was first introduced in 1985 by Sugeno. This type of fuzzy inference is similar to the Mamdani method in many aspects. The main difference between Mamdani and Sugeno is that the output MFs is only linear or constant for Sugeno fuzzy inference. In fuzzy systems, adjusting the parameters of MFs is time consuming and need enough experience. In addition, for generating rules adequate previous knowledge of the system is needed. However, Jang (1993) proposed ANFIS to solve the problem. In fact, by performing training algorithms like BP

4 (Back Propagation) on input/output data set, the characteristics of data has been extracted and transformed to the rules and parameters of FIS for the best mapping from input to output. Elaborated information on ANFIS structure and its behaviours can be found in literature. In this paper, just addressed some parts of it. Each rule in ANFIS is like: If x is A1 and y is B1 Then f 1 =p 1 x+q 1 y+r 1 (9) If x is A2 and y is B2 Then f 2 =p 2 x+q 2 y+r 2 (10) Which x and y are inputs, A1, A2, B1and B2 are input MF and f is output that can be linear or constant. p, q and r are the consequent parameters. Since, would have chosen linear output with one input then f=px+r. A simple structure of ANFIS with two inputs and two membership functions on each input is illustrated in Fig.3. First layer is input layer and it has membership functions. In second layer, multiplying function of each membership function with each other is done to make the fire strength weight of each rule. Third layer just makes a normalization of weights in each rule. In fourth layer, the output of Takagi-Sugeno (f) is made with combination of consequent parameters (p, q and r) with inputs (x and y). Finally in fifth layer, final output of the network is made by adding the outputs of fourth layer. Fig.3. A simple structure of ANFIS There are two types of parameters in ANFIS, one is premise parameters and the other one is consequent parameters. Premise parameters are related to the membership function, as used triangle shape membership function, in this study they are a, b and c in the following equations.(8) 0, x a, x b f ( x; a, b, c) c x, c b 0, x a a x b b x c c x (8) Tuning of premise parameters affects the shape of membership function directly. Consequent parameters are related to fourth layer and make the output of Takagi-Sugeno and they are p, q and r. In this work, main input and output are heat, Q, and drum pressure, p. Then, ANFIS has just one input and the best and minimum number of membership function is 4. So the number of rules would be 4. The resulted structure of ANFIS is presented in fig.4. Fig.4. The structure of the proposed ANFIS Training data set is obtained from the simulated model which is presented in section 2. The ANFIS is trained with hybrid algorithm which contains back propagation and least square method. The obtained result is satisfactory and illustrated in fig.5. In the figure, red line is related to mathematical model which is presented in section 2 and the blue line is related to ANFIS. It is obvious that trained ANFIS follow the mathematical model in a good manner. Input of this incitation is presented in fig.6. The trained data was in a medium load range. The designed ANFIS shows good responses in medium load range even with other shape of input, i.e. the direction of increasing or decreasing. However, the ANFIS just shows good responses in medium load range and in other range which are not included in training data set, the quality of the response decreases gradually. In spite of this, the proposed ANFIS structure is also employed in control application (not modelling) and it shows good responses in comparison to PID. In next session, this comparison has been made.

5 DrumPressure(Kpa) Math Model ANFIS DrumPressure(Kpa) 950 Fig.5. Mathematical and ANFIS model output 2.4 x Heat (Kj) Training data is obtained from the PID with a definite coefficient. After that trained ANFIS works as single and independent controller in the boiler to control the drum pressure. The obtained result of two control structure is shown in fig.7.the operation point of the system is around 901 Kpa which is obtained from operspec command in MATLAB. The operation point line is illustrated with green line in Fig.7, in another word, it is the desired point which PID and ANFIS should keep the pressure of the boiler in this aria. ANFIS structure shows perfect responses in comparison to PID, particularly when there is a sudden change in input energy (Heat, Q,). However, if the input range is changed, responses will decrease in quality because of the training data set. Since our training data set is chosen in medium load, i.e. input heat is around 200 to 230 Mj, ANFIS will show good responses only in mentioned range. Medium load of heat or input energy is illustrated in fig.8. Nevertheless, this structure has a big deficiency, it still needs PID as a source of producing data, consequently, good tuning of PID is prerequisite of having a good data set for training ANFIS as a controller and complete remove of the PID controller is not good Compare between ANFIS(Blue)and PID(Red)... Kp=10.0 Ki=0.5 Kd=0.0 PID ANFIS 2 Fig.6. Related input of fig.5 ANFIS vs. PID One of the main controllers in industry which still has a lot of application and is employed in many plants is PID. It has a simple structure and understandable perfectly to many operators. However, it has some deficiencies which the worst one is the tuning. Each PID controller needs a comprehensive work for fine tuning and most of the time it has been made by try and error. In addition, by the time in each plant engineer should retune the PID parameters due to the variable nature of the system dynamic. Several methods have been proposed to overcome the issues of PID controllers. Many of them suggest an optimal way to calculate the PID coefficients like fuzzy logic, genetic algorithm and etc. The other methods are based on designing a different control structure like DMC, MPC, Fuzzy control and etc, explained by Fleming and Purshouse (2012). In this paper, ANFIS is employed as a controller instead of PID Fig.7. ANFIS and PID output response PLC implementation Programmable Logic Controller (PLC) is a device which is so popular and practical in different industries. It does a pre-programmed logic with respects to its digital and analogue inputs and makes appropriate outputs. For the first time, PLCs have been used as an alternative with relaycontactors circuits which control task and maintenance of the plants with those circuits was a

6 nightmare, specially in big plants with many variables. At this stage, the PLCs were proposed to overcome the issues of outdated and traditional relay-contactors circuits. Gradually, they have been employed in many factories, process plants, power plants and even in houses. 2.3 x Heat(Kj) discrete (event) control automotive, electronics, etc. Their primary goal was to replace the relay technology. Nowadays they have wide instruction libraries including function block for continues control (well-designed PID, lead-lag blocks, etc.) but there are missing libraries for intelligent control (fuzzy systems and neural networks). The proposed paper will summarize some existing fuzzy toolboxes for PLC's and present a universal fuzzy system for PLC with a methodology to convert Matlab fuzzy system into PLC's fuzzy structure (Körösi and Turcsek, 2011) Fig.8. Related input(heat) of fig.7 They have many features and include many types of functions and control blocks which make them so practical. In this paper, the procedure of implementation fuzzy logic is explained and the ANFIS which is used as a model of boiler is implemented in a S7 PLC from SIEMENS company which is common in industrial plants. Functions in programmable logic controllers libraries are simple (bit operations, summations, subtractions, multiplications, divisions, etc.) or complex (sine, cosine, absolute value, vector summations, PID, etc.) mathematical functions but often without fuzzy systems, while PLC systems are currently the most commonly used control systems in industry. The aim of the proposed paper is to present a universal fuzzy system s design for PLC and the principle of Matlab fuzzy system conversion into PLC s fuzzy structure. Typically, these processes are still controllable by using and applying the expert knowledge of operators who have learned how the process responds to various input conditions. The most common industrial control systems are Distributed Control System (DCS) and PLC s. DCS (Fig.9.) is a computerized control system used to control the production lines in the industry as oil refining plants, chemical plants, pharmaceutical manufacturing, etc. where continues control (PID loops) is dominating. PLC systems were typical for Fig.9. Typical Architecture of a Distributed Process Control System SIMATIC S7 Fuzzy Control The S7 Fuzzy Control software package consists of two individual products: The product Fuzzy Control mainly contains the control block (function block - FB) and the data block (DB).The product Configuration Fuzzy Control contains the tool for configuring the control block.the FB is already prepared in its full range of performance and with all algorithms for configuration and assigning parameters. A user-friendly tool is available for the configuration and parameter assignment of this function block (Fig.10). Fuzzy controllers are easy to configure on the basis of Fuzzy Control because their functionality is limited to the definition and execution of core functions in fuzzy theory. An instance data block in the CPU of the programmable controller forms the interface between the function block, the configuration tool, and the user. It s possible to download a number of fuzzy applications to a CPU and run them there. Each application is stored in a separate data block; the number of the data block can be freely assigned (Fig.11)

7 Fig.10. Block diagram of the configuration tool sub function Fig.11. Structure of the block calls There are three main parts of the designed fuzzy structure: fuzzification, inference mechanism and defuzzification. Fuzzification is the first step in the fuzzy inferencing process. This involves a domain transformation where crisp inputs are transformed into fuzzy inputs. Crisp inputs are exact inputs measured by sensors and passed into the control system for processing, such as temperature, pressure, rpm's, etc. Each crisp input that is to be processed by the FIU has its own group of membership functions or sets to which they are transformed. This group of membership functions exists within a universe of discourse that holds all relevant values that the crisp input can possess (Körösi and Turcsek, 2011). Fig.12. PLC's integrated fuzzy tools efficient development and configuration of Fuzzy systems. Empirical process expertise and verbalized knowledge by experience can directly transformed into controllers, pattern identification or logic decisions. Associated functions are also easy to configure with the help of FuzzyControl++. The rules are inputs either via a table or via a matrix editor. Dynamic changes of the rules basis identified immediately and, if no rule should be applicable, a value previously prescribed for each output will be use. The inference and defuzzification method used by FuzzyControl++ is the well-known Takagi-Sugeno method. FuzzyControl++ can execute on SIMATIC S7 PLC's, the SIMATIC PCS7 process control system and the WinCC SCADA system and provides special function blocks. It is implemented in this paper in Fig.14. that been shown Fuzzy Pressure Controller made by only 4 Rules that been produced by MATLAB's ANFIS editor, they are in Fig.15 as 4 simple Rules was been built in IF..THEN Box in Fig.14. In Fig.16 about input Heat (Q), at Midrange of Boiler's Operating Point, converted to Triangular function in role of the Fuzzy Controller's input. Fig.17 shows how is configuring Output of the Fuzzy Controller and Fig.18 is showing Obviously real output of PLC after download new fuzzy program to S7-400H PLC and Set point Tracking about Drum Pressure is clear.in Figure is obvious that with increasing or decreasing of energy input also Drum Pressure behaves same way and it is correct because One of important factors in Drum Pressure depends is Heat input (Körösi and Turcsek, 2011). Step 7: STEP 7 is the standard software package used for configuring and programming SIMATIC programmable logic controllers. It is a part of the SIMATIC Siemens industry software, Körösi and Turcsek (2011) and Siemens (), that covers widely usages such as: Based on several types of programming: Flow chart, Contact List, SCL, Grafcet,... Expandable with applications offered by the software industry SIMATIC. Calculation of functional modules and communication modules. Data transfer ordered by event using communication blocks and function blocks. Configuring Connections FuzzyControl++ : The FuzzyControl++ configuration tool for the automation of technical processes enables the

8 Fig.13. FuzzyControl++ Drivers and Runtime Module Fig.16. Fuzzy Controller's input Fig.14. Fuzzy Controller of the pressure created on Fuzzycontrol++ Fig.17. Output of the Fuzzy Controller Fig.15. The Fuzzy Rule Table Fig.18. The fuzzy controller regulation s curve plotter

9 range load ANFIS doesn t show super behaviour and still it needs PID controller to produced training data and implementation ANFIS controller. Fortunately, nowadays or in future with improvement of technology that due to production of high speed CPU's for PLC or DCS systems, problems such as slow deffuzification progress would be solved easily and could be used in many of industrial Plants and factories, and using of new methods such as this paper overcome some problems that create with conventional classic control methods. Fig.19. The fuzzy controller regulation s surface Conclusions The influence of age and low education level on motorcycle accidents resulting in death was undeniably proved to be high in this study. In addition, it was confirmed that men are more exposed to such traumas. Although the frequency of head and face injuries in individuals killed in motorcycle accidents were significant, it decreased from 2007 to In this research, the ability of ANFIS in modelling and control of a boiler is investigated. The strength of ANFIS is related to its data driven nature which it doesn t need any mathematical equation to model a system. It does just need an input/output dataset of a system to make a model to mimic the exact behaviour of the system. A mathematical model of a boiler is addressed by Aström and Bell (1999). This model produces required data for ANFIS in training procedure and further qualification. Designed ANFIS shows good responses to model the real plant (in this work simulated model by Aström and Bell (1999)). In addition, in section 4 another ANFIS was designed to mimic the behaviour of a PID controller which was predesigned in boiler model and again it shows good responses in controlling the plant and it became an alternative way to control the plant. This paper presented a fuzzy system design for PLC system and the automatic fuzzy structure conversion from MATLAB into PLC. The fuzzy toolbox has been verified on Real Training Package and it s suitable for modelling and control nonlinear processes. The fuzzy system can be designed directly in Matlab and after sets of simulations the final fuzzy system can be programmed into PLC. However, trained ANFIS just has good result in a medium load range of boiler which is used in training process. In high References Angell, C., Kind P.M., Henriksen, E. and Guttersued, Q., An empiricalmathematical modeling approach to upper secondary physics. Phys. Educ., 43(3): Aström, K.J. and Bell, R.D., Drum-boiler dynamics. Automatica, 36(3): Boyatt R., Harfield, A. and Beynon, M., Learning about and through Empirical modelling. ICALT, Fleming, P.J. and Purshouse, R.C., Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice, 10(11): Jang, R., ANFIS: adaptive-network based fuzzy inference system, IEEE Trans. Syst. Man Cybern. B., 23(3): Karimi, I. and Salahshoor, K., A New Fault Detection and Diagnosis Approach for a Distillation Column based on a Combined PCA and ANFIS Scheme. 24th Chinese Control and Decision Conference (CCDC), Körösi, L. and Turcsek, D., Fuzzy System for PLC. Inproceedings of MATLAB conference Retrieved from: TLAB12/full_paper/040_Korosi.pdf. Neshat, M., Adeli, A., Masoumi, A. and Sargolzae, M., (2011). A Comparative Study on ANFIS and Fuzzy Expert System Models for Concerete Mix Design. IJCSI, 8(2): Rusinowski, H. and Stanek, W., Neural modelling of steam boilers. Energ. Convers. Manage., 48: Siemens, A.G., FuzzyControl++ User s Manual. Karlsruhe, Germany. Siemens, A.G., SIMATIC Programming with STEP 7, Manual.Siemens, pp Siemens, A.G., SIMATIC. Process Control System PCS 7. OS Process Control (V7.1). Operating Instructions, Germany.