Cai Ca (1) = Inlet reactant concentration in reactor. = Outlet reactant concentration in reactor. =Inlet feed flow rate. =Volume of reactor

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1 International Journal of Technical Research and Applications e-issn: -86, Volume, Issue (Mar-Apr 5), PP. 7- FAULT DTTION AND DIAGNOSIS IN ONTINUOUS STIRRD TANK RATOR (STR) R. Gowthami, Dr. S. Vijayachitra Department of I Kongu ngineering ollege rode, India gowthami.ei@gmail.com, dr.svijayachitra@gmail.com Astract ontinuous Stirred Tank Reactor (STR) here is considered as a nonlinear process. The STR is widely used in many chemical plants. Due to changes in process parameters the accuracy of final product can e reduced. In order to get accurate final product the faults developed in STR during the chemical reaction need to e diagnosed. If not, the faults may lead to degrade the performance of the system. For this purpose there are various fault diagnosis methods are to e considered. Among the methods, the neural network predictive controller can e used to detect faults in STR. Servo response is performed to understand the ehavior of STR. By detecting various faults and with suitale control techniques, the accuracy of the desirale products in STR can e improved. Index Terms ontinuous Stirred Tank Reactor, Neural Network Predictive ontroller, Fuzzy Logic ontroller, Fault detection and estimation. I. INTRODUTION The ontinuous Stirred Tank Reactor (STR) system is highly nonlinear, exothermic, irreversile first order process. In STR, when the reactants are added into the tank the stirrer will stir the reactants to give desired product. Once the equipment is running, it is usually operated at steady state and designed to achieve well mixing. The STR also known as ack mix reactor is commonly used as perfect reactor type in chemical engineering. A STR is a model which is used to estimate individual operation variales while using a continuous agitated tank reactor in order to otain the required output. STR is widely used in the organic chemicals industry for medium and large scale production. The reactor is widely operated y three control loops that will regulate the outlet temperature and the inlet flow rate of the reactant tank level. During the process the heat will e generated and hence the heat of reaction can e removed y a coolant medium that flows through a jacket around the reactor. During the STR process the faults occur which further leads to inaccurate result. A fault is defined as an unexpected change of the system functionality which may e related to a failure in a physical component such as sensor and actuator. In order to detect various types of faults in STR the Neural Network Predictive ontroller can e applied. II. MATHMATIAL MODLING OF STR A reaction will create new components while simultaneously reducing reactant concentrations. The reaction may give off heat or give required energy to progress the process []. Figure. shows the STR consists of one inlet reactants and one outlet flow rate. In STR the outlet concentration is controlled y changing the volumetric flow rate [8]. Figure. shows the characteristic of the concentration and volumetric flow rate. Figure. shows the servo response of ontinuous Stirred Tank Reactor. If the outlet concentration is not equal to the set point then the volumetric flow rate is adjusted in order to make outlet concentration to reach the desired set point. By the mass alance (typical unit, Kg/s), The Rate of change of mass in the system=the Rate of mass flow in Rate of mass flow out da dt ra K ai a F V K R T A = Inlet reactant concentration in reactor = Outlet reactant concentration in reactor =Inlet feed flow rate =Volume of reactor =Frequency factor =Activation energy =Universal gas constant =Reactor temperature.8.6 F V e RT ai a K e RT a () Time Figure. ontinuous Stirred Tank Reactor volumetric flow of coolant time Figure. Behavior of Manipulated Variale- ontroller Variale in ontinuous Stirred Tank Reactor 7 P a g e

2 A volumetric flow of coolant Time time Figure. Servo Response of ontinuous Stirred Tank Reactor III. MTHOD OF FAULT DIAGNOSIS The term fault is generally defined as a departure from an acceptance range of an oserved variale [6], [7]. Figure 4. shows that the faults developed in the plant diagnosed y the sensor and the sensor value will then e compared with the set point in the comparator. If there is any error in the sensor then the feedack controller generates a signal to the actuator. The actuator signal will e given to the diagnosis system. If any error occurs in the actuator, the diagnosis system will provide an alarm. The fault diagnosis system provides the supervision system with information aout the onset, location and severity of the faults. Based on the system inputs and outputs together it will make fault decision information from the fault diagnosis system. Figure 4. General omponents of Fault Diagnosis Framework FUZZY LOGI ONTROLLR Fuzzy logic is an extension of crisp set. A fuzzy logic system is a type of control system that work ased on fuzzy logic. The fuzzy logic controller is ased on mathematical system that evaluates input values for fuzzy logic system in terms of sensile variales. The logical variales take values in two ways they are continuous and discrete values. If the logical variale uses continuous value which ranges from to in contrast to digital logic and if logical variales for fuzzy logic operate on discrete values which is either or that is true or false respectively. Fuzzy system is useful in any situation in which the measurement depends strongly on the context or human opinion. A fuzzy reasoning algorithm estalishes a preliminary fuzzy diagnosis system. Fuzzy logic controller is commonly used in machine control operations. hen the logic involved in fuzzy logic controller can deal with concepts which can e expressed in terms as neither true nor false ut consideraly as partially true. In such cases the rules are created to deal oth with true, partially true, false and partially false. Fuzzy sets mean the input variales. The input variales in a fuzzy operating machine can e otained y mapping the sets of memership functions. Fuzzificaion means the series of action for converting a crisp value to a fuzzy value. International Journal of Technical Research and Applications e-issn: -86, Volume, Issue (Mar-Apr 5), PP. 7- The fuzzy logic controller uses inaccurate or not precise input and output variales y using memership function. The memership function can e expressed in terms of linguistic variales. In the fuzzy logic controller the if-then rules are used to identify the fault and fault free data. At the same time it provide the symptoms for faulty and fault less data y predefined fuzzy sets. The amount of fault that present is otained y comparing the rules of reference model and with the rules of fuzzy reference model which is identified y using data otained from STR plant [],[]. Here the process historical data is used [7]. The process historical data means data otained from STR plant. IV. FUZZY DISION MAKING The fuzzy logic controller is used for direct integration of the human operator into the fault detection and supervision of STR process. hanges in any variale in the process will reduce the accuracy of the final product. It also provides supervision of entire process. In the supervision of the process the alarm will e provided. If any error occurs in the process the alarm will e generated. For this purpose the fuzzy decision making is necessary. In order to avoid any incorrect decision which may cause false alarm then the fuzzy decision making is used to decide whether the fault has occurred and at which place in the process the fault has occurred. The fuzzy decision making is similar to the expert system and supervisory control. V. FAULT STIMATION BY FUZZY LOGI ONTROLLR The fuzzy logic system handles the imprecision of input and output variales directly y defining them with memerships and sets that can e expressed in linguistic terms. The fuzzy reference models are made up from if-then rules which descrie the symptoms of faulty and fault free of predefined fuzzy reference sets. A particular model is defined y specifying the values of the elements of its associated fuzzy relational array. ach element of the array is a measure of the crediility that the associated rules correctly descries the ehavior of the system around the particular operating point. For fault detection and estimation in STR using Fuzzy Logic ontroller. The fault level in STR depends on four variales such as feed flow rate (F ), outlet reactant concentration ( A), coolant temperature (T ) and feed temperature (T ). These four variales fault level depends on the volume of reactor (V), reactant concentration in the reactor ( A), reactor temperature (T) and Jacket temperature (T ) [5]. Based on these fault range the Fuzzy Logic ontroller is developed. The Fuzzy Logic ontroller consists of four inputs and four outputs. The memership function is developed for those four inputs and four outputs. The rules are created for these variales. Based on the input fault range the fault level is estimated y using Fuzzy Logic ontroller. The figure.5 shows various inputs to the Fuzzy Inference System such as volume of reactor (V), reactant concentration in the reactor ( A), reactor temperature (T) and jacket temperature (T ) and the fault outputs considered for Fuzzy Inference System are feed flow rate (F ), outlet reactant concentration ( A), coolant temperature (T ) and feed temperature (T ). Figure 5. Fuzzy Inference System ditor for overall fault 8 P a g e

3 International Journal of Technical Research and Applications e-issn: -86, Volume, Issue (Mar-Apr 5), PP. 7- ith the help of Fuzzy Logic ontroller, various faults considered are detected and estimated and the results otained are shown in Tale Tale : Fuzzy Logic Based Fault stimation in STR Figure 8. Fuzzy Inference System ditor for fault in coolant temperature (T ) Similarly the fault level for individual variale can also e otained. Figure.6 shows the fault in feed flow rate (F ) depends on volume of reactor (V), reactant concentration in the reactor ( A), reactor temperature (T) and jacket temperature (T ). After developing the fuzzy logic controller the estimation of fault level in feed flow rate (F ) can e done is shown in tale. Tale 4: Fuzzy Logic Based Fault stimation for oolant Temperature (T ) Similarly the fault level in feed temperature (T ) depends on volume of reactor (V), reactant concentration in the reactor ( A), reactor temperature (T) and jacket temperature (T ) as shown in the figure 9. Tale 5 shows the estimation of fault level in feed temperature (T ). Figure 6. Fuzzy Inference System ditor for fault in feed flow rate (F ) Tale : Fuzzy Logic Based Fault stimation for Feed Flow Rate Similarly the fault level in outlet reactant concentration ( A) is ased on volume of reactor (V), reactant concentration in the reactor ( A), reactor temperature (T) and jacket temperature (T ) as shown in the figure 7. Tale shows the estimation of fault level in outlet reactant concentration ( A). Figure 9. Fuzzy Inference System ditor for fault in feed temperature (T ) Tale.5 Fuzzy Logic Based Fault stimation for feed temperature (T ) Figure 7. Fuzzy Inference System ditor for fault in outlet reactant concentration ( A) Tale : Fuzzy Logic Based Fault stimation for Outlet Reactant oncentration Similarly the fault level in coolant temperature (T ) depends on volume of reactor (V), reactant concentration in the reactor ( A), reactor temperature (T) and jacket temperature (T ) as shown in the figure.8. After developing the fuzzy logic controller the estimation of fault level in coolant temperature (T ) can e otained as shown in tale 4. VI. NURO PRDITIV XL SOFTAR Prediction means estimate, forecast and predict the future condition for this purpose the neural predictive XL software is used. In Microsoft excel the predictions can e done y a uilt in tool ut the correctness of its results is greatly decreased when non-linear relationship occur or if any missing data are present. Neural networks are a tried, tested and most commonly used technology for most complex prediction operation. It is modeled y considering the human rain. The neural network is joined onto one another networks of separate processors [], [4]. By changing the connections of these neural networks the finding answer to a prolem can e identified. One of the major reasons that the analysts not using these latest methods such as neural networks in order to improve forecasts ecause the neural network method is very difficult to monitor. Neuro XL Predictor removes the mental process and practical limit y hiding the complicated nature of its latest neural network ased methods while taking profit of analysts having present information in mind. Since users make the act of predicting the future through the commonly seen 9 P a g e

4 excel point of interaction, it reduces the learning time, greatly decreasing the intervening period etween loading the software and performing useful predictions. The application is extremely known automatically and not difficult to use this neuro XL predictor software. In neural predictive XL y the knowledge of previous inputs and outputs it is ale to predict what the future output for present input can e identified. In this neural predictive XL the sensor fault input and output data is loaded in the excel sheet y using neural predictive XL software the future output is predicted. Tale.6 the fault in the output can e predicted y using neuro predictor XL. In ontinuous Stirred Tank Reactor the sensor fault is considered. Neuro predictor XL is a spreadsheet which is used to detect fault in ontinuous Stirred Tank Reactor. The sensor fault in STR is used [9]. In the sensor fault the flow rate of the liquid at the inlet ( ), control valve opening ( Fcv ), temperature of the inlet reactant ( Ti ), control valve opening ( ), temperature of the tank ( ), temperature of the outlet coolant ( ), flow rate of coolant ( ), liquid level ( L ) are the variales considered in STR. By loading the sensor faults of STR in the spread sheet the neuro predictor XL software is used to predict future fault in STR. The neuro predictor XL software predict future fault in STR y seeing present and past faults in sensor of STR. So this neuro predictor XL software is useful for analysts to predict future fault in STR. By predicting the future fault the analysts can take some preventive measure to reduce the fault efore its effect felt in the process. By doing these measure the final outcome of the process can e maintained. Tale.6 Prediction of Fault in STR here, L cv F i F cv T i L cv T F i T j Predicted Output = Flow rate of liquid at inlet ( cm / s ) = ontrol valve opening = Temperature of the inlet reactant(º) = ontrol valve opening = Temperature of the tank (º) International Journal of Technical Research and Applications e-issn: -86, Volume, Issue (Mar-Apr 5), PP. 7- off heat or give required energy to proceeds the process. Figure. shows the STR consists of two inlet reactants and one outlet flow rate. The two inlet reactants are which is inlet flow rate of concentrated feed and is inlet flow rate of diluted feed [], [7]. By the mass alance (typical unit, Kg/s), The Rate of change of mass in the system=the Rate of mass flow in Rate of mass flow out T F j dt () V h () (V dt h ) () here V =. (. dt h ) (4) here h =Liquid level (cm) =Inlet flow rate of the concentrated feed =Inlet flow rate of the diluted feed =Outlet flow rate (cm /s) (cm /s) (cm /s) The concentration of the continuous stirred tank reactor is given in the following equation d ( )( ) ( )( ) r dt h h (5) here, k r (6) ( k ) here, =The concentration of product at the output of the process ( =Inlet concentration feed ( =Outlet concentration feed ( =Inlet flow rate of the concentrated feed ( cm /s ) =Outlet flow rate of the diluted feed ( cm /s ) k, k =The constants associate with the rate of consumption =Liquid level (cm) h T j F j L = Temperature of the outlet coolant water (º) = Flow rate of coolant ( cm / s ) = Liquid level (cm) VII. NURAL NTORK PRDITIV ONTROLLR The Mathematical Model of STR for two tank system is A reaction will create new components while simultaneously reducing reactant concentrations [9]. The reaction may give Figure.ontinuous Stirred Tank Reactor P a g e

5 International Journal of Technical Research and Applications e-issn: -86, Volume, Issue (Mar-Apr 5), PP. 7- oncentration in mol/(cm) Servo Response of STR Reference Signal Figure. Neural Network Predictive ontroller The neural network predictive controller is a graphical user interface tool as shown in figure. From that neural network predictive controller select the plant identification which consists of neural network plant model. The plant model predicts the future plant outputs. The neural network plant model consists of one hidden layer. By generating training data y giving series of random reference signal the plant input and output data will e generated. By using trainlm the plant model training is done from this the training data for NN predictive controller will e otained as shown in figure. and Validation data for NN predictive controller will e generated as shown in figure Input 5. rror NN Output 5 Figure. Training Data for NN Predictive ontroller Input rror -. NN Output Figure. Validation Data for NN Predictive ontroller Figure 4. shows the servo response of STR. By using the neural network predictive controller plant model the characteristic of concentration in STR is otained Time in Secs Figure 4. Servo Response of STR VIII. ONLUSION AND FUTUR SOP The application of ontinuous Stirred Tank Reactor is very essential nowadays. Using material alance condition, the STR process is effectively modeled which provides a etter understanding of its characteristic ehavior of the process. The modeling is effectively verified y its servo response. Various sensor faults considered in STR are detected and estimated in percentage y means of using Neural Network Predictive ontroller and Fuzzy Logic ontroller and found good results. This fault detection and estimation work can e further extended to actuator faults also. IX. RFRNS [] Dong-Juan Li., 4, Neural network control for a class of continuous stirred tank reactor process with dead-zone input, lsevier Journal of Neurocomputing, Vol., pp [] Kanse Nitin G., Dhanke P.B. and Thomare Ahijit.,, Modeling and Simulation Study for omplex Reaction y Using Polymath, Research Journal of hemical Sciences, Vol., No.4, pp [] Manikandan P., Geetha M., Jui K., Jovitha Jerome.,, Fault Tolerant Fuzzy Gain Scheduling Proportional-Integral- Derivative ontroller for ontinuous Stirred Tank Reactor, Australian Journal of Basic and Applied Sciences, Vol.7, No., pp [4] Rihan Zafira Adul Rahman, Azura he Soh, Noor Fadzlina inti Muhammad.,, Fault Detection and Diagnosis for ontinuous Stirred Tank Reactor Using Neural Network, Kathmandu University Journal of Science, ngineering and Technology, Vol.5, No., pp [5] Souragh Dash, Raghunathan Rengaswamy, Venkat Venkatasuramanian.,, Fuzzy- logic ased trend classification for fault diagnosis of chemical processes, lsevier Journal of omputers and hemical ngineering, Vol.7, pp [6] Venkat Venkatasuramanian, Raghunathan Rengaswamy, Kewen Yin, Surya N. Kavuri.,, A review of process fault detection and diagnosis Part I:Quantitative model-ased methods, lsevier Journal of omputers and hemical ngineering, Vol.7, pp.9-. [7] Venkat Venkatasuramanian, Raghunathan Rengaswamy, Surya N. Kavuri, Kewen Yin.,, A review of process fault detection and diagnosis Part III: Process history ased methods, lsevier Journal of omputers and hemical ngineering, Vol.7, pp [8] Vishal Vishnoi, Suhransu Paee, Gagandeep Kaur.,, ontroller Performance valuation for oncentration of Isothermal ontinuous Stirred Tank Reactor, International Journal of Scientific and Research Pulication, Vol., No.6, pp.5-5. [9] Yunosuke Maki and Kenneth A. Loparo.,997, A Neural- Network Approach to Fault Detection and Diagnosis in Industrial Processes, I Transactions on ontrol Systems Technology, Vol.5, No.6, pp P a g e

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