l£>/vOt\ / * ]6 /OS PROPOSED AUTOMATION OF TEA WITHERING PROCESS USING FUZZY LOGIC CONTROLLER A dissertation submitted to the Department of Electrical Engineering, University of Moratuwa in partial fulfilment of the requirements for the degree of Master of Science by JAYASUNDARA MUDIYANSELAGE INDIKA JAYASUNDARA -jmAKY uiwc«rn <* tDMUM^ irj ianw " MUWfl Supervised by: Dr. Lanka Udawatta, Mr. K.Raveendran. j n \ 3 8 Department of Electrical Engineering University of Moratuwa, Sri Lanka February 2008 University of Moratuwa 91250 9 1 2 5 0 DECLARATION The work submitted in this dissertation is the result of my own investigation, except where otherwise stated. It has not already been accepted for any degree, and is also not being concurrently submitted for any other degree. J.M.I Jayasundara Date endorse the declaration by the candidate. Dr. Lanka Udawatta - / r a ^ K.Raveendran 3 CONTENTS Declaration 3 Contents 4 Abstract 6 Dedication 7 Acknowledgement 8 List of Figures 9 List of Tables 10 1. Introduction 11 1.1 Background 11 1.2 Motivation 14 1.3 Objective 14 2. Tea Processing 15 2.1 Withering 15 2.2 Rolling 16 2.3 Fermentation 17 2.4 Drying 17 2.5 Grading and Packaging 17 3. Fuzzy Controller 18 3.1 Fuzzy logic History and Applications 18 3.2 Fuzzy logic in Industrial Automation 18 3.3 Multivariable Control 19 3.4 Structure of a Fuzzy Controller 20 3.4.1 Pre-processing 20 3.4.2 Fuzzyfication 20 3.4.3 Rule Base 21 3.4.4 Inference Engine 21 3.4.5 Defuzzyfication 22 3.4.6 Post Processing 22 4 4. Statement of the Problem 23 4.1 Preliminaries 23 4.2 Behaviour of Process Inputs 24 5. Proposed Solution 28 5.1 Methods and Techniques 28 5.2 Development of the Control Algorithm 36 5.3 The Fuzzy Inference System 38 5.4 Rule Base 40 6. Results and Analysis 45 6.1 Evaluation of the Proposed Control Strategy 45 6.2 Energy Saving Approximations 49 7. Application of the Proposed Method 53 7.1 Implementation 53 7.2 Configuring Fine Tuning and Commissioning 54 7.3 Practical Issues 54 8. Conclusions 55 8.1 Conclusions, Remarks and Discussion 55 8.2 Considerations for Future Research 56 References 57 Appendix 59 ABSTRACT Tea processing is one of the major energy intensive food processing industries in Sri Lanka and the process "withering", which is the first stage of the complete process, accounts for about half of the total electrical energy consumption in the tea industry. This process consumes electrical energy mainly to run the withering fans. The traditional methods of controlling withering process have proven to be very inefficient in energy point of view. This study proposes a fuzzy logic based withering control methodology which will optimize the electrical energy consumption of the process while maintaining the quality of the processed tea. Present process analysis was done with field experimental data and the performance of the proposed system was evaluated on Matlab® platform. This proposed control structure can be implemented, modified and field tuned for optimization depending on the practical installation characteristics and expected to save a considerable amount of electrical energy in tea processing industry. ACKNOWLEDGEMENT I would like to express thanks to my supervisors Dr. Lanka Udawatta and Mr. K.Raveendran for their continuing guidance, encouragement, and support throughout the course of my study. My sincere thanks go to the officers at Tea Research institute of Sri Lanka, Thalawakelle, Faculty of Engineering, University of Moratuwa, for helping in various ways to clarify the things related to my work in time with excellent cooperation and guidance. Also I must thank the management and staff of Rotax Limited, for support and encouragement extended to me in making this study a success. Finally, I should thank many individuals, friends and colleagues who have not been mentioned here personally in making this educational process a success. May be I could not have made it without your support. 8 LIST OF FIGURES Figure 3.1: Standard Fuzzy Logic Controller. Figure 4.1: Withering Trough Arrangement. Figure 4.2: Variation of Air Flow with Process Time. Figure 4.3: Variation of Chamber Pressure with Process Time. Figure 5.1: Fuzzyfication of Input Air Flow Figure 5.2: Fuzzyfication of Input Chamber Pressure Figure 5.3: Fuzzyfication of Input Relative Humidity Figure 5.4: Fuzzyfication of Input Chamber Temperature Figure 5.5: Fuzzyfication of Output Motor Frequency Figure 5.6: Fuzzyfication of Output Damper Angle Figure 5.7: Layout of the Withering Trough Figure 5.8: Fuzzy Withering Control System Figure 6.1: Comparison of Normal Process and Fuzzy Withering Controller Outputs Figure 6.2: Graphical View of Motor Frequency Pattern With Respect To Chamber Pressure and Air Flow Readings. Figure 6.3: Graphical View of Damper Angle Variation Pattern With Relation To Humidity and Chamber Pressure Readings Figure 7.1: Structure of the Proposed Control System Figure A.l: Motor Power Consumption with Time (At Constant Frequency) Figure A.2: Air Flow Vs Chamber Pressure LIST OF TABLES Table 5.1: Fuzzyfication of Input Air Flow Table 5.2: Fuzzyfication of Input Chamber Pressure Table 5.3: Fuzzyfication of Input Relative Humidity Table 5.4: Fuzzyfication of Input Chamber Temperature Table 5.5: Fuzzyfication of Output Motor Frequency Table 5.6: Fuzzyfication of Output Damper Angle Table 6.1: Matlab Fuzzy Inference Evaluation Results Table 6.2: Electrical Energy Consumption of the Present and Proposed systems Table A. 1: Field Experiment Data Table A.2: Air Velocity through Withering Bed. Table A3: Withering Chamber Pressure Variation during the Process Table A.4: Motor Power Consumption with Time Chapter One Introduction 1.1 Background Tea is nearly 5,000 years old and was discovered, as legend has it, in 2737 B.C. by a Chinese emperor when some tea leaves accidentally blew into a pot of boiling water. In the 1600s, tea became popular throughout Europe and the American colonies. Now, tea is consumed as a beverage throughout the world and grown widely in countries of Asia, Africa and the Near East. Earliest mention of tea is from China in 350 B.C. It found its way to Europe in 1559, to England in 1615, and to Indonesia in 1684. Commercial cultivation began in India in 1823 and in 1867 in Sri Lanka. [6] Tea industry plays a vital role in the economy of Sri Lanka. It is the major plantation crop in the country and Sri Lanka has the dignity of being the world third largest tea producer. Based on the 2003 data from World Bank, tea export is around 13% of the total export (663billion/5,133billion) of the country. According to annual report-2005 of Central Bank of Sri Lanka, annual production of made tea is nearly 317 thousand metric tones and 97% (309 thousand metric tones) of this production is exported. [8] Tea plantation was first introduced in mid 19th century, during the British colonial era and since then tea industry faces ups and downs, being the major commercial crop in the country. Geographical conditions of the country, especially of the hill country is well suited for tea plantation and this factor causes largely for this industry to be flourished. Electrical energy is a critical element in tea industry, because many machines which are used for tea processing are operated by electrical power. During the time when tea was first introduced to Sri Lanka, there was not a minute crisis of energy. In some cases, up- country factory owners generated their own power from micro hydro resources available at their plantations. However the condition is different now. Electricity price has been increased rapidly during the past decade, making it comparatively higher than the electricity prices of neighbouring countries as well as other tea export countries. Due to this reason unit cost of made tea has increased tremendously during the last few years. 11 High prices due to the high production cost of made tea ultimately impedes its competitiveness in the world market, even in spite of the quality and undeniable flavour of Sri Lankan Tea. Therefore it is distinct that, to reduce the production cost of made tea, reducing the operational cost or energy cost is a must. Yet most of the machines used in tea factories are those used during colonial times and the concern for energy efficiency of these machines is very little. Lot of uneducated and inexperienced employees are engaged in tea sector. Further, most of the employees of tea factories are non-technical people and energy conservation practices are very rarely being applied. Tendency of the factory owners to use better and energy efficient systems seems to be poor and the efforts put so far to change this situation have not achieved the expected results. Tea production associates with a number of machinery which are operated by electrical energy. Hence energy cost is a crucial factor in the production cost of made tea. Therefore unit cost of made tea can be brought down by reducing the specific energy (energy required to produce 1 kg of made tea) consumption of the production. For it to be achieved energy conservation and efficient methods and practices should be exercised at plant level. In-house expertise knowledge and capacity is a vital factor for achieving this. But many small and medium scale tea industries do not have facilities to undertake tests like testing the quality of tea, estimation of moisture, measuring process parameters like air flow rate, pressure, etc. Therefore it has become difficult to know and assess their current level of performance. Lack of dedicated technical resource persons to look in to technical improvements, lack of capacity to access latest technological information are also barriers encountered in improving energy efficiency and tea quality by the tea industry in Sri Lanka. Withering process consumes electrical energy to run the blower fans. Withering accounts for about 50% of the total electricity consumption in tea processing. [1] Withering is achieved by placing tea leaves on the troughs and air being blown by an axial flow fan. To assist in having a control over the process a combination of hot and ambient air is used. It has been found that around 50% of the withering is achieved during the first 4-6 hours of the process and for the rest it takes around 6-10 hours. During the initial phase of the withering process, moisture on the surface of the leaf gets evaporated and from there onwards moisture from the interior of the leaf is propagated to the surface through 12 diffusion process. The moisture removal becomes slower when the diffusion process takes place from the interior of the tea leaves. [1] Industry has always required tools for increasing production rate and/or product quality while keeping the costs as low as possible. Doubtless, one of these tools is automatic process control. In the early days, engineers essentially used their knowledge of the process and their understanding of the underlying physics to design controllers, such as P1D controllers. Analysis methods were combined with an empirical approach of the problem to design controllers with an acceptable level of performance. During the last decades, with the increasing competition on the markets, more powerful control techniques have been developed and used in various industrial sectors in order to increase their productivity [23]. Tea withering process is very difficult process to model and involves multiple variables. Also it is a non linear process and involves high level of operator expertise. In this type of situations fuzzy logic base controlled systems have proved to be the more appropriate approach for achieving process automation objectives. There are five types of systems where fuzziness is necessary or beneficial: • Complex systems those are difficult or impossible to model • Systems controlled by human experts • Systems with complex and continuous inputs and outputs • Systems that use human observation as inputs or as the basis for rules • Systems those are naturally vague Fuzzy logic controller system has several advantages over conventional control methodologies: • Fewer values, rules, and decisions are required • More observed variables can be evaluated • Linguistic, not numerical, variables are used, making it similar to the way humans think • It relates output to input, without having to understand all the variables, permitting the design of a system that may be more accurate and stable than one with a conventional control system • Simplicity allows the solution of previously unsolved problems 13 • Rapid prototyping is possible because a system designer doesn't have to know* everything about the system before starting work • They're cheaper to make than conventional systems because they're easier to design • They have increased robustness • They simplify knowledge acquisition and representation • A few rules encompass great complexity Also there are few drawbacks of fuzzy systems when compared with conventional systems: • I t ' s hard to develop a model from a fuzzy system • Though they're easier to design and faster to prototype than conventional control systems, fuzzy systems require more simulation and fine tuning before they're operational [14]. 1.2 Motivation While the research institutions dedicated to the tea industry have the best knowledge on the tea process and the related quality and energy issues, they lack the knowledge in new technologies and research capacities especially in electrical and automation related areas. Lack of unified effort between these two fields has severely affected the utilization of new concepts and technologies for the improvement of the tea industry in Sri Lanka. This study is a result of an effort to utilize advancements in the field of electrical engineering for the advancement of the tea processing industry. 1.3 Objective The objective of this study is to develop a withering control methodology which will optimize the electrical energy consumption of the process while maintaining the quality of the processed tea. Also it is a main objective to make the system flexible in terms of control algorithm considering the non linear behaviour of the process and the need for using the developed concept and the system as a structural basis for further research and experiments. 14 Chapter Two Tea Processing Sri Lankan tea industry is around 150 years old and a main source of foreign exchange of the island. The tea manufacturing process consists of five main stages, 1. Withering 2. Rolling 3. Fermentation 4. Drying 5. Grading & Packaging 2.1 Withering Withering is principally a drying process to remove the surface moisture and partially the internal moisture of the freshly harvested green leaves. In addition, withering is done to get the correct physical condition, which will allow the leaves to be rolled without breaking. Also, the withering promotes dissipation of heat generated during continuous respiration (chemical changes). There are two major types of withering, open or natural withering and artificial or trough withering. In the open method, withering is controlled by the thickness of spread, and the length of time of the withering phase. Trough withering is a widely used withering process. Usually, the green leaves from the tea estates are brought to the factory in the afternoons and are spread thinly on banks of troughs (tats). The troughs are made of metal wire meshes with wooden support on which tea leaves are spread and the air is blown from the bottom so that the air passes through the green leaves. The air is supplied either directly from an air heater or the exhaust from the dryer, which is usually located at ground level whereas troughs are located in an upper floor. Withering is done at 20-30°C depending on the climatic conditions. For best withering, a wet and dry bulb temperature difference of 4°C is maintained. During withering, the moisture content of the green leaves is reduced up to 55% for Orthodox tea 15 production. Depending on the weather and the condition of the leaf, withering takes about 12-18 hours. [6] In withering, more air is blown at the initial stage and on an average the air flow rate is about 15,000-20,000 cubic feet per minute (CFM) depending on the size of the trough. After four to five hours, the flow rate can be reduced to two-thirds of its initial value. To reduce the air flow rate, throttle valves are provided at the fan inlets. Once proper withering is achieved, the air flow is continued to prevent the spoiling of withered leaves. Withering troughs are generally installed in the first floor of the factory. Green leaf is spread over a wire mesh which is fitted on plenum chamber. The trough should be fitted with a suitable fan to deliver the required quantity of air as per the size of the trough. To achieve proper withering the fan has to deliver around 15-20 CFM air for every one square foot of trough area. For artificial withering hot air from the drier room is mixed with outside air and used. Fans are arranged in such a way that they can draw hot air from the drier and cool air from the atmosphere. The current of air performs a twofold function: Conveying heat to the leaf as well as carrying of water vapour through a bed of green leaves to achieve physical withering. Whenever the hygrometric difference is below 3° C, hot air is mixed in suitable proportion or heat energy is supplied to increase the hygrometric difference with a corresponding rise in the dry bulb temperature of air. But the dry bulb temperature of air after mixing should not exceed a maximum limit beyond which the quality of the withered leaf is not acceptable. At present, almost all the Sri Lankan tea factories practice trough withering. The dimensions of trough in most of the factories vary considerably. The width of the standard (conventional) trough is 6' and its length varies between 60' and 120'. [1] 2.2 Rolling The chemical compounds of the tea leaves are released to initiate oxidation in the fermentation process. Rolling twists the leaf, and at the same time, breaks the leaf structure (cells) to release the juices (catechins and enzymes) for oxidation.[6] A compressed drum/roller twists the withered leaves on a continuous circular motion. A rolling machine size varies from 150-325 kg of leaf per hour. The roller has minimum 16 cutting action and more compressed rolling action. The compression of the roller depends on the type of withering. Low pressure rollers are suitable for under-withered leaves and high pressure rollers for over-withered leaves. Normally, light rolling at the initial stage and heavier rolling at the later stage of the rolling operation are done. The duration of rolling varies from 15 to 45 minutes. 2.3 Fermentation The rolling process is followed by fermentation, which is a biochemical oxidation process where tea flavours are produced. The fermentation is an important process in black tea production. Oxidation takes place in a room where high humidity air at a temperature of 23-29°C is maintained. The fermentation process does not require any energy unless humidifiers are used. 2.4 Drying The fermented tea particles are dried or fired to arrest the fermentation and also to reduce the moisture to about 3%. Clean and odourless hot air is passed through the fermented tea particles in dryers. The temperature of the hot air varies between 90-160°C depending on the type of dryer. Drying or firing is a thermal energy intensive operation that also consumes electrical energy to drive blowers and dryers. 2.5 Grading and Packaging Dried tea consists of particles of different sizes, stalks, fibres, leaf portions, etc. The dried tea is sorted into different grades by passing it over mechanically oscillated sieves for grading. In grading, tea particles are sifted into different sizes then classified according to appearance and type. The colour separator recently being used in the grading process could remove stalk particles by tracing the colour electronically. After grading, tea is packed in airtight containers in order to prevent absorption of moisture. Packaging could be either in tea chests (wood based) or tea bags, etc. packaging as per requirement. 17 Chapter Three Fuzzy Controllers A fuzzy control system is a control system based on fuzzy logic - a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 0 and 1 (true and false). Fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans. 3.1 Fuzzy Logic History and Applications Fuzzy logic was first proposed by Lotfi A. Zadeh of the University of California at Berkeley in a 1965 paper. [12] He elaborated on his ideas in a 1973 paper that introduced the concept of "linguistic variables", which in this article equates to a variable defined as a fuzzy set. Other research followed, with the first industrial application, a cement kiln built in Denmark, coming on line in 1975. 3.2 Fuzzy Logic In Industrial Automation Fuzzy logic has proven well its broad potential in industrial automation applications. In this application area, engineers primarily rely on proven concepts. For discrete event control, they mostly use ladder logic, a programming language resembling electrical wiring schemes and running on programmable logic controllers (PLC). For continuous control PID type controllers are mostly employed. While PID type controllers do work fine when the process under control is in a stable condition, they do not cope well in other cases [10]: 18 LIBRARY UNIVERSITY CT MORATUWA, SRI LANKA M0RATUW4 The presence of strong disturbances (non-linearity) Time-varying parameters of the process (non-linearity) Presence of dead times The reason for this is that a PID controller assumes the process to behave in a strictly linear fashion. While this simplification can be made in a stable condition, strong disturbances can push the process operation point far away from the set operating point. Here, the linear assumption usually does not work any more. The same happens if a process changes its parameters over time. In these cases, the extension or replacement of PID controllers with fuzzy controllers has been shown to be more feasible. 3.3 Multivariable Control The real potential of fuzzy logic in industrial automation lies in the straightforward way fuzzy logic renders possible the design of multi-variable controllers. In many applications, keeping a single process variable constant can be done well using a PID controller. However, set values for all these individual control loops are often still set manually by operators. The operators analyze the process condition, and tune the set values of the PID controllers to optimize the operation. This is called "supervisory control" and mostly involves multiple variables. PID controllers can only cope with one variable. This usually results in several independently operating control loops. These loops are not able to "talk to each other". In cases where it is desirable or necessary to exploit interdependencies of physical variables, one is forced to set up a complete mathematical model of the process and to derive differential equations from it that are necessary for the implementation of a solution. This is not practical in many situations. The general observation in industry is that single process variables are controlled by simple control models such as PID, while supervisory control is done by human operators. Fuzzy logic provides an efficient solution to the problem. Fuzzy logic enables design supervisory multi-variable controllers from operator experience and experimental results rather than from mathematical models. 19 9 1 2 5 0 3.4 Structure of a Fuzzy Controller With fuzzy logic, practical experience which can be described verbally is implemented qualitatively or quantitatively, in the form of IF-THEN assignments (fuzzy logic rules). The block diagram of a fuzzy logic system is shown below. Several input variables are linked with an output variable. In the context of fuzzy logic, we often speak of linguistic input and output variables. This is because when fuzzy logic rules are stated, the input and output variables are added verbally When the structure of a complete fuzzy controller is considered, there are specific components characteristic of a fuzzy controller that supports the design procedure. The following is a block diagram representation of these components. Figure 3.2: Standard Fuzzy Logic Controller. 3.4.1 Pre-processing The inputs from process sensors/transducers have to be first fed to a pre-processor which will do the necessary filtering to remove noise, scaling of the inputs, etc. 3.4.2 Fuzzyfication The first block inside the controller is Fuzzyfication and it converts the input data to degree of membership by interacting with the membership functions. The Fuzzyfication block therefore matches the input data with the conditions of the rules to determine how well the condition of each rule matches the particular input at that instance. 20 Membership functions: In designing a fuzzy controller, building the membership functions is a first most task to be accomplished. There are two specific questions to consider, 1. How to determine the shape of the membership functions? 2. How many membership functions are necessary and sufficient? According to fuzzy set theory, the choice of shape and width is subjective, but some rules of thumb apply. [12] • The membership functions should be sufficiently wide to allow for noise in the measurements. • A certain amount of overlap is desirable; otherwise the controller may run in to poorly defined states, where it does not return a well defined output. The following is a recommended practice to start with a design [12]. • Start with triangular sets. All membership functions for a particular input or output should be symmetrical triangles of the same width. The leftmost and the rightmost should be shouldered ramps. • The overlap should be at least 50%. The width should be initially chosen so that each value of the universe is a member of at least two sets, except possibly for the elements at the two extreme ends. If on the other hand there is a gap between two sets no rules will fire for values in the gap. 3.4.3 Rule Base The rules may use several variables both in the condition and the conclusion of the rules. The controllers therefore can be applied for multi input multi output (MIMO) and single input single output (SISO) situations. Rule formats: A linguistic controller contains the rules in an "IF-THEN" format but they can be presented in different formats. 3.4.4 Inference Engine In the fuzzy inference engine, fuzzy logic principles are used to combine the fuzzy "IF- THEN" rules in the fuzzy rule base into a mapping from a fuzzy set in the input universe 21 to a fiizzy set in the output universe. There are two ways to infer with a set of rules: composition based inference and individual-rule based inference, Composition Based inference: In composition based inference, all rules in the fuzzy rule base are combined into a single fuzzy relation, which is then viewed as a single fuzzy "IF-THEN" rule. Individual-Rule Based inference: In individual-rule based inference, each rule in the fuzzy rule base determines an output fuzzy set and the output of the whole fuzzy inference engine is the combination of the individual fuzzy sets. The combination can be taken either by union or by intersection. 3.4.5 Defuzzification The resulting fuzzy set must be converted to a number that can be sent to the process as a control signal. This operation is called Defuzzification. There are several defuzzification methods. 3.4.6 Post processing In case the output is defined on a standard universe, this must be scaled to engineering units. The post processing block often contains an output gain that can be tuned. 22 Chapter Four Statement of the Problem 4.1 Preliminaries In the present practice, withering process is controlled manually by experienced operators using mostly rules of thumb deciding factors which they have developed through several years of experience. As a result the energy consumption of the process is not optimized and also the quality of withering is not uniform. The withering process is a highly nonlinear process involving multiple variables and difficult to model mathematically. The present practice of manual feeling of the flow of air through the withering bed is very much dependent on the operator's skills and direct instrumentation and automation of the process is also not possible due to the very low air velocity present at the withering bed. Therefore an alternate way of achieving a more consistent control is required. Figure 4.1: A Photograph of the Withering Trough Arrangement. 23 4.2 Behaviou r of Process Parameters Withering process has four process inputs of interest, 1. Air Flow: The air flow rate through the withering bed is a critical factor that determines the rate of withering, The rate of removal of moisture from the tea leaves varies on the level of moisture content in the leaves. In the initial stage of the process the leaves contains a high level of surface moisture and during that period, the rate of removal of moisture has a strong relationship with the air flow rate. The internal moisture is hard to remove and the rate of removal is low compared to the initial stage. So maintaining the same air flow rate is not necessary. The air velocity out of the bed through the tea leaves is difficult to measure as it is in the range of 0-0.5 m/s and needs averaging through a large area to get an accurate measurement. To overcome this problem air velocity is measured at the input to the chamber and air velocity at the exit through tea leaves is calculated using the input output areas. 24 Figure 4.2: Variation of Air Flow with Process Time. 2. Chamber Pressure: As the tea leaves get withered it increases the resistance to flow of air through the withering bed. This in turn increases the withering chamber pressure. As the pressure builds up, the air flow rate drops at the same fan speed and the operation point of the withering fan gets shifted away from the most efficient point of operation on its performance curve. 25 Figure 4.3: Variation of Chamber Pressure with Process Time. 3. Relative Humidity: The relative humidity level of the withering air flow is another factor that determines the rate of withering and maintaining a low relative humidity level can compensate for reduced air flow rate. 26 r 4. Process Air Temperature: Since the process air humidity is reduced by means of heating, the air temperature goes high and cannot be avoided. Temperature rises for shot durations on and off will not affect the quality of the tea badly but continuous over temperature will damage the tea leaves and results in poor quality processed tea. 27 Chapter Five Proposed Solution A fuzzy logic based approach is proposed for automated control of the process considering, • Highly non linear nature of the process, • Involvement of multiple variables, • Necessity to base the control on operator past experience, • Necessity for easy reconfiguration on field experiments for different climate conditions and physical trough characteristics depending on the factory location. 5.1 Methods and Techniques An averaging flow grid is proposed for obtaining the air flow rate input to the control system. The air flow grid consists of two tubes mounted diagonally across the trough and the tubes are drilled with a series of equally spaced holes. The holes in one tube face directly upstream and sense total pressure, while the pair of tubes on the second tube faces forward in an inclined angle so that it senses static pressure. The total and static pressure are averaged along the length of each tube and provide pressure signals out the trough. The pressure differentials across these connectors can be fed to a pressure transmitter with square rooting function which can give a 4-20mA electrical signal which is the input signal for the control system. M = ~ (5.1) Where, M = Flow grid magnification factor, AP = Flow grid differential pressure (Pa) PV = Chamber mean velocity pressure (Pa) 28 The velocity relationship is V = ^ ~ x P V (5.2) V = Mean air velocity (m/s) p = Density of air (kg/m3) Therefore the basic formula for the flow grid is rr 2 AP And allowing for changes in air density, V = J— x — xCF (5.4) p0 M ; Where, p0 = Standard density of air 1.2 kg/m3 r r p0 101350 T 101350 Lb = — = x x (5.5) p B 293 101350+ PS v ' B = Barometric pressure (Pa) T = absolute air stream temperature degrees K P.s = Chamber static pressure (Pa) To obtain the volume flow rate, Q = AxV (5.6) Q = Ax -x^-xCF (5.7) \Po M Where, Q =Volume flow rate 29 r j For Fuzzyfication of the input Air Flow, five membership functions are defined. The input universe is 0 to 30,000 cfm. The width of each membership function is selected to be around 10,000 cfm. The leftmost and rightmost membership functions are shouldered ramps and the three in the centre are symmetrical triangles of the same width. Overlap is selected to be around 50%. MF NAME NoFlow LowFlow StandardFlow HighFlow FullFlow BASE x (10J) 0-10 5-15 10-20 15-25 20-30 SHAPE Trapezoidal Triangular Triangular Triangular Trapezoidal POINTS 0,0,5,10 5,10,15 10,15,20 15,20,25 20,25,30,30 Table 5.1: Fuzzyfication of Input Air Flow NoFlow LowFlow StandardFlow HighFlow FullFlow 1 - 30 0) JD E a; E a> o.4 d) a> 0) Q 10 15 20 25 30 35 40 45 50 Motor Frequency (Hz) Figure 5.5: Fuzzyfication of Output Motor Frequency 34 the process air input. The degree of mixing is controlled by controlling the damper angle. For Fuzzyfication of the input hot air damper angle, five membership functions are defined. The input universe is 0 to 90 degrees. The width of each membership function is selected to be around 30 degrees. The leftmost and rightmost membership functions are shouldered ramps and the three in the centre are symmetrical triangles of the same width with an overlap of around 50%. MF NAME Close LittleOpen HalfOpen Open FullOpen BASE 0-30 15-45 30-60 45-75 60-90 SHAPE Trapezoidal Triangular Triangular Triangular Trapezoidal POINTS 0,0,15,30 15,30,45 30,45,60 45,60,75 60,75,90,90 Table 5.6: Fuzzyfication of Output Damper Angle LittleOpen HalfOpen Open FullOpen 0) -Q £ E * Chapter Eight Conclusions 8.1 Conclusions, Remarks and Discussion The use of fuzzy logic in withering control needs much practical experimentation with the basic structure in place, so that the membership functions and the rule base can be modified based on trial and error so that maximum energy efficiency and quality of the processed tea is ensured. This study provides the basis for such industrial experiments. This study shows that a considerable amount of energy is wasted in conventional tea withering process by the way of excessive air flow rates and also due to inefficient operation of the fan motor. The proposed fuzzy logic based system provides solutions for both these issues. The experimental and simulation results shows that with proper utilization of low humid air by controlling the damper angle, withering fan motor speed can be reduced without affecting the rate of withering. Also this study shows that chamber pressure build-up can be used as an indicator for inefficient operation of the withering fan and energy wastage due to this inefficient operation of the withering fan can be reduced by means of improved relative humidity levels of the process air for compensation of a motor speed reduction. The nature of the process does not require high accurate control and also does not require very fast reaction to system changes. The proposed fuzzy logic based control system well suites these process characteristics and can effectively integrate the operator experience to an automated withering control system for more consistent energy efficient and quality process. 55 8.2 Considerations for Future Research In a broader perspective for future advancements in the tea processing industry, the withering process should be looked at as a hole and new conceptualisation is required to overcome the inherent draw backs of the present system that was developed hundreds of years ago to suit the environmental, commercial and technical resources available at that period of time. For example, the present open withering system releases the used air which is very high in moisture content, to the process environment and it poses two problems. The fans sucks in this high moisture content air through the ambient air stream and mixes with dry air out from the dryer room and blows through the withering bed. The resulted air is high in moisture content and as a result withering becomes slow and energy inefficient. The high moisture content air out from one trough can deposit on already withered tea leaf on adjacent troughs as the withering troughs are located close by and also the withering times are also staggered among the individual troughs to facilitate distributed plucking and collection times. To solve these issues, a mechanism which can guide the used air out of the process environment has to be developed. Another concern related to the ambient air stream is that during different weather conditions and climates the humidity level of the ambient air stream can be very high and therefore improvisation of some method to source the ambient air stream through a processing chamber where you can reduce the .humidity level to a certain extent by the way of utilisation of the refrigeration cycle or any other method, it will improve the process. 56 f r % ^ iri • x References [1] Dias S.G., Energy management in tea Processing, Management Frontiers, 2005. [2] Rudramoorthy R., Kumar C.P.S, Velavan R. and Sivasubramaniam S, Innovative Measures for Energy Management in Tea Industry, 42nd National Convention of Indian Institute of Industrial Engineering, 29-30, India, pp. 163-167, 2000. [3] Das S.K., Further Increasing the Capacity of Tea Leaf Withering Troughs, Agricultural Engineering International: the CIGR E-journal. Vol. VIII. January, 2006. [4] Osofisan P. B., Optimization of the Fermentation Process in a Brewery with a Fuzzy Logic Controller, LJS, AcademicDirect, 2007. [5] Improving Fan System Performance, United States Department of Energy, 1989. [6] Energy and Environment-Tea Sector: Small and Medium scale Industries in Asia, School of Environment, Resources and Development, Asian Institute of Technology, Thailand, 2002. [7] Maxwell, J. B., How to Avoid Overestimating Variable Speed Drive Savings, Energy Systems Laboratory, Texas A&M University, 2005. [8] Annual Report, Central Bank of Sri Lanka, 2005. [9] Machin J., The study of evaporation from small surfaces by the direct measurement of water vapour pressure gradients, J. Exp. Biol., 1970. [10] Kovacic Z. and Bogdan S., Fuzzy controller design, theory and applications, Taylor and Francis, 2006. [11] Kandel A. and Langholz G., Fuzzy control systems, CRC Press, 1993. [12] Reznik L., Fuzzy Controllers Handbook, How to Design Them, How They Work, Butterworth-Heinemann, 1997. [13] Buckley J.J., Simulating Fuzzy systems, Springer, 2005. [14] Martin F., McNeill and Thro E., Fuzzy Logic, A practical approach, AP Professional, 1994. [15] Harris J., Fuzzy logic applications in engineering science, Springer, 2006. [16] Passino K. M. and Yurkovich S., Fuzzy control, Addison-Wesley, 1998. 57 [17] Wang L., A course in fuzzy systems and control, Prentice Hall, 1997 [18] Desa D.O.J, Applied technology and instrumentation for process control, Taylor and Francis, 2005 [19] Lee K.H., First course on fozzy theory and applicatrons, Springer 2005 [20] Shinskey F.G., Process control systems, applications, design, adjustment, McGraw- Hill, 1988. [21] Knight A., Basics of Matlab and beyond, CRC Press, 2000 [22] Sivanandam S.N., Sumathi S. and Deepa S.N., Introduction to fozzy logic using Matlab, Springer, 2007. [23] Codrons B., Process modeling for control- A , m i f i « i a- , • black-box techniques, Springer 200^ U S ' " g S t a n d a r d 58 59 Ti me An em om ete r r ead ing (fi t/m ) Ma no me ter R ead ing Pi V, v 2 v 3 v 4 v 5 v 6 v 7 v 8 V 9 V T V s 12 :30 22 41 21 07 19 09 19 77 20 48 18 91 23 26 23 45 22 46 0.2 7 0.2 3 13 :00 21 13 194 1 18 19 19 33 20 05 18 42 21 24 22 05 20 64 0.3 0 0.2 7 13 :30 19 70 18 72 18 70 18 45 19 00 17 06 21 22 21 71 20 58 0.3 1 0.3 4 14 :00 159 1 18 42 17 34 16 27 18 08 17 99 19 98 20 17 21 24 0.4 0 0.3 5 14 :30 18 16 17 48 17 05 16 39 18 15 17 87 19 26 20 14 20 34 0.4 0 0.3 7 15 :00 17 08 16 74 18 37 17 12 17 70 16 81 18 94 21 07 20 08 0.4 2 0.3 7 15 :30 17 50 18 51 17 70 15 43 17 76 162 1 19 28 19 87 199 1 0.4 4 0.4 0 16 :00 18 23 18 30 17 99 17 95 17 32 17 28 19 32 20 01 20 51 0.4 5 0.4 2. 16 :30 18 81 18 54 18 22 16 39 16 85 16 95 18 10 20 19 19 18 0.4 4 0.4 4 17 :00 14 91 13 43 15 29 15 95 158 7 16 02 16 78 19 44 18 86 0.4 4 0.4 4 17 :30 18 22 16 42 16 37 16 38 16 64 15 86 16 79 19 02 18 54 0.4 6 0.4 6 18 :00 17 72 128 3 179 5 14 60 15 59 15 89 17 92 18 78 18 59 0.4 7 0.4 7 18 :30 16 44 16 24 16 06 17 00 16 55 14 20 16 66 18 80 18 76 0.4 8 0.4 8 19 :00 172 1 15 76 15 79 16 51 16 71 15 18 18 13 18 70 18 38 0.4 8 0.4 8 19 :30 163 3 13 44 15 87 14 69 16 95 17 48 16 13 191 3 18 32 0.5 0 0.4 8 20 :00 17 38 17 91 16 62 16 84 17 72 16 86 19 50 19 89 19 42 0.4 8 0.4 7 20 :30 18 17 18 03 . 17 86 15 87 16 47 17 17 18 34 196 1 19 62 0.4 8 0.4 8 21 :00 17 42 15 84 17 98 17 55 15 83 17 57 16 57 18 77 18 66 0.4 7 0.4 6 22 :00 18 64 18 11 17 29 16 88 166 5 17 42 18 30 19 52 20 60 0.4 7 0.4 5 22 :30 20 70 17 07 21 57 18 69 19 95 18 35 20 42 21 96 22 24 0.2 9 0.2 9 23 :00 19 68 18 09 20 19 18 49 18 50 195 7 20 68 22 11 22 98 0.3 1 0.3 0 23 :30 17 05 16 20 17 60 17 50 17 95 191 1 20 34 21 42 20 06 0.3 5 0.3 2 0:0 0 19 43 18 17 19 45 17 21 18 39 18 38 19 25 20 61 20 50 0.3 7 0.3 4 0:3 0 18 37 20 01 19 13 17 96 17 83 18 64 20 08 21 44 21 77 0.3 7 0.3 4 1:0 0 20 29 19 83 20 20 14 99 17 79 17 33 19 23 21 60 20 98 0.3 7 0.3 4 1:3 0 17 17 15 29 18 23 19 11 17 55 18 05 18 88 21 26 19 92 0.3 5 0.3 5 2:0 0 19 01 18 18 17 45 17 22 171 3 17 74 17 11 20 59 19 59 0.3 6 0.3 2 2:3 0 18 85 16 30 18 90 16 28 174 5 17 08 19 56 20 42 19 16 0.3 7 0.3 4 3:0 0 157 1 14 16 16 24 14 46 15 17 16 09 15 92 181 1 17 49 0.3 5 0.3 4 Ta ble A .l: Fie ld Ex pe rim en t D ata 60 Calculation of withering bed air velocity from field data Vintake ' VI + V2 + V3 + V4 + V5 + V6 + VI + V8 + V9 ( f t / s 9 { / m i n y 0— y x A int ake intake intake Q intake- v 3 8 3 4 V , t x — x make ^ ^ (ft3 / • I { / mm F y bed Qbed Abed 38 34 25.4x25.4x25.4 i m 3 = int ake X — X — X X 60(™ . 12 12 1000x1000x1000 But Qimake =Qhed(ut same pressure) . y — ^'nt ake _ Qint ake (ftl/ ^ " bed~ Abed ~ 33.4 V s > 61 Process time (minutes) Qintake (CFM) 0 19026 30 17986 60 17456 90 16485 120 16429 150 16336 180 16163 210 16635 240 16269 270 14606 300 15373 330 14937 360 15021 390 15186 420 14785 450 16160 480 16060 510 15567 570 16287 600 18035 630 17969 660 16667 690 17082 720 17465 750 17167 780 16491 810 16347 840 16345 870 14287 Table A.2: Air velocity through Withering Bed. 62 Process time (minutes) Qintake ( C F M ) 0 19026 30 17986 60 17456 90 16485 120 16429 150 16336 180 16163 210 16635 240 16269 270 14606 300 15373 330 14937 360 15021 390 15186 420 14785 450 16160 480 16060 510 15567 570 16287 600 18035 630 17969 660 16667 690 17082 720 17465 750 17167 780 16491 810 16347 840 16345 870 14287 Table A.2: Air velocity through Withering Bed. 62 Chamber pressure calculations from field data Chamber pressure = meter reading x meter constant (0.2kPa) Process Time (Minutes) Total Pressure (Pa) StaticPressure (Pa) 0 54 46 30 60 54 60 62 68 90 80 70 120 80 74 150 84 74 180 88 80 210 90 84 240 88 88 270 88 88 300 92 92 330 94 94 360 96 96 390 96 96 420 100 96 450 96 94 480 96 96 510 94 92 570 94 90 600 58 58 630 62 60 660 70 64 690 74 68 720 74 68 750 74 68 780 70 70 810 72 64 840 74 68 870 70 68 Table A3: Withering Chamber Pressure Variation during the Process 63 Motor power consumption variation during the process Process Time (Minutes) Motor Power (W) 0 1480 30 1948 60 1958 90 2021 120 2002 150 2018 180 2032 210 2027 240 2021 270 2012 300 2020 330 2030 360 2031 390 2048 420 2039 450 2047 480 2045 510 2032 570 2037 600 1959 630 1999 660 1987 690 1974 720 1995 « 750 1992 780 1993 810 2006 840 1985 870 1995 Table A.4: Motor Power Consumption with Time (At Constant Frequency of 36Hz) Motor Power Vs Process Time Figure A. 1: Motor Power Consumption with Time (At Constant Frequency of 36Hz) 65 1.95 1.9 1.85 1.8 1.75 O S 1.7 o LL. . < 1.65 1.6 1.55 1.5 x 10 1.45 40 50 Air Flow Vs Static Pressure i 60 * * * * * _L _L 70 80 Static Pressure (Pa) 90 Figure A.2: Air Flow Vs Chamber Pressure