PRACTICAL MODELLING AND CONTROL IMPLEMENTATION STUDIES ON A pH NEUTRALIZATION PROCESS PILOT PLANT A thesis submitted for the degree of Doctor of Philosophy By Rosdiazli Ibrahim MSc (Automation and Control) BEng (Electronic and Computer) Department of Electronics and Electrical Engineering Faculty of Engineering University of Glasgow March 2008 © Rosdiazli Ibrahim March 2008 To My Beloved Wife, Nurlidia Mansor And My Lovely Princesses Nur Azra Adli Nur Auni Adli Nur Ahna Adli ii ABSTRACT In recent years the industrial application of advanced control techniques for the process industries has become more demanding, mainly due to the increasing complexity of the processes themselves as well as to enhanced requirements in terms of product quality and environmental factors Therefore the process industries require more reliable, accurate, robust, efficient and flexible control systems for the operation of process plant In order to fulfil the above requirements there is a continuing need for research on improved forms of control There is also a need, for a variety of purposes including control system design, for improved process models to represent the types of plant commonly used in industry Advanced technology has had a significant impact on industrial control engineering The new trend in terms of advanced control technology is increasingly towards the use of a control approach known as an “intelligent” control strategy Intelligent control can be described as a control approach or solution that tries to imitate important characteristics of the human way of thinking, especially in terms of decision making processes and uncertainty It is also a term that is commonly used to describe most forms of control systems that are based on artificial neural networks or fuzzy logic The first aspect of the research described in the thesis concerns the development of a mathematical model of a specific chemical process, a pH neutralization process It was intended that this model would then provide an opportunity for the development, implementation, testing and evaluation of an advanced form of controller It was also intended that this controller should be consistent in form with the generally accepted definition of an “intelligent” controller The research has been based entirely around a specific pH neutralization process pilot plant installed at the University Teknologi Petronas, in Malaysia The main feature of interest in this pilot plant is that it was built using instrumentation and actuators that are currently used in the process industries The dynamic model of the pilot plant has been compared in detail with the results of experiments on the plant itself and the model has been assessed in terms of its suitability for the intended control system design application iii The second stage of this research concerns the implementation and testing of advanced forms of controller on the pH neutralization pilot plant The research was also concerned with the feasibility of using a feedback/feedforward control structure for the pH neutralization process application Thus the study has utilised this control scheme as a backbone of the overall control structure The main advantage of this structure is that it provides two important control actions, with the feedback control scheme reacting to unmeasured disturbances and the feedforward control scheme reacting immediately to any measured disturbance and set-point changes A nonmodel-based form of controller algorithm involving fuzzy logic has been developed within the context of this combined feedforward and feedback control structure The fuzzy logic controller with the feedback/feedforward control approach was implemented and a wide range of tests and experiments were carried out successfully on the pilot plant with this type of controller installed Results from this feedback/feedforward control structure are extremely encouraging and the controlled responses of the plant with the fuzzy logic controller show interesting characteristics Results obtained from tests of these closed-loop system configurations involving the real pilot plant are broadly similar to results found using computer-based simulation Due to limitations in terms of access to the pilot plant the investigation of the feedback/feedforward control scheme with other type of controllers such as Proportional plus Integral (PI) controller could not be implemented However, extensive computer-based simulation work was carried out using the same control scheme with PI controller and the control performances are also encouraging The emphasis on implementation of advanced forms of control with a feedback/feedforward control scheme and the use of the pilot plant in these investigations are important aspects of the work and it is hoped that the favourable outcome of this research activity may contribute in some way to reducing the gap between theory and practice in the process control field iv ACKNOWLEDGEMENT I would like to express my deepest appreciation to my supervisor, Professor David J Murray-Smith for his admirable way of supervising the work, invaluable guidance, assistance and support throughout this research My special gratitude goes to my sponsor, Universiti Teknologi Petronas, Malaysia for giving me the opportunity and the scho larship for my studies I would also wish to extend my thanks to Universiti Teknologi Petronas for allowing access to the pilot plant facilities for experimental investigations and the financial support in carrying out this research, especially the investment on the new system I would also want to acknowledge the funding provided by the Department of Electronics and Electrical Engineering, University of Glasgow, in support of conference attendance and aspects of the experimental work carried out at Universiti Teknologi Petronas An extended acknowledgment to Azhar Zainal Abidin for his assistance during my experimental work at the laboratory and also to PCA Automation for their technical support during installation of the new system My special thanks go to my beloved parents for their endless encouragement and prayers throughout the educational years of my life To my wife and my lovely daughters, thank you very much for all their patience, understanding and priceless sacrifices Last but not least, 'Terima Kasih' to all my fellow friends and colleagues for their continuous encouragement especially to my badminton mates and Glasgow University's Badminton Club for providing a stress release session every week v TABLE OF CONTENTS 1.0 INTRODUCTION 1.1 Research Overview 1.1.1 Problem Identification 1.1.2 Research Objectives 1.1.3 Significance of the Research 1.2 2.0 BACKGROUND AND LITERATURE REVIEW 10 2.1 pH Process Characteristics 10 2.2 pH Control Techniques 14 2.3 3.0 Overview of the Thesis 2.2.1 Significance of pH control 14 2.2.2 Overview of pH control 15 2.2.3 The Conventional Approach 26 2.2.4 Fuzzy Logic Control 26 Summary and Research Motivation 34 THE pH NEUTRALIZATION PILOT PLANT 37 3.1 Overall System Architecture 39 3.2 The Reactor Tank 41 3.3 Instrumentation and Measurements Involved 43 3.3.1 pH Meters 44 3.3.2 Conductivity Meters 45 3.3.3 Flowmeters 46 3.3.4 Control Valves 47 3.4 Data Acquisition System 53 3.5 Practical Issues Associated with the Pilot Plant 56 vi 4.0 5.0 MODELLING AND SIMULATION OF THE pH NEUTRALIZATION PROCESS PILOT PLANT 59 4.1 Overview of the pH Neutralization Process Modelling 61 4.2 Preliminary Development of the Mathematical Model 65 4.3 Experimental Results from the Enhanced Data Acquisition System 70 4.4 Empirical Modelling for Development of the Modified pH Model 4.4.1 Investigation of the values of the dissociation constants 77 4.4.2 Evaluation of the Modified Model 80 DEVELOPMENT OF A CONVENTIONAL PROPORTIONAL PLUS INTEGRAL (PI) CONTROLLER FOR THE PILOT PLANT 88 5.1 Overview of the PID Controller 89 5.2 Simulation work on the PI form of Controller 92 5.3 6.0 77 5.2.1 Practical implementation of the PI controller 93 5.2.2 Experimental and Simulation Results – Set-Point Tracking 97 Summary 104 ADVANCED CONTROLLER DESIGN, DEVELOPMENT, IMPLEMENTATION AND TESTING 109 6.1 Choice of Control System Structure 110 6.2 Development and Implementation of the Fuzzy Inference System 114 6.3 6.2.1 Fuzzy Inference System for the Flow Controller 115 6.2.2 Fuzzy Inference System for the pH Controller 124 Simulation and Experimental Results 6.3.1 Experimental Results from the pH Neutralization Pilot Plant 131 6.3.2 Computer-based Simulation Results for the Fuzzy Logic Controller 145 6.3.3 Computer-based simulation of the feedforward/feedback control strategy using PI controllers 6.4 130 Summary 158 164 vii 7.0 CONCLUSIONS AND RECOMMENDATIONS 167 7.1 167 Research Project Conclusions 7.1.1 The pH neutralization process model 168 7.1.2 The implementation of the feedback/feedforward control scheme with the advanced controller 171 7.2 Summary of the Main Contributions 173 7.3 Recommendations for Future Research 174 8.0 REFERENCES 177 9.0 LIST APPENDICES 188 viii LIST OF FIGURES Figure 2.1: Typ ical titration curves for monoprotic acid (left) and polyprotic acid (right) 13 Figure 2.2: Membership function of a classical set 29 Figure 2.3: Membership function of a fuzzy set 29 Figure 2.4: Typical membership function for fuzzy logic systems 30 Figure 2.5: General procedures of designing a fuzzy system 32 Figure 3.1: Piping and Instrumentation Diagram (P&ID) of the pilot plant 37 Figure 3.2: Photograph of the pH neutralization pilot plant 38 Figure 3.3: Overall system architecture of the pilot plant showing the three functional levels 39 Figure 3.4: The reactor tank 41 Figure 3.5: Photograph of the reactor tank at the pilot plant 42 Figure 3.6: Photographs of the magnetic flowmeters 47 Figure 3.7: Typical characteristic of a control valve 48 Figure 3.8: Photograph of the control valves 49 Figure 3.9: Control valve characteristics 50 Figure 3.10: Photograph of the new data acquisition system 54 Figure 4:1: The flowchart of the modelling process 60 Figure 4:2: A schematic diagram for the pH neutralization process 65 Figure 4:3: MATLAB/Simulink blocks of the pH neutralization on process model 69 Figure 4:4: Experimental results obtained using the enhanced data acquisition system during a test involving a step change of the flow rate for the alkaline stream 71 Figure 4:5: The dynamic response from the neutralization pilot plant for square-wave variation of alkaline flowrate with constant flowrate of acid solution 73 Figure 4:6: Dynamic response – simulation of Experiment 75 Figure 4:7: Dynamic response – Simulation of Experiment 76 Figure 4:8: MATLAB/Simulink representation of the modified pH model 77 Figure 4:9: Dynamic response from the modified pH model – Experiment 79 Figure 4:10: Dynamic response from the modified pH model – Experiment 80 Figure 4:11: Dynamic responses of the model for the original and modified configurations 82 Figure 4:12: Distribution of error 83 ix Figure 5:1: MATLAB/Simulink representation for the PI controller 91 Figure 5:2: MATLAB/Simulink representation of the pilot plant for the modified model, with a PI controller 93 Figure 5:3: PID tuning (Experiment 1) 95 Figure 5:4: PID tuning (Experiment 2) 96 Figure 5:5: PI controller performance 98 Figure 5:6: Responses obtained from the system with the PI controller tuned for an operating point involving a pH set value of 99 Figure 5:7: Simulation results of the modified pH model with PI controller 101 Figure 5:8: Comparison between calculated and implemented tuning parameters 102 Figure 5:9: Further computer based investigation of tuning parameters 103 Figure 5:10: The transient performance measures 105 Figure 6:1: An overview of the controller structure proposed for the pilot plant 111 Figure 6:2: Control valve characteristics 116 Figure 6:3: Simplifed MATLAB/Simulink model representation for the fuzzy logic flow controller 117 Figure 6:4: Membership function for input set 119 Figure 6:5: Membership function for the output set 121 Figure 6:6: The response of the fuzzy logic controller in terms of the manipulated variable as a function of the error 124 Figure 6:7: MATLAB/Simulink representation for the overall pH controller 125 Figure 6:8: Membership function for the input set for the pH fuzzy logic controllers 126 Figure 6:9: Membership function for outputs set for pH fuzzy logic controller 128 Figure 6:10: The response of the pH fuzzy logic controller 130 Figure 6:11: The step response experiment for changes of the pH set point 132 Figure 6:12: Additional response from the set point experiment 135 Figure 6:13: Set point tracking test results 137 Figure 6:14: Responses obtained from a load disturbance experiment 139 Figure 6:15: Responses obtained from the concentration disturbance experiment 142 Figure 6:16: Responses from the experiment involving large changes of set point 144 Figure 6:17: Simulation of the set point change experiment 146 Figure 6:18: The new structure of the controller 147 Figure 6:19: Membership function for the additional input set 148 Figure 6:20: Membership function for the additional output set 148 Figure 6:21: Simulation of set point change experiment with modified fuzzy logic pH controller 152 x REFERENCE Wright, R A., Smith, B E., & Kravaris, C 1998, "On-Line identification and nonlinear control of pH processes", Industrial and Engineering Chemistry Research, vol 37, no 6, pp 2446-2461 Wright, R A., Soroush, M., & Kravaris, C 1991, "Strong acid equivalent control of pH processes: An experimental study", Industrial and Engineering Chemistry Research, vol 30, no 11, pp 2437-2444 Wright, R A & Kravaris, C 1991, "Nonlinear control of pH processes using the strong acid equivalent", Industrial and Engineering Chemistry Research, vol 30, no 7, pp 1561-1572 Wright, R A & Kravaris, C 2001a, "On- line identification and nonlinear control of an industrial pH process", Journal of Process 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"Fuzzy sets", Information and control, vol 8, no 3, pp 338353 Zadeh, L A 1965a, "Fuzzy sets", Information and control, vol 8, no 3, pp 338353 Zadeh, L A 1968, "Probability Measures of Fuzzy Events", Journal of Mathematical Analysis and Applications, vol 10, pp 421-427 Zadeh, L A 1971, "Similarity Relations and Fuzzy Orderings", Information Sciences, vol 3, pp 177-200 Zadeh, L A 1972, "A fuzzy-Set-Theoretic Interpretation of Linguistic Hedges", Journal of Cybernetics, vol 2, no 2, pp 4-34 Zadeh, L A 1973, "Outline of a New Approach to the Analysis of Complex Systems and Decision Processes", IEEE Transactions on System, Man, and Cybernetics, SMC - pp 28-44 Zadeh, L A 1975a, "The concept of a linguistic variable and its application to approximate reasoning I", Information Sciences, vol 8, no 3, pp 199-249 Zadeh, L A 1975b, "The concept of a linguistic variable and its application to approximate reasoning II", Information Sciences, vol 8, no 4, pp 301-357 Zadeh, L A 1975c, "The concept of a linguistic variable and its application to approximate reasoning-III", Information Sciences, vol 9, no 1, pp 43-80 Zadeh, L A 1976a, "A Fuzzy- Algorithmic Approach to the Definition of Complex and Imprecise Concepts", International Journal of Man-Machine Studies, vol 8, pp 249-291 Zadeh, L A 1976b, "A Theory of Approximate Reasoning", Machine Intelligence, vol 9, pp 149-194 Zadeh, L A 1976c, "A Theory of Approximate Reasoning, edited by J Hayes, D Michie, and L.I Mikulich", Machine Intelligence, vol 9, pp 149-194 185 REFERENCE Zadeh, L A 1976d, "The concept of a linguistic variable and its application to approximate reasoning-III", Information Sciences, vol 9, no 1, pp 43-80 Zadeh, L A 1978a, "Fuzzy sets as a basis for a theory of possibility", Fuzzy Sets and Systems, vol 1, no 1, pp 3-28 Zadeh, L A 1978b, "PRUF-A Meaning Representation Language for Natural Languages", International Journal of Man-Machine Studies, vol 10, pp 395-460 Zadeh, L A 1983a, "A Computational Approach to Fuzzy Quantifiers in Natural Language", Computer and Mathematics with Applications, vol 9, pp 149-184 Zadeh, L A 1983b, "The Role of Fuzzy Logic in Management of Uncertainty in Expert Systems", Fuzzy Sets and Systems, vol 11, pp 199-227 Zadeh, L A 1984, "A Theory of Commensense Knowledge In: Aspect of Vagueness, edited by H.J Skala, S Termini, and E Thrillas,", Dordrecht, Holland pp 257-296 Zadeh, L A 1985, "Syllogistic Reasoning in Fuzzy Logic and its Application to Usuality and Reasoning with Dispositions", IEEE Transactions on System, Man, and Cybernetics, SMC - 15 pp 754-763 Zadeh, L A 1986, "Test-Score Sematics as a Basic for a Computioanl Approach to the Representation of Meaning", Literary and Linguistic Computing, vol 1, pp 2435 Zadeh, L A & Bellman, R E 1970, "Decision-Making in a Fuzzy Environment", Management Science, vol 17, no 4, pp 141-164 186 APPENDICES 9.0 LIST APPENDICES 188 Appendix I: Recalibration Results 189 Appendix II: Technical specification of the pH meter 191 Appendix III: Technical specification of the conductivity meter 193 Appendix IV: List of I/O of the system and pin assignment for the I/O cards 195 Appendix V: Layout of user interface for experimental work 197 187 LIST OF APPENDIXES 9.0 LIST APPENDICES Appendix I: Recalibration Results Appendix II: Technical specification of the pH meter Appendix III: Technical specification of the conductivity meter Appendix IV: List of I/O of the system and pin assignment for the I/O cards Appendix V: Layout of user interface for experimental work 188 APPENDIX I: RECALIBRATION RESULTS Appendix I: Recalibration Results Calibartion Results for Conductivity Meter (Acid Tank) 50 Before calibration Reading from portable meter After calibration 45 40 Conductivity (mS) 35 30 25 20 15 10 0 0.02 0.04 0.06 0.08 Concentration (Molarity) 0.1 0.12 Calibration Results for Conductivity Meter (Alkaline Tank) 25 Before calibration Reading from portable meter After calibration Conductivity (mS) 20 15 10 0 0.02 0.04 0.06 Concentration (M) 0.08 0.1 0.12 189 APPENDIX I: RECALIBRATION RESULTS Calibration Results for pH Meter at Reactor Tank 10 Before Calibration After Calibration Reading from the pH meter (pH Value) 3 Buffer solutions (pH Value) 10 190 APPENDIX II: TECHNICAL SPECIFICATION OF THE pH METER Appendix II: Technical specification of the pH meter • Controller Model : alpha-pH1000 1/4 DIN pH/ORP Controller Product Features :: :: :: :: :: :: :: :: :: :: :: :: :: :: :: :: Built-In Programmable Limit, Proportional (Pulse Length or Pulse Frequency) - ideal for precision process control applications User-Customization through Advanced Setup Menu offers flexibility in matching the controller's functions to suit individual's specific requirement Automatic Calibration with Auto-Buffer Recognition e liminates mistakes during calibration Symmetrical Mode Operation eliminates electronic noise problems when used with solution ground One-Point Online Calibration without shutting down the line Hold Relay for use with float switches/flow swit ches and other controllers as a failsafe function Two Level Password Protection prevents un authorized tampering with settings to 2000 Second Time Delay Adjustment on control and alarm delays Two Galvanically Isolated Scaleable 0-20/4-20 mA Outputs for pH/ORP Wash Contact Relay controls electrodes cleaning systems at desired duration and frequency Choice of Glass or Antimony Electrode for general purpose or hydrofluoric acid applications Adjustable Hysteresis (Dead Band) prevents rapid contact switching near set point Non-Volatile Memory retains all stored parameters and calibration data even if power fails Large Dual Display shows pH (or ORP) with temperature simultaneously - features clear multiple icons, set points, and status messages Choice of Temperature Sensor Pt100/Pt1000 with 2-wire or 3-wire temperature input selection Easy Installation and Wiring with detachable plug-in connectors Applications General: Useful for any batch or on-line type application that requires accurate pH or ORP control Water Purification/Treatment: Use for batch and on-line control of incoming process water, rinse water treatment, recirculating system and waste water treatment Industrial: Ideal for chemical processing, food processing aquarium, pharmaceutical, hydroponics and waste control industries Regulatory: Hook to recorder to document data for regulatory compliance 191 APPENDIX II: TECHNICAL SPECIFICATION OF THE pH METER • pH Process Electrode Model: EC100GTSO-05B Specifications Product Specification Description pH Range to 14 Reference Annular Teflon, double junction Reference electrolyte Saturated KCl, polymerized gel Operating temperature to 80 °C Pressure tolerance bars Temperature sensor Pt 100 Potential matching pin Platinum Material PPS (Ryton) Thread 3/4” NPT Cable Integral 5m low-noise semi-conductor screened Connector BNC Dimensions: (excludes cable) Length 151 mm Diameter (external) 26 mm Weight 650 g 192 APPENDIX III: TECHNICAL SPECIFICATION OF THE CONDUCTIVITY METER Appendix III: Technical specification of the conductivity meter • Controller Model : alpha-CON1000 1/4 DIN Conductivity Controller Product Features :: :: :: :: :: :: :: :: :: :: :: :: :: :: Ten Selectable Conductivity Measurement Ranges in one controller via its IP54 front panel Highlevel accuracy of ±1% of full scale can be obtained with appropriate cells and correct temperature coefficient User-Customization through Advanced Setup Menu offers flexibility in matching the controller's functions to suit individual's specific requirement Choice of Cell Constant (0.01, 0.1, 1.0, 10.0) for accurate control in any solution Hold Relay for use with float switches/flow switches and other controllers as a failsafe function Two Level Password Protection prevents un authorized tampering with settings to 2000 Second Time Delay Adjustment on control and alarm delays Two Galvanically Isolated Scalebale 0-20/4-20 mA Outputs Wash Contact Relay controls electrodes cleaning systems at desired duration and frequency Adjustable Hysteresis (Dead Band) prevents rapid contact switching near set point Non-Volatile Memory retains all stored parameters and calibration data even if power fails Line Resistance Compensation against intrinsic cable resistance for longer cable connection Large Dual Display shows measurement with temperature simultaneously - features clear multiple icons, set points, and status messages Choice of Temperature Sensor Pt100/Pt1000 with 2-wire or 3-wire temperature input selection Easy Installation and Wiring with detachable plug-in connectors Applications General: Use for virtually any batch or online applications where rapid, accurate control Great for OEM/syste m integrator Industrial: Use in applications involving agriculture, chemical processing, boiler and water heaters, waferfab, microprocessor manufacturing, pharmaceuticals, pulp and paper industries, and bleach manufacturing Water Purification/Treatment: Use to treat batches of incoming process water, ultrapure water, boiler and feed water control Regulatory: Hook to recorder to document data for regulatory compliance 193 APPENDIX III: TECHNICAL SPECIFICATION OF THE CONDUCTIVITY METER • Conductivity Process Electrode Model : EC91346S Specifications Product Specification Description Conductivity range Up to 500 mS/cm Cell constant, k 0.3, 4-Cell Temperature sensor Pt 100, 3-wire Pressure rating bar Material Ryton, SS 316 Thread 3/4” NPT Cable Integrated 7.6m, 8-wire double-shielded, open Dimensions: (excludes cable) Length 150.5 mm Diameter (external) 22.2 mm Weight 650 g 194 APPENDIX IV: LIST OF I/O OF THE SYSTEM AND PIN ASSIGNMENT FOR I/O CARDS Appendix IV: List of I/O of the system and pin assignment for the I/O cards Digital Input Card Card NoPin Name Assignment Card A-1 LS100 Card A-2 Card B-3 Card A-4 Card A-5 Card A-6 Card A-7 Card A-8 Card B-1 Card B-2 Card B-3 Card B-4 Card B-5 Card B-6 LS110 P100-Run P100-Trip P110-Run P110-Trip AG120 AG120 DCS/XPC Card B-7 Card B-8 Digital Output Card Pin Name Assignment P110 P100 AG120 AG130 AG140 MM32-Diamond Digital Input Description Limit switch for overflow indication – Reactor Tank Limit switch for overflow indication – Discharged Tank Unused Indication for pump status for acid stream –RUN Indication for pump status for acid stream –STOP Indication for pump status for alkaline stream –RUN Indication for pump status for alkaline stream –STOP Indication for agitator at reactor tank –RUN Indication for agitator at reactor tank –RUN Unused Unused Unused Unused Indication for selector switch for DCS – Plantscape Honeywell System or New Data Acquisition system (MATLAB) Unused Unused MM32-Diamond Digital Output Description Pump activation for acid stream Pump activation - alkaline stream Agitator activation – Reactor Tank Agitator activation – Cascaded Tank Agitator activation – Discharged Tank Unused Unused Unused 195 APPENDIX IV: LIST OF I/O OF THE SYSTEM AND PIN ASSIGNMENT FOR I/O CARDS Analogue Input Card Card NoPin Name Assignment CT100 FT120 MM32-Diamond Analogue Input Description CT110 10 11 12 13 14 15 16 FT121 Measured value from conductivity meter – Acid Tank Measured value from flowmeter – Acid stream Measured value for conductivity meter – Alkaline Tank Measured value from flowmeter – Alkaline stream AT122 AT130 1T140 Unused Unused Measured value from pH meter – Reactor Tank Measured value from pH meter – Cascaded Tank Measured value from pH meter – Dischanged Tank Analogue Output Card Pin Name Assignment FCV120 FCV121 ACV130 Unused Unused Unused Unused Unused Unused Unused MM32-Diamond AnalogueOutput Description Control valve for acid stream Control valve for alkaline stream Control valve for product Unused 196 LS110 DCS-XPC AG120-Trip FT121 CT110 FT120 CT100 Analog Input Card MM-32 AT131 Diamond AT141 Analog Input AT122 AT130 AT140 Digital Input Card (Port B) MM-32 Diamond Digital Input Digital Input Card (Port A) P100-Run MM-32 Diamond P100-Trip Digital Input P110-Run P110-Trip AG120-Run LS100 Scalling Conductivity Flowrate pH Meters Flowrate H2SO4 pH - AT122 FCV120 MV auto = man = Out1 Out5 auto = manl = 0 auto = man = 0 SP H2SO4 (0-100l/h) 50 SPpH (6-10) MVpH MVFlowAcid manual PI Controller SPAcid AG140 errorFlowAcid Auto/Man1 MVacid PVAcid MValkaline Auto/Man SPpH PVpH AG130 AG120 (OFF = / ON = 1) P100 P110 The pH Neutralisation Pilot Plant - PI Controller Record Out6 Analog Output Flowrate NaOH Flowrate H2SO4 Digital Output ExtraPoint (unused) Discharged Tank Cascaded Tank Reactor Tank Alkaline Pump Acid Pump Out4 APPENDIX V: LAYOUT OF USER INTERFACE FOR EXPERIMENT WORK Appendix V: Layout of user interface for experimental work 197 LS110 LS100 DCS-XPC CT110 FT120 CT100 Analog Input Card FT121 MM-32 AT131 Diamond AT141 Analog Input AT122 AT130 AT140 Digital Input Card (Port B) MM-32 Diamond Digital Input AG120-Trip Digital Input Card (Port A) P100-Run MM-32 Diamond P100-Trip Digital Input P110-Run P110-Trip AG120-Run Scalling set Conductivity Flowrate pH Meters Flowrate H2SO4 pH - AT122 Flowrate NaOH 60 Manual Manual FCV121 auto = man = auto = man = FCV120 MV auto = manl = Out1 Out5 SPH2SO4 (100-200) 80 SPpH (6-10) SPNaOH (100-200) 100 AG130 AG120 AG140 MVpH errpH errFlowNaOH MVFlowAcid manual Controller SPAcid1 SPAcid AutoSel1 errorFlowAcid ManVal1 PVAcid Manual AutoSel2 SPpH PVpH PVNaOH ManVal AutoSel PVNaOH1 MVFlowNaOH 1 P100 P110 The pH Neutralisation Pilot Plant - Fuzzy Logic Controller Out6 Analog Output Flowrate H2SO4 Flowrate NaOH Digital Output ExtraPoint (unused) Discharged Tank Record Cascaded Tank Reactor Tank Alkaline Pump Acid Pump Out4 APPENDIX V: ABSTRACT OF THE CONFERENCE PAPER 198