In many process industries the control of liquid level is mandatory. But the control of nonlinear process is difficult. Many process industries use conical tanks because of its non linear shape contributes better drainage for solid mixtures, slurries and viscous liquids. So, control of conical tank level is a challenging task due to its non-linearity and continually varying cross-section. This is due to relationship between controlled variable level and manipulated variable flow rate, which has a square root relationship. The main objective is to execute the suitable controller for conical tank system to maintain the desired level. System identification of the non-linear process is done using black box modelling and found to be first order plus dead time (FOPDT) model. In this paper it is proposed to obtain the mathematical modelling of a conical tank system and to study the system using block diagram after that soft computing technique like fuzzy and conventional controller is also used for the comparison.
Step variation studies of arm7 microcontroller based fuzzy logicIAEME Publication
This document describes a study of an ARM7 microcontroller-based fuzzy logic controller for controlling water level in a tank. The controller aims to improve on the performance of a conventional PID controller. A water tank system is set up with inlet and outlet valves. Water level is measured by a pressure transducer and controlled by adjusting the inlet valve opening. Fuzzy logic control algorithms are written in C and implemented on the ARM7 microcontroller. The controller is tested by subjecting it to step and step-variation inputs and its performance is compared to a PID controller based on rise time and tracking ability. Results show the fuzzy logic controller has quicker rise time and better tracking, outperforming the PID controller.
In this session you will learn:
Basics of control systems
Open and Closed loop control systems
Elements of automatic control
Two position control system
Modes of automatic control
In this session you will learn:
Instruments
Transmitters
Control valves
Valve actuators
Valve positioner
For more information, visit: https://www.mindsmapped.com/courses/industrial-automation/complete-training-on-industrial-automation-for-beginners/
Control in process industries is important to precisely regulate all aspects of manufacturing processes. This involves controlling variables like temperature, pressure, flow and level. Process control technology helps manufacturers keep operations running safely and efficiently within specified limits to maximize quality and profitability. It works by measuring process variables, evaluating measurements against set points, and controlling variables through manipulated elements like valves. This ensures consistent products despite disturbances.
This document provides an introduction to process control instrumentation and techniques. It is split into multiple units covering key topics like pressure, level, temperature, and flow measurements. The objectives are to understand the four main process variables that are measured and controlled, know what a process instrument is and how it functions, and gain an understanding of different instrument types and their applications in process control systems. Basic definitions of instrumentation terms are also provided to establish a common vocabulary.
The document provides an introduction to process control. It defines process control and explains its importance in process industries. It discusses different types of processes like continuous, batch, and their characteristics. It also explains different process control elements like feedback, feedforward, manual and automatic control systems. It distinguishes between feedback and feedforward control schemes. It discusses different process variables involved in control like controlled, manipulated and disturbance variables. Finally, it explains concepts of process dynamics including different dynamic elements like resistance, capacitance, time constant, dead time, and their effect on process response.
In this session you will learn:
Basics of control systems
Open and Closed loop control systems
Elements of automatic control
Two position control system
Modes of automatic control
For more information, visit: https://www.mindsmapped.com/courses/industrial-automation/complete-training-on-industrial-automation-for-beginners/
Step variation studies of arm7 microcontroller based fuzzy logicIAEME Publication
This document describes a study of an ARM7 microcontroller-based fuzzy logic controller for controlling water level in a tank. The controller aims to improve on the performance of a conventional PID controller. A water tank system is set up with inlet and outlet valves. Water level is measured by a pressure transducer and controlled by adjusting the inlet valve opening. Fuzzy logic control algorithms are written in C and implemented on the ARM7 microcontroller. The controller is tested by subjecting it to step and step-variation inputs and its performance is compared to a PID controller based on rise time and tracking ability. Results show the fuzzy logic controller has quicker rise time and better tracking, outperforming the PID controller.
In this session you will learn:
Basics of control systems
Open and Closed loop control systems
Elements of automatic control
Two position control system
Modes of automatic control
In this session you will learn:
Instruments
Transmitters
Control valves
Valve actuators
Valve positioner
For more information, visit: https://www.mindsmapped.com/courses/industrial-automation/complete-training-on-industrial-automation-for-beginners/
Control in process industries is important to precisely regulate all aspects of manufacturing processes. This involves controlling variables like temperature, pressure, flow and level. Process control technology helps manufacturers keep operations running safely and efficiently within specified limits to maximize quality and profitability. It works by measuring process variables, evaluating measurements against set points, and controlling variables through manipulated elements like valves. This ensures consistent products despite disturbances.
This document provides an introduction to process control instrumentation and techniques. It is split into multiple units covering key topics like pressure, level, temperature, and flow measurements. The objectives are to understand the four main process variables that are measured and controlled, know what a process instrument is and how it functions, and gain an understanding of different instrument types and their applications in process control systems. Basic definitions of instrumentation terms are also provided to establish a common vocabulary.
The document provides an introduction to process control. It defines process control and explains its importance in process industries. It discusses different types of processes like continuous, batch, and their characteristics. It also explains different process control elements like feedback, feedforward, manual and automatic control systems. It distinguishes between feedback and feedforward control schemes. It discusses different process variables involved in control like controlled, manipulated and disturbance variables. Finally, it explains concepts of process dynamics including different dynamic elements like resistance, capacitance, time constant, dead time, and their effect on process response.
In this session you will learn:
Basics of control systems
Open and Closed loop control systems
Elements of automatic control
Two position control system
Modes of automatic control
For more information, visit: https://www.mindsmapped.com/courses/industrial-automation/complete-training-on-industrial-automation-for-beginners/
This document provides an introduction to instruments used in process control industries. It describes the main components of an instrumentation system, including sensors, transducers, converters, transmitters, indicators, recorders, controllers, final control elements, and actuators. Sensors measure process parameters and transducers convert these measurements to electrical signals. Transmitters then standardize these signals so they can be processed by other equipment for indication, alarms, or automatic control. Common controller types are PLCs and DCSs. Final control elements and actuators are used to physically affect the process. The next session will cover sensors and transmitters in more detail.
This document discusses automation in the pharmaceutical industry. It defines automation and describes its advantages such as improved quality, reduced costs, and increased safety. Automation can occur at various stages of manufacturing like material handling, production processes, and quality control. The document also discusses process control and variables like temperature, pressure, and flow that are important to measure. It provides examples of automation in tablet manufacturing that can improve material handling and specific unit operations.
This document provides an introduction to process control. It defines a process as an operation that transforms raw materials into a more useful state. The objectives of process control are to produce desired outputs from inputs in the most economical way. Processes can be described by differential equations and are affected by various internal and external conditions. Effective process control requires maintaining safety, meeting production specifications, and optimizing economics while addressing changing external influences. Examples of processes include unit operations in chemical plants and manufacturing units. The document outlines the basic components of a process control system and loop.
This document discusses various technologies for measuring the level of liquid in a tank, including both continuous and point-level measurements. It analyzes technologies such as pressure sensors, sight glasses, floats, ultrasonic sensors, and conductivity probes; discussing their advantages and limitations. The objective is to select the most appropriate method for consistently measuring water level to support plastic injection molding operations and maintain sufficient water supply and pump pressure.
This document provides an introduction to process control. It discusses the importance of precise control of variables like temperature, pressure, and flow in process industries. Process control is necessary to reduce variability, increase efficiency, and ensure safety. Key terms are defined, like process variable, set point, error, and load disturbance. The components of control loops like transducers, transmitters, and different signal types are also explained.
Internal model controller based PID with fractional filter design for a nonli...IJECEIAES
1. The document presents an Internal Model Controller (IMC) based PID controller with a fractional filter for a nonlinear hopper tank process.
2. A hopper tank experimental setup is developed and linearized to obtain a first-order plus dead time model at different operating points.
3. An IMC based PID controller with a fractional filter is proposed for the linearized models, with two tuning factors: the filter time constant and fractional order. Controller settings are obtained and compared to another IMC based PID with fractional filter approach.
The document provides information on process line diagrams, symbols, and standards. It discusses general instruments and how they are represented with a balloon and internal code. It also covers symbols for different types of transmitters, valves, controllers, and line diagrams. Guidelines are provided for instrument identification and procedures in piping and instrumentation diagrams. Letter codes used for instrument identification and examples are also included.
The document discusses automation in the pharmaceutical industry, noting that automation reduces human intervention and increases precision, quality, and production capacity. It describes different types of automatic control systems used in processes like heat exchange. Finally, it examines automation applications in tablet manufacturing, highlighting specific unit operations like mixing, compression, and coating that benefit from automation.
Process control involves using computers or microprocessors to control industrial processes. There are three main types of process control: batch process control which combines specific amounts of raw materials together for a set time like making prepackaged meals; continuous process control which regulates uninterrupted processes like fuel production; and discrete process control which produces specific items through manufacturing applications like robotic assembly lines. Process control is used across many industries like oil refining, chemicals, food production, and automotive manufacturing.
This document provides information on the duties, responsibilities, knowledge, physical characteristics, and working conditions of various oil and gas pipeline professionals. It describes the roles of oil pipeline operators and maintenance workers, control center operators, gaugers, utility workers, and tank farm operators. Similar details are provided for gas pipeline compressor operators, control operators, and utility operators. Essential skills, knowledge requirements, and personal characteristics are outlined for each role.
Session 03 - History of Automation and Process IntroductionVidyaIA
In this session you will learn:
History of Industrial Automation
Types of Industrial Automation
Process Industries
Overview of Continuous & Batch Process
This document discusses key design parameters for chemical process control systems. It covers the classification of process variables, including manipulated, disturbance, measured and unmeasured variables. The document also addresses the main objectives of control systems, which are ensuring stability, suppressing disturbances, and optimizing economic performance. It questions what variables should be measured, what inputs can be manipulated, and what control configurations are best depending on the number of inputs and outputs in the chemical process. Finally, it describes the main types of control configurations: feedback, inferential, feedforward, and combinations of feedback and feedforward control.
Control Loop Foundation - Batch And Continous ProcessesEmerson Exchange
This presentation, by Emerson's Terry Blevins and Mark Nixon, is a guide for engineers, managers, technicians, and others that are new to process control or experienced control engineers who are unfamiliar with multi-loop control techniques.
Their book is available in the ISA Bookstore at: http://emrsn.co/1E
In this session you will learn:
Feed documents overview
PFD and P&ID
Process flow diagram
Piping and instrumentation diagram
For more information, visit: https://www.mindsmapped.com/courses/industrial-automation/complete-training-on-industrial-automation-for-beginners/
Wout Last and Juanita Karreman gave a presentation on Hint, an engineering and IT services company specializing in metering and allocation solutions from engineering to billing. The presentation covered Hint's professionals, engineering and IT solutions, and maintenance and support services. Hint provides solutions for custody transfer metering, analyzer management, allocation measurements, and production and reservoir management to optimize accuracy and reduce costs.
Basics of Automation in Solid Dosage Form Production (Formulation & Developem...Vishal Shelke
Basics of Automation in Solid Dosage Form Production by Mr. Vishal Shelke(Formulation & Developement M.Pharm Sem II)
https://youtube.com/vishalshelke99
https://instagram.com/vishal_stagram
Sub :- Formulation & Developement
M.Pharm Sem II
Savitribai Phule Pune University
This document discusses process control systems. It defines a process as a sequence of interdependent procedures that transforms inputs into outputs. Control involves regulating all aspects of a process. There are three main types of processes: continuous, batch, and discrete. A process control system uses controllers and feedback to maintain process variables like pressure, temperature and flow within desired ranges. It consists of sensors, actuators and an operator interface. The two main types are open-loop and closed-loop systems. Process control has applications in industries like food production, manufacturing, and waste water treatment. Future areas of development include smart cities and transportation.
Effect of Different Defuzzification methods in a Fuzzy Based Liquid Flow cont...IJITCA Journal
Most of the process control technique is suffered by the complex dynamic systems with nonlinear or timevariable thats why it is very difficult to describe the behaviour of the system. One way to deal with the
uncertainty of the behaviour of the system is to use fuzzy logic.If Fuzzy logic was modelled on spontaneous human reasoning then it captures the impreciseness the most input data which are inherent. In a fuzzy logic controller the focus is the human operator's behaviour, whereas in conventional PID controller what is modeled is the system or process being controlled.FLC regulator has a very good result from complex
nonlinear dynamic processes, uses the reasoning of the human mind which is not always in the form of a
yes or no. In this work,it shows overall effective control and operation of the mechanical equipments applied for control of liquid flow, implemented the fuzzy liquid flow algorithm and compared the effect of
using different defuzzification methods.Flow control system takes information about sensor output voltage,
control valve opening & flows rate as parameters and controls in case of overflowing & wastage.In this design two input parameters: sensor output voltage and rate of change voltage and one output parameters: opening of the control valve .
Effect of Different Defuzzification methods in a Fuzzy Based Liquid Flow cont...IJITCA Journal
Most of the process control technique is suffered by the complex dynamic systems with nonlinear or timevariable
thats why it is very difficult to describe the behaviour of the system. One way to deal with the
uncertainty of the behaviour of the system is to use fuzzy logic.If Fuzzy logic was modelled on spontaneous
human reasoning then it captures the impreciseness the most input data which are inherent. In a fuzzy logic
controller the focus is the human operator's behaviour, whereas in conventional PID controller what is
modeled is the system or process being controlled.FLC regulator has a very good result from complex
nonlinear dynamic processes, uses the reasoning of the human mind which is not always in the form of a
yes or no. In this work,it shows overall effective control and operation of the mechanical equipments
applied for control of liquid flow, implemented the fuzzy liquid flow algorithm and compared the effect of
using different defuzzification methods.Flow control system takes information about sensor output voltage,
control valve opening & flows rate as parameters and controls in case of overflowing & wastage.In this
design two input parameters: sensor output voltage and rate of change voltage and one output
parameters: opening of the control valve .
This document provides an introduction to instruments used in process control industries. It describes the main components of an instrumentation system, including sensors, transducers, converters, transmitters, indicators, recorders, controllers, final control elements, and actuators. Sensors measure process parameters and transducers convert these measurements to electrical signals. Transmitters then standardize these signals so they can be processed by other equipment for indication, alarms, or automatic control. Common controller types are PLCs and DCSs. Final control elements and actuators are used to physically affect the process. The next session will cover sensors and transmitters in more detail.
This document discusses automation in the pharmaceutical industry. It defines automation and describes its advantages such as improved quality, reduced costs, and increased safety. Automation can occur at various stages of manufacturing like material handling, production processes, and quality control. The document also discusses process control and variables like temperature, pressure, and flow that are important to measure. It provides examples of automation in tablet manufacturing that can improve material handling and specific unit operations.
This document provides an introduction to process control. It defines a process as an operation that transforms raw materials into a more useful state. The objectives of process control are to produce desired outputs from inputs in the most economical way. Processes can be described by differential equations and are affected by various internal and external conditions. Effective process control requires maintaining safety, meeting production specifications, and optimizing economics while addressing changing external influences. Examples of processes include unit operations in chemical plants and manufacturing units. The document outlines the basic components of a process control system and loop.
This document discusses various technologies for measuring the level of liquid in a tank, including both continuous and point-level measurements. It analyzes technologies such as pressure sensors, sight glasses, floats, ultrasonic sensors, and conductivity probes; discussing their advantages and limitations. The objective is to select the most appropriate method for consistently measuring water level to support plastic injection molding operations and maintain sufficient water supply and pump pressure.
This document provides an introduction to process control. It discusses the importance of precise control of variables like temperature, pressure, and flow in process industries. Process control is necessary to reduce variability, increase efficiency, and ensure safety. Key terms are defined, like process variable, set point, error, and load disturbance. The components of control loops like transducers, transmitters, and different signal types are also explained.
Internal model controller based PID with fractional filter design for a nonli...IJECEIAES
1. The document presents an Internal Model Controller (IMC) based PID controller with a fractional filter for a nonlinear hopper tank process.
2. A hopper tank experimental setup is developed and linearized to obtain a first-order plus dead time model at different operating points.
3. An IMC based PID controller with a fractional filter is proposed for the linearized models, with two tuning factors: the filter time constant and fractional order. Controller settings are obtained and compared to another IMC based PID with fractional filter approach.
The document provides information on process line diagrams, symbols, and standards. It discusses general instruments and how they are represented with a balloon and internal code. It also covers symbols for different types of transmitters, valves, controllers, and line diagrams. Guidelines are provided for instrument identification and procedures in piping and instrumentation diagrams. Letter codes used for instrument identification and examples are also included.
The document discusses automation in the pharmaceutical industry, noting that automation reduces human intervention and increases precision, quality, and production capacity. It describes different types of automatic control systems used in processes like heat exchange. Finally, it examines automation applications in tablet manufacturing, highlighting specific unit operations like mixing, compression, and coating that benefit from automation.
Process control involves using computers or microprocessors to control industrial processes. There are three main types of process control: batch process control which combines specific amounts of raw materials together for a set time like making prepackaged meals; continuous process control which regulates uninterrupted processes like fuel production; and discrete process control which produces specific items through manufacturing applications like robotic assembly lines. Process control is used across many industries like oil refining, chemicals, food production, and automotive manufacturing.
This document provides information on the duties, responsibilities, knowledge, physical characteristics, and working conditions of various oil and gas pipeline professionals. It describes the roles of oil pipeline operators and maintenance workers, control center operators, gaugers, utility workers, and tank farm operators. Similar details are provided for gas pipeline compressor operators, control operators, and utility operators. Essential skills, knowledge requirements, and personal characteristics are outlined for each role.
Session 03 - History of Automation and Process IntroductionVidyaIA
In this session you will learn:
History of Industrial Automation
Types of Industrial Automation
Process Industries
Overview of Continuous & Batch Process
This document discusses key design parameters for chemical process control systems. It covers the classification of process variables, including manipulated, disturbance, measured and unmeasured variables. The document also addresses the main objectives of control systems, which are ensuring stability, suppressing disturbances, and optimizing economic performance. It questions what variables should be measured, what inputs can be manipulated, and what control configurations are best depending on the number of inputs and outputs in the chemical process. Finally, it describes the main types of control configurations: feedback, inferential, feedforward, and combinations of feedback and feedforward control.
Control Loop Foundation - Batch And Continous ProcessesEmerson Exchange
This presentation, by Emerson's Terry Blevins and Mark Nixon, is a guide for engineers, managers, technicians, and others that are new to process control or experienced control engineers who are unfamiliar with multi-loop control techniques.
Their book is available in the ISA Bookstore at: http://emrsn.co/1E
In this session you will learn:
Feed documents overview
PFD and P&ID
Process flow diagram
Piping and instrumentation diagram
For more information, visit: https://www.mindsmapped.com/courses/industrial-automation/complete-training-on-industrial-automation-for-beginners/
Wout Last and Juanita Karreman gave a presentation on Hint, an engineering and IT services company specializing in metering and allocation solutions from engineering to billing. The presentation covered Hint's professionals, engineering and IT solutions, and maintenance and support services. Hint provides solutions for custody transfer metering, analyzer management, allocation measurements, and production and reservoir management to optimize accuracy and reduce costs.
Basics of Automation in Solid Dosage Form Production (Formulation & Developem...Vishal Shelke
Basics of Automation in Solid Dosage Form Production by Mr. Vishal Shelke(Formulation & Developement M.Pharm Sem II)
https://youtube.com/vishalshelke99
https://instagram.com/vishal_stagram
Sub :- Formulation & Developement
M.Pharm Sem II
Savitribai Phule Pune University
This document discusses process control systems. It defines a process as a sequence of interdependent procedures that transforms inputs into outputs. Control involves regulating all aspects of a process. There are three main types of processes: continuous, batch, and discrete. A process control system uses controllers and feedback to maintain process variables like pressure, temperature and flow within desired ranges. It consists of sensors, actuators and an operator interface. The two main types are open-loop and closed-loop systems. Process control has applications in industries like food production, manufacturing, and waste water treatment. Future areas of development include smart cities and transportation.
Effect of Different Defuzzification methods in a Fuzzy Based Liquid Flow cont...IJITCA Journal
Most of the process control technique is suffered by the complex dynamic systems with nonlinear or timevariable thats why it is very difficult to describe the behaviour of the system. One way to deal with the
uncertainty of the behaviour of the system is to use fuzzy logic.If Fuzzy logic was modelled on spontaneous human reasoning then it captures the impreciseness the most input data which are inherent. In a fuzzy logic controller the focus is the human operator's behaviour, whereas in conventional PID controller what is modeled is the system or process being controlled.FLC regulator has a very good result from complex
nonlinear dynamic processes, uses the reasoning of the human mind which is not always in the form of a
yes or no. In this work,it shows overall effective control and operation of the mechanical equipments applied for control of liquid flow, implemented the fuzzy liquid flow algorithm and compared the effect of
using different defuzzification methods.Flow control system takes information about sensor output voltage,
control valve opening & flows rate as parameters and controls in case of overflowing & wastage.In this design two input parameters: sensor output voltage and rate of change voltage and one output parameters: opening of the control valve .
Effect of Different Defuzzification methods in a Fuzzy Based Liquid Flow cont...IJITCA Journal
Most of the process control technique is suffered by the complex dynamic systems with nonlinear or timevariable
thats why it is very difficult to describe the behaviour of the system. One way to deal with the
uncertainty of the behaviour of the system is to use fuzzy logic.If Fuzzy logic was modelled on spontaneous
human reasoning then it captures the impreciseness the most input data which are inherent. In a fuzzy logic
controller the focus is the human operator's behaviour, whereas in conventional PID controller what is
modeled is the system or process being controlled.FLC regulator has a very good result from complex
nonlinear dynamic processes, uses the reasoning of the human mind which is not always in the form of a
yes or no. In this work,it shows overall effective control and operation of the mechanical equipments
applied for control of liquid flow, implemented the fuzzy liquid flow algorithm and compared the effect of
using different defuzzification methods.Flow control system takes information about sensor output voltage,
control valve opening & flows rate as parameters and controls in case of overflowing & wastage.In this
design two input parameters: sensor output voltage and rate of change voltage and one output
parameters: opening of the control valve .
1. The document describes several experiments related to process control systems, including temperature control loops, pressure control loops, flow control loops, and level control loops. It also covers programming a PLC and using a distributed control system.
2. The experiments are intended to study the elements of different control loops, take readings by varying set points, and observe the behavior of processes under control.
3. Programming concepts covered include logic gates, adders, multiplexers, and programming a PLC using ladder logic. The document also provides an overview of DCS systems and architectures.
Importance of three elements boiler drum level control and its installation i...ijics
Conversion of water into steam is the primary function of a utility boiler. The steam pressure is used to turn
a steam turbine thus, generating electricity. Within the boiler drum there exists a steam/water interface.
Boiler steam drum water level is one of the important parameters of power plant that must be measured
and controlled. For safe and efficient boiler operation, a constant level of water in the boiler drum is
required to be maintained. Too low water level may cause damage boiler tube by overheating. On the other
hand too high drum water level leads to improper function of separators, difficulty in temperature
controlling and damage in superheater tubes. Turbine may also be damaged by moisture or water
treatment chemicals carryover. The amount of water entering the boiler drum must be balanced with the
amounts of steam leaving to accomplish the constant water level in the drum. Therefore it is extremely
important to have the knowledge of the operating principles, installation requirements, strength and
weaknesses of drum water level control system. Ignoring these considerations can result in misapplication,
frequent maintenance, unsafe operation and poor instrument as well as system performance. In this paper
design aspects and installation requirements of boiler drum level control are discussed for safe and
economic operation.
IRJET-Smart Controlling and Monitoring of Water SystemIRJET Journal
This document describes a smart water controlling and monitoring system that aims to minimize water wastage. It uses sensors to detect water levels in overhead and underground tanks. When certain levels are reached, the system automatically controls the inlet valve and motor pump to fill the tanks. It can monitor the tank levels and operating status of the valves and motor from a remote location using GSM technology. This allows for automated operation that conserves water and prevents equipment damage from low or overflowing levels while still providing status updates when users are away.
Cost effective and efficient industrial tank cleaning processeSAT Journals
Abstract The tank cleaning process is one of the major requirements in many industries such as in Pharmaceutical Industries, Fast Moving Consumer Goods Companies (FMCG) and in Paint Industries. The tank cleaning in many small scale industries is still manual and lengthy process. In industries sometimes these tanks are placed in an area where humans cannot go and work, so to avoid this problem we thought of fully automated process of tank cleaning for that we have chosen Programmable Logic Controller (PLC).The PLC is the heart of the system Proposed system provides cost effective and efficient alternate solution to existing system. This system reduces the bulkiness and it is easy to understand (user friendly).It does not require difficult or complex algorithm and coding is also not lengthy. This system stores the water used for the process of tank cleaning instead of throwing away. From conductivity sensor we will come to know the cleanliness of the tank so the ultimate results obtained are reliable and accurate. Keywords: Programmable Logic Controller.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Instrumentation is the science of measuring and controlling process variables. The document discusses various instrumentation techniques used to measure temperature, pressure, flow, level, vibration and other variables. It describes common sensors, transmitters, controllers and control elements used in instrumentation systems. Control loops with feedback are used to optimize processes, improve product quality and safety. Programmable logic controllers (PLCs) and distributed control systems (DCSs) are computer-based approaches to automation and process control.
AUTOMATION OF WATER TREATMENT PLANT USING PLCIRJET Journal
This document discusses automating a water treatment plant using a programmable logic controller (PLC). It begins with an introduction to the need for automation in water treatment to improve efficiency and reduce human error. It then provides details on the various processes involved in water treatment like reverse osmosis and ultraviolet light. It presents a block diagram of a water treatment system with inputs like level and flow sensors and outputs like pumps and alarms that would be controlled by the PLC. The document concludes that automating the water treatment processes using a PLC can achieve 98-99% efficiency and save valuable water resources.
A hydraulic system was developed to actuate a cylinder forwards and backwards using an electronic valve. The system includes a directional valve, pressure transducer, and flow meter connected to a computer interface. It allows monitoring and controlling the cylinder's position. The interface displays pressure, flow rate, and includes controls to move the cylinder or return it to neutral. This provides a test system for developing feedback control of hydraulic cylinders.
Programmable logic controller (plc) for polymer mixing tankMohab Osman
This document describes a project to automate the manual polymer mixing process in a water treatment plant using a Programmable Logic Controller (PLC). The goals are to improve accuracy, reduce errors and costs. It involves designing a system using an Omron CP1E PLC to control water intake, polymer dosing and mixing based on tank level sensors. The system is tested through jar tests and simulations to determine optimal coagulant and polymer dosages to meet water quality standards. Results show the automated system using a PLC improves process performance over the manual method.
Liquid Flow Control by Using Fuzzy Logic Controllerijtsrd
This document describes a study that uses fuzzy logic control to regulate water levels and flow rates in a hydroelectric dam system. The system uses two inputs - water level and flow rate - and two outputs - release control valve position and drain valve position. A Mamdani-type fuzzy logic controller is designed and simulated in MATLAB. The results show the controller is able to maintain water levels and stabilize the system in the presence of disturbances, demonstrating fuzzy logic is an effective approach for complex, non-linear hydroelectric dam control problems.
International Journal of Engineering Research and DevelopmentIJERD Editor
This document describes a system for controlling the temperature of a shell and tube heat exchanger using a TwinCAT PLC. The system uses sensors to measure the outlet temperature of the tube fluid and a control valve regulates the flow of cold water to maintain the outlet at the setpoint temperature. The TwinCAT PLC implements a control algorithm that compares the measured temperature to the setpoint and sends signals to adjust the control valve position. Testing showed the system successfully maintained the outlet temperature at the desired setpoint. The automated temperature control reduces maintenance costs compared to a non-automated system.
This document summarizes an automatic liquid level and temperature monitoring/controlling system using LabVIEW and Arduino. The system uses a temperature sensor and ultrasonic sensor to monitor liquid temperature and level in a tank. It then implements proportional control to adjust a fan, heater and pump to maintain the temperature and liquid level between set points. The LabVIEW interface allows setting temperature and level set points. When levels/temperatures exceed the points, the Arduino activates components accordingly to regulate the variables and provide automated control of the liquid system.
This document discusses the components of a control system, including primary elements/sensors that measure process variables, controllers that compare measurements to setpoints and compute corrections, final control elements like control valves that implement corrections to manipulate the process, and the process itself. It provides examples of a level control loop for a surge tank, describing how level is measured by a sensor and adjusted by a control valve based on the controller's output to maintain the setpoint level. Signal types used between components and common controller types are also outlined.
This document presents a water level monitoring and control system using an Arduino module. It discusses various level measurement techniques including visual, float and arm, pressure, weight, capacitance, and ultrasonic methods. It describes types of water level control systems including those based on GSM, fuzzy logic, and microcontrollers. It provides a functional description of the Arduino-based system including sensor, display, indicator, processing and control, and power supply units. It discusses the importance and applications of such systems in industries, agriculture, and other liquid storage. The conclusion states that the system can automate water pumping and prevent wastage of resources.
This document provides an overview of process control concepts including:
1. Process control refers to methods used to control process variables when manufacturing a product. Modeling the process is important for understanding how to control it.
2. The basic elements of a process control loop include a measurement, controller, actuator, and process. Common signals are the process variable and manipulated variable.
3. Common types of controllers are on-off, proportional, integral, derivative, and PID. On-off is simple but ineffective. Proportional reduces offset but has steady state error. Integral eliminates offset but can oscillate. Derivative reduces oscillations. PID combines all three for optimal control.
4. Cascade control uses
This document introduces industrial instrumentation and provides examples. It defines instrumentation as automated measurement and control that is used in research, industry and everyday life. It then lists common process variables that are measured through instrumentation like pressure, flow rate, temperature and lists common control devices. The rest of the document defines key instrumentation terms and provides two examples - a boiler water level control system using pneumatic signals and a wastewater disinfection system using electronic signals.
Implementation of sequential design based water level monitoring and controll...IJECEIAES
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Soft Computing Technique and Conventional Controller for Conical Tank Level Control
1. Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Vol. 4, No. 1, March 2016, pp. 65~73
ISSN: 2089-3272, DOI: 10.11591/ijeei.v4i1.196 65
Received August 14, 2015; Revised January 5, 2016; Accepted January 20, 2016
Soft Computing Technique and Conventional Controller
for Conical Tank Level Control
Sudharsana Vijayan*
1
, Avinashe KK
2
PG scholar, Dept. of Electronics and Instrumentation, Asst. professor, Dept. of Electronics and
Instrumentation Vimal Jyothi Engineering College, Chemperi, Kannur, Kerala
*Corresponding author, e-mail: sudharsanavijayan@gmail.com
1
, Avinashe@vjec.ac.in
2
Abstract
In many process industries the control of liquid level is mandatory. But the control of nonlinear
process is difficult. Many process industries use conical tanks because of its non linear shape contributes
better drainage for solid mixtures, slurries and viscous liquids. So, control of conical tank level is a
challenging task due to its non-linearity and continually varying cross-section. This is due to relationship
between controlled variable level and manipulated variable flow rate, which has a square root relationship.
The main objective is to execute the suitable controller for conical tank system to maintain the desired
level. System identification of the non-linear process is done using black box modelling and found to be
first order plus dead time (FOPDT) model. In this paper it is proposed to obtain the mathematical modelling
of a conical tank system and to study the system using block diagram after that soft computing technique
like fuzzy and conventional controller is also used for the comparison.
Keywords: Matlab, Fuzzy, PID, Nonlinear System
1. Introduction
The control of nonlinear
[1]
systems has been an significant research topic and many
approaches have been proposed
[2]
. In most of the process industries controlling of level, flow,
temperature and pressure is a exigent one. They may be classified as linear and non-linear
processes based on the plant dynamics. Control of industrial processes is a challenging task for
several reasons due to their nonlinear behavior, uncertain and time varying parameters,
constraints on manipulated variable, interaction between manipulated and controlled variables,
unmeasured and frequent disturbances, dead time on input and measurements.
The control of liquid level in tanks and flow between the tank is a basic crisis in process
industries. In level control process, the tank systems like cylindrical, cubical are linear one, but
that type of tanks does not provides a complete drainage. For complete drainage of fluids, a
conical tank is used in some of the process industries, where its nonlinearity might be at the
bottom only in the case of conical bottom tank. The drainage efficiency can be improved further
if the tank is fully conical in shape. In many processes such as distillation columns, evaporators,
re-boilers and mixing tanks, the particular level of liquid in the vessel is of great significance in
process operation. A level that is too high may upset reaction equilibria, cause damage to
equipment or result in spillage of valuable or hazardous material. If the level is too low it may
have bad consequences for the sequential operations
[2]
.
So control of liquid level is an important and frequent task in process industries. Level of
liquid is desired to maintain at a constant value. This is achieved by controlling the input flow.
The control variable is the level in a tank and the manipulated variable is the inflow to the tank.
Conical tanks find wide applications in process industries, namely hydrometallurgical industries,
food process industries, concrete mixing industries and wastewater treatment industries.
The paper is organized as follows: Section I discusses about non linear level process, in
Section II and III discusses about experimental setup and modeling of the system and controller
design like conventional controller and fuzzy logic controller (FLC), respectively. The simulation
results are presented in Section IV. The conclusions are given in Section V.
2. ISSN: 2089-3272
IJEEI Vol. 4, No. 1, March 2016 : 65 – 73
66
2. Proposed Work
2.1. Experimental Setup
The system used is a conical tank and is highly nonlinear due to the variation in area of
cross section. The controlling variable is inflow of the tank. The controlled variable is level of the
conical tank. Level sensor is used to sense the level in the process tank and fed into the signal
conditioning unit and the required signal is used for further processing.
Figure 1. Block diagram of process
The level process station used to perform the experiments and to collect the data
[11]
.
One of the computers used as a controller. It consists of the software which is used to control
the level process station. The Figure 1 consists of a process tank, reservoir tank, control valve, I
to P converter, level sensor and pneumatic signals from the compressor. When the set up is
switched on, level sensor senses the actual level, initially the signal is converted to current
signal in the range between 4 to 20mA.This signal is then given to the computer through data
acquisition cord.
Based on the controller parameters and the set point value, the computer will take
consequent control action and the signal is sent to the I/P converter. Then the signal is
converted to pressure signal using I to P converter and the pressure signal acts on a control
valve which controls the inlet flow of water in to the tank.
Capacitive type level sensor is used to senses the level from the process and converts
into electrical signal. Then the electrical signal is fed to the I/V converter which in turn produces
corresponding voltage signal to the computer. The actual water level storage tank sensed by the
level transmitter is feedback to the level controller & compared with a desired level to produce
the required control action that will position the level control as needed to maintain the desired
level. Now the controller decides the control action & it is given to the V/I converter and then to
I/P converter. The final control element (pneumatic control valve) is now controlled by the
resulting air pressure. This in turn control the inflow to the conical tank & the level is maintained.
The tank is made up of stainless steel body and is mounted over a stand vertically. Water enters
the tank from the top and leaves the bottom to the storage tank. The System specifications of
the tank are as follows chapter.
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2. Proposed Work
2.1. Experimental Setup
The system used is a conical tank and is highly nonlinear due to the variation in area of
cross section. The controlling variable is inflow of the tank. The controlled variable is level of the
conical tank. Level sensor is used to sense the level in the process tank and fed into the signal
conditioning unit and the required signal is used for further processing.
Figure 1. Block diagram of process
The level process station used to perform the experiments and to collect the data
[11]
.
One of the computers used as a controller. It consists of the software which is used to control
the level process station. The Figure 1 consists of a process tank, reservoir tank, control valve, I
to P converter, level sensor and pneumatic signals from the compressor. When the set up is
switched on, level sensor senses the actual level, initially the signal is converted to current
signal in the range between 4 to 20mA.This signal is then given to the computer through data
acquisition cord.
Based on the controller parameters and the set point value, the computer will take
consequent control action and the signal is sent to the I/P converter. Then the signal is
converted to pressure signal using I to P converter and the pressure signal acts on a control
valve which controls the inlet flow of water in to the tank.
Capacitive type level sensor is used to senses the level from the process and converts
into electrical signal. Then the electrical signal is fed to the I/V converter which in turn produces
corresponding voltage signal to the computer. The actual water level storage tank sensed by the
level transmitter is feedback to the level controller & compared with a desired level to produce
the required control action that will position the level control as needed to maintain the desired
level. Now the controller decides the control action & it is given to the V/I converter and then to
I/P converter. The final control element (pneumatic control valve) is now controlled by the
resulting air pressure. This in turn control the inflow to the conical tank & the level is maintained.
The tank is made up of stainless steel body and is mounted over a stand vertically. Water enters
the tank from the top and leaves the bottom to the storage tank. The System specifications of
the tank are as follows chapter.
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IJEEI Vol. 4, No. 1, March 2016 : 65 – 73
66
2. Proposed Work
2.1. Experimental Setup
The system used is a conical tank and is highly nonlinear due to the variation in area of
cross section. The controlling variable is inflow of the tank. The controlled variable is level of the
conical tank. Level sensor is used to sense the level in the process tank and fed into the signal
conditioning unit and the required signal is used for further processing.
Figure 1. Block diagram of process
The level process station used to perform the experiments and to collect the data
[11]
.
One of the computers used as a controller. It consists of the software which is used to control
the level process station. The Figure 1 consists of a process tank, reservoir tank, control valve, I
to P converter, level sensor and pneumatic signals from the compressor. When the set up is
switched on, level sensor senses the actual level, initially the signal is converted to current
signal in the range between 4 to 20mA.This signal is then given to the computer through data
acquisition cord.
Based on the controller parameters and the set point value, the computer will take
consequent control action and the signal is sent to the I/P converter. Then the signal is
converted to pressure signal using I to P converter and the pressure signal acts on a control
valve which controls the inlet flow of water in to the tank.
Capacitive type level sensor is used to senses the level from the process and converts
into electrical signal. Then the electrical signal is fed to the I/V converter which in turn produces
corresponding voltage signal to the computer. The actual water level storage tank sensed by the
level transmitter is feedback to the level controller & compared with a desired level to produce
the required control action that will position the level control as needed to maintain the desired
level. Now the controller decides the control action & it is given to the V/I converter and then to
I/P converter. The final control element (pneumatic control valve) is now controlled by the
resulting air pressure. This in turn control the inflow to the conical tank & the level is maintained.
The tank is made up of stainless steel body and is mounted over a stand vertically. Water enters
the tank from the top and leaves the bottom to the storage tank. The System specifications of
the tank are as follows chapter.
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67
2.2. Mathematical Modelling
The dynamic behavior [11] of the liquid level h in the conical storage tank system shown
in Figure 2. From the figure,
})(2{
3
1 2
h
H
R
A
dt
dh
dt
dV
})(2{
3
1 22
h
H
R
A
dt
dh
FF outin
Figure 2. Tank crossection
Output mass flow rate can be written in terms of the exit velocity, Ve
eeout VaF
From mass balance eqn,
)()()
2
)(
()(0 21
21
2
21
21
pp
qzzqg
vv
quuq
dt
dme
)()0()
2
()0(0 21
2
2
pp
qqg
v
qq
Finally,
YhUhYhF
dt
dy
sssis 2
5
23
2
3
2
CUy
dt
dy
)(
2
12
shC
and 2
52
sh
Taking Laplace transform,
]1[)(
)(
S
C
sU
sy
IJEEI ISSN: 2089-3272
Soft Computing Technique and Conventional Controller for Conical … (Sudharsana Vijayan)
67
2.2. Mathematical Modelling
The dynamic behavior [11] of the liquid level h in the conical storage tank system shown
in Figure 2. From the figure,
})(2{
3
1 2
h
H
R
A
dt
dh
dt
dV
})(2{
3
1 22
h
H
R
A
dt
dh
FF outin
Figure 2. Tank crossection
Output mass flow rate can be written in terms of the exit velocity, Ve
eeout VaF
From mass balance eqn,
)()()
2
)(
()(0 21
21
2
21
21
pp
qzzqg
vv
quuq
dt
dme
)()0()
2
()0(0 21
2
2
pp
qqg
v
qq
Finally,
YhUhYhF
dt
dy
sssis 2
5
23
2
3
2
CUy
dt
dy
)(
2
12
shC
and 2
52
sh
Taking Laplace transform,
]1[)(
)(
S
C
sU
sy
IJEEI ISSN: 2089-3272
Soft Computing Technique and Conventional Controller for Conical … (Sudharsana Vijayan)
67
2.2. Mathematical Modelling
The dynamic behavior [11] of the liquid level h in the conical storage tank system shown
in Figure 2. From the figure,
})(2{
3
1 2
h
H
R
A
dt
dh
dt
dV
})(2{
3
1 22
h
H
R
A
dt
dh
FF outin
Figure 2. Tank crossection
Output mass flow rate can be written in terms of the exit velocity, Ve
eeout VaF
From mass balance eqn,
)()()
2
)(
()(0 21
21
2
21
21
pp
qzzqg
vv
quuq
dt
dme
)()0()
2
()0(0 21
2
2
pp
qqg
v
qq
Finally,
YhUhYhF
dt
dy
sssis 2
5
23
2
3
2
CUy
dt
dy
)(
2
12
shC
and 2
52
sh
Taking Laplace transform,
]1[)(
)(
S
C
sU
sy
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2.3. Tank Specification
Height of the tank, H = 70cm
Top diameter of the tank, D = 35.2cm
Bottom diameter of the tank, d = 4cm
Valve coefficient. K = 2
From above data calculate the process gain (C) and time constant (τ)
Where,
α = 5.035
β = 10.7
steady state value ℎ =10 (model 1)
process gain C=3.184 and time constant τ = 62.81
so, model 1 =
3.184
62.81 + 1
steady state value ℎ =20 (model 2)
process gain C=4.472 and time constant τ = 355.28
so, model 2 =
4.472
355.28 + 1
3. Controller Design
3.1. Controller Tunning
A proportional-integral-derivative controller (PID controller) is a generic control loop
feedback mechanism (controller) widely used in many control systems [5]. A PID controller
calculates an error value as the difference between a measured process variable and a desired
set point. The controller attempts to minimize the error by adjusting the process control inputs.
The PID controller is simple and robust and hence widely used in most of the process
industries.
The controller parameters can be tuned using Cohen and Coon method or Ziegler
Nichol’s method. Cohen [6] and Coon method is commonly referred to as open loop response
method for tuning the parameters of conventional controllers.
3.2. Conventional Controllers
A PI controller as Figure 3 is commonly used in engineering control system. PI
controller calculation involves two separate constant parameters, Proportional and Integral
denoted by KP and KI. P depends on present error and I on the accumulation of past errors [5].
By tuning these two parameters in PI control algorithm the controller can provide desired action
designed for specific process requirement. Different tuning rules used for achieving by properly
selecting the tuning parameters and τ for a PI controller [7].
Figure 3. Block diagram of PID controller
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68
2.3. Tank Specification
Height of the tank, H = 70cm
Top diameter of the tank, D = 35.2cm
Bottom diameter of the tank, d = 4cm
Valve coefficient. K = 2
From above data calculate the process gain (C) and time constant (τ)
Where,
α = 5.035
β = 10.7
steady state value ℎ =10 (model 1)
process gain C=3.184 and time constant τ = 62.81
so, model 1 =
3.184
62.81 + 1
steady state value ℎ =20 (model 2)
process gain C=4.472 and time constant τ = 355.28
so, model 2 =
4.472
355.28 + 1
3. Controller Design
3.1. Controller Tunning
A proportional-integral-derivative controller (PID controller) is a generic control loop
feedback mechanism (controller) widely used in many control systems [5]. A PID controller
calculates an error value as the difference between a measured process variable and a desired
set point. The controller attempts to minimize the error by adjusting the process control inputs.
The PID controller is simple and robust and hence widely used in most of the process
industries.
The controller parameters can be tuned using Cohen and Coon method or Ziegler
Nichol’s method. Cohen [6] and Coon method is commonly referred to as open loop response
method for tuning the parameters of conventional controllers.
3.2. Conventional Controllers
A PI controller as Figure 3 is commonly used in engineering control system. PI
controller calculation involves two separate constant parameters, Proportional and Integral
denoted by KP and KI. P depends on present error and I on the accumulation of past errors [5].
By tuning these two parameters in PI control algorithm the controller can provide desired action
designed for specific process requirement. Different tuning rules used for achieving by properly
selecting the tuning parameters and τ for a PI controller [7].
Figure 3. Block diagram of PID controller
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68
2.3. Tank Specification
Height of the tank, H = 70cm
Top diameter of the tank, D = 35.2cm
Bottom diameter of the tank, d = 4cm
Valve coefficient. K = 2
From above data calculate the process gain (C) and time constant (τ)
Where,
α = 5.035
β = 10.7
steady state value ℎ =10 (model 1)
process gain C=3.184 and time constant τ = 62.81
so, model 1 =
3.184
62.81 + 1
steady state value ℎ =20 (model 2)
process gain C=4.472 and time constant τ = 355.28
so, model 2 =
4.472
355.28 + 1
3. Controller Design
3.1. Controller Tunning
A proportional-integral-derivative controller (PID controller) is a generic control loop
feedback mechanism (controller) widely used in many control systems [5]. A PID controller
calculates an error value as the difference between a measured process variable and a desired
set point. The controller attempts to minimize the error by adjusting the process control inputs.
The PID controller is simple and robust and hence widely used in most of the process
industries.
The controller parameters can be tuned using Cohen and Coon method or Ziegler
Nichol’s method. Cohen [6] and Coon method is commonly referred to as open loop response
method for tuning the parameters of conventional controllers.
3.2. Conventional Controllers
A PI controller as Figure 3 is commonly used in engineering control system. PI
controller calculation involves two separate constant parameters, Proportional and Integral
denoted by KP and KI. P depends on present error and I on the accumulation of past errors [5].
By tuning these two parameters in PI control algorithm the controller can provide desired action
designed for specific process requirement. Different tuning rules used for achieving by properly
selecting the tuning parameters and τ for a PI controller [7].
Figure 3. Block diagram of PID controller
5. IJEEI ISSN: 2089-3272
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69
A Proportional Integral Derivative controller (PID) is a generic control loop feedback
mechanism widely used in industrial control systems. It is robust due to its strategy to give
bounded errors. It was an essential element of early governors and it became the standard tool
when process control emerged in the 1940s. In process control today, more than 95% of the
control loops are of PID type, most loops are actually PI control.PID controllers can these days
be found in all areas where control is needed.
3.3. Fuzzy Controller
MATLAB is a high-performance language for technical computing integrates
computation, visualization, and programming in an easy-to-use environment where problems [9]
and solutions are expressed in familiar mathematical notation.
This paper presents a simple approach to design fuzzy logic based controller for
MATLAB/Simulink environment [10]. It provides tools to create and edit fuzzy inference systems
within the framework of MATLAB, and it is also possible to integrate the fuzzy systems into
simulations with Simulink. (U) is defined as the output for fuzzy controller. They are fuzzy
linguistic variable represent the level error (e), change of level error (Δe) and the output control
effort (u) respectively. A fuzzy logic system (FLS) [12] can be defined as the nonlinear mapping
of an input data set to a scalar output data.
Figure 4. Fuzzy logic system
A FLS consists of four main parts
[13]
:
Fuzzifier
Rules
Inference engine
Defuzzifier
These components and the general architecture of a FLS are shown in Figure. The
process of fuzzy logic is explained in Algorithm 1: Firstly, a crisp set of input data are gathered
and converted to a fuzzy set using fuzzy linguistic variables, fuzzy linguistic terms and
membership functions. This step is known as fuzzification. Afterwards, an inference is made
based on a set of rules. Lastly, the resulting fuzzy output is mapped to a crisp output using the
membership functions, in the defuzzification step [8]. The input and output of the fuzzy
controllers are designed below,
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Figure 5. Input variable “error”
Figure 6. Input variable “change error”
Figure 7. Output variable “output”
Generally fuzzy controller has two inputs like error and change in error. Figure 5 is the
membership function of error and Figure 6 is the membership function of change in error.
Figure 7 represents the controlling action of the valve.
A fuzzy inference system (FIS) essentially defines a nonlinear mapping of the input data
vector into a scalar output, using fuzzy rules. The mapping process involves input/output
membership functions, FL operators, fuzzy if–then rules, aggregation of output sets, and
defuzzification.
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Figure 5. Input variable “error”
Figure 6. Input variable “change error”
Figure 7. Output variable “output”
Generally fuzzy controller has two inputs like error and change in error. Figure 5 is the
membership function of error and Figure 6 is the membership function of change in error.
Figure 7 represents the controlling action of the valve.
A fuzzy inference system (FIS) essentially defines a nonlinear mapping of the input data
vector into a scalar output, using fuzzy rules. The mapping process involves input/output
membership functions, FL operators, fuzzy if–then rules, aggregation of output sets, and
defuzzification.
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Figure 5. Input variable “error”
Figure 6. Input variable “change error”
Figure 7. Output variable “output”
Generally fuzzy controller has two inputs like error and change in error. Figure 5 is the
membership function of error and Figure 6 is the membership function of change in error.
Figure 7 represents the controlling action of the valve.
A fuzzy inference system (FIS) essentially defines a nonlinear mapping of the input data
vector into a scalar output, using fuzzy rules. The mapping process involves input/output
membership functions, FL operators, fuzzy if–then rules, aggregation of output sets, and
defuzzification.
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Figure 8. Fuzzy controller system
An FIS with multiple outputs can be considered as a collection of independent multi-
input, single-output systems. FIS contains four components: the fuzzifier, inference engine, rule
base, and defuzzifier. The rule base contains linguistic rules that are provided by experts [10]. It
is also possible to extract rules from numeric data. Once the rules have been established, the
FIS can be viewed as a system that maps an input vector to an output vector.
4. Simulation Result
At first, the mathematical model of conical tank level process is derived in terms of
differential equation and an open loop response is obtained by performing step test in Matlab.
The process is identified and closed loop control performances of various PI and PID
controllers were studied and results are presented in figures for two regions. Response and
comparison of conventional controllers and fuzzy controllers are shown in Figure 9 and
Figure 10.
Figure 9. Response of model1 at setpoint 5cm
IJEEI ISSN: 2089-3272
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Figure 8. Fuzzy controller system
An FIS with multiple outputs can be considered as a collection of independent multi-
input, single-output systems. FIS contains four components: the fuzzifier, inference engine, rule
base, and defuzzifier. The rule base contains linguistic rules that are provided by experts [10]. It
is also possible to extract rules from numeric data. Once the rules have been established, the
FIS can be viewed as a system that maps an input vector to an output vector.
4. Simulation Result
At first, the mathematical model of conical tank level process is derived in terms of
differential equation and an open loop response is obtained by performing step test in Matlab.
The process is identified and closed loop control performances of various PI and PID
controllers were studied and results are presented in figures for two regions. Response and
comparison of conventional controllers and fuzzy controllers are shown in Figure 9 and
Figure 10.
Figure 9. Response of model1 at setpoint 5cm
IJEEI ISSN: 2089-3272
Soft Computing Technique and Conventional Controller for Conical … (Sudharsana Vijayan)
71
Figure 8. Fuzzy controller system
An FIS with multiple outputs can be considered as a collection of independent multi-
input, single-output systems. FIS contains four components: the fuzzifier, inference engine, rule
base, and defuzzifier. The rule base contains linguistic rules that are provided by experts [10]. It
is also possible to extract rules from numeric data. Once the rules have been established, the
FIS can be viewed as a system that maps an input vector to an output vector.
4. Simulation Result
At first, the mathematical model of conical tank level process is derived in terms of
differential equation and an open loop response is obtained by performing step test in Matlab.
The process is identified and closed loop control performances of various PI and PID
controllers were studied and results are presented in figures for two regions. Response and
comparison of conventional controllers and fuzzy controllers are shown in Figure 9 and
Figure 10.
Figure 9. Response of model1 at setpoint 5cm
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Figure 10. Response of model2 at setpoint 15cm
From this response, fuzzy controller fastly tracks the set point than PI and PID
controllers. Comparison of controller action like settling time delay time and rise time and errors
are shown in Table 1.
Table 1. Comparison of control actions model1 at setpoint 5cm and model2 at setpoint 15cm
Model Controller Settling time (sec) Delay Time (sec) Rise Time (sec)
1
PI 13.89 0.6328 1.6799
PID 13.37 0.3012 1.5136
FUZZY 1.215 0.2352 1.4895
2
PI 4.69 0.2021 0.5316
PID 4.2195 0.0915 0.5746
FUZZY 0.873 0.0751 0.4631
Analysis shows in Table 2, the design of fuzzy controller gives a better performance by
means of minimum delay time, minimum settling time, minimum rise time and satisfactory over a
wide range of process operations.
Table 2. Comparison of errors model1 at setpoint 5cm and model2 at setpoint 15cm
Model Controller ISE (sec) IAE (sec) ITAE (sec)
1
PI 13.44 7.152 17.76
PID 6.15 4.893 12.73
FUZZY 0.3614 0.1359 0.9207
2
PI 39.36 7.024 5.705
PID 18.01 4.79 4.094
FUZZY 17.82 2.18 2.4
The behavior of fuzzy controller is to provide better performance in the case of errors
and control actions.
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5. Conclusion
The controlling of nonlinear process is a challenging task. The nonlinearity of the
conical tank is analyzed. Modelling and transfer function of the system is done by using system
identification. Open loop step test method is used to find the proportional gain, delay time and
dead time. Here Taylor series approximation is used for the non linear approximation because
of accuracy compared to other non linear approximation technique. Conventional controllers like
PI & PID controllers are used as basic controllers; fuzzy controller is also used for obtaining
improved performance. Here, after analyzing both simulated response of models, the fuzzy
controller is superlative controller than PI and PID controllers.
References
[1] D Angeline, K Vivetha, K Gandhimathi T Praveena. ‘Model based Controller Design for Conical Tank
System’. International Journal of Computer Applications. 0975 – 8887. 2014; 85(12).
[2] Anna Joseph and Samson Isaac J. ‘Real Time Implementation of Model Reference Adaptive
Controller for a Conical Tank’. Proceedings of International Journal on Theoretical and Applied
Research in Mechanical Engineering. 2013; 2(1): 57-62.
[3] T Pushpaveni, S Srinivasulu Raju, N Archana, M Chandana. ‘Modeling and Controlling of Conical
tank system using adaptive controllers and performance comparison with conventional PID’.
International Journal of Scientific & Engineering Research, ISSN 2229-5518. 2013; 4(5): 629.
[4] K Sundaravadivu, K Saravanan and V Jeyakumar. “Design of Fractional Order PI controller for liquid
level control of spherical tank modeled as Fractional Order System”. IEEE, ICCSCE, Malaysia. 2011:
522 – 525.
[5] Marshiana D and Thirusakthimurugan P. ‘Design of Ziegler Nichols Tuning controller for a Non-linear
System’. Proceedings of International Conference on Computing and Control Engineering. 2012:
121-124.
[6] Abhishek Sharma and Nithya Venkatesan. ‘Comparing PI controller Performance for Non Linear
Process Model’. Proceedings of International Journal of Engineering Trends and Technology. 2013;
4(3): 242-245.
[7] H Kiren Vedi, K Ghousiya Begum, D Mercy, E Kalaiselvan. “A Comparative Novel Method of
Enhanced Tuning of Controllers for Non-Linear Process”. National System Conference. 2012.
[8] A Short Fuzzy Logic Tutorial April 8, 2010
[9] Fuzzy control programming. Technical report, International Electrotechnical Commision. 1997.
[10] J Mendel. Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE. 1995; 83(3): 345-
377.
[11] Rajesh T, Arun Jayakar, Siddharth SG. “Design and implementation of IMC based PID controller for
conical tank level control process”. International Journal of Innovative Research in Electrical,
Electronics and Instrumentation and control Engineering. 2014; 2(9).
[12] Tutorial on fuzzy Logic.
[13] Marcelo Godoy Simoes, Colorado School of Mines Engineering Division 1610 Illinois Street Golden,
Colorado 80401-1887 USA “Introduction to fuzzy control”.