This paper presents an analogous electrical model for water processing plant. The three major components of the plant; the connecting pipes, the water tanks and the filter have been modeled here by resistors, capacitors and inductors of an electrical circuit as a model of the plant. The mechanical properties of these components are thus represented by equivalent electrical properties as resistance, capacitance and inductance, respectively. In these representations the resistance is expressed as a function of both the cross sectional area (A) and the length (L) of the pipes; and the capacitance is expressed as a function of the Area of the tanks (capacity of tank), while the inductance (constriction to debris) is expressed as a function of number of wound string on the filter cartridge. From the results of the simulation, it was observed that by varying the electrical parameters (resistors, capacitors, inductor), it is possible to study the way the manipulation of equivalent parameters of the analogous mechanical components (the connecting pipes, the water tanks and the water filter) of the water plant could influence the rate of water flow in the production process. This work does demonstrate the possibility of knowing from the beginning the various sizes of pipes, tanks and filter to be used and how these will affect the flow of water in the plant before going into the physical construction of the plant. This method could be used in a more complex system.
Comparative Analysis of Pso-Pid and Hu-PidIJERA Editor
PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter‟ of power system engineering. It is an important component in every control engineer‟s tool box. PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence on the PID controller
This document outlines the design methodology for process control in 15 steps: 1) understand the process, 2) identify operating parameters, 3) identify hazardous conditions, 4) identify measurables, 5) identify measurement points, 6) select measurement methods, 7) select control methods, 8) select control systems, 9) set control limits, 10) define control logic, 11) create redundancy, 12) define fail-safes, 13) set lead/lag criteria, 14) investigate effects of changes, and 15) integrate and test systems. It also lists the typical components of a control system and provides an example of a surge tank level control system.
Plant wide control design based on steady-state combined indexesISA Interchange
This work proposes an alternative methodology for designing multi-loop control structures based on steady-state indexes and multi-objective combinatorial optimization problems. Indeed, the simultaneous selection of the controlled variables, manipulated variables, input-output pairing, and controller size and interaction degree is performed by using a combined index which relies on the sum of square deviations and the net load evaluation assessments in conjunction. This unified approach minimizes both the dynamic simulation burden and the heuristic knowledge requirements for deciding about the final optimal control structure. Further, this methodology allows incorporating structural modifications of the optimization problem context (degrees of freedom). The case study selected is the well-known Tennessee Eastman process and a set of simulations are given to compare this approach with early works.
Pervasive Computing Based Intelligent Energy Conservation SystemEswar Publications
Most of the HVAC system in home is running based on static control algorithm; based on fixed work schedules. In that old system energy became waste when home contains low or no people occupancy. In this paper we presented new dynamic approach of HVAC system control, by combined with pervasive computing. Pervasive computing can be defined as availability of centralized system and information anywhere and anytime. We achieved our target by using occupancy sensors for collecting home status. Initially our occupancy sensors collect human presence and current HVAC status details and stored in centralized system. Then based on our user defined threshold value the centralized system maintains the building's heating, cooling and air quality conditions by controlling HVAC devices. I.e. this system turned off HVAC systems when a home is unoccupied, or put the system into an energy saving sleep mode when persons are asleep.
Experimental analysis and thermal comfort index of air conditioned meeting halleSAT Journals
Abstract India’s building energy consumption is increasing continuously. The subcontinent does not have custom made thermal comfort standards. We conducted thermal comfort field study in meeting hall, Madurai, tamilnadu, india. Experimental study of thermal comfort was conducted in three levels of load conditions and three levels of air flow in meeting hall during summer 2014. The above three level of load condition and air flow measurements values observed by using direct reading instruments. Thermal comfort indices were also calculated and compare to the natural ventilated meeting hall and outdoor environment. This study aims to evaluate the indoor comfort quality of the meeting hall. This paper is focused on improving the indoor air quality by controlling the vital parameters like carbon dioxide, carbon monoxide, oxygen, room temperature, relative velocity and relative humidity which were used as indicators for indoor air quality and comfort levels. The investigated meeting hall building is located in India, tamilnadu, Madurai and the volume of the building is about 10.60m (length) × 9.14m (width) × 3m (height). This paper provides the detailed specifications to improve the thermal comfort in the air conditioned living atmosphere and ensure safety and healthy living environment. This paper contained Experimental analysis and Calculation of thermal comfort index. Keywords: Meeting hall, Indoor Air Quality, various loads, various air flow rates, thermal comfort index.
Experimental analysis and thermal comfort index of air conditioned meeting halleSAT Publishing House
The document describes an experimental analysis of thermal comfort in an air conditioned meeting hall in Madurai, India. Measurements were taken under different load and air flow conditions during the summer of 2014. Parameters like temperature, humidity, CO2, CO, O2 and air velocity were measured to evaluate indoor air quality and comfort levels. The meeting hall volume was approximately 10.6m x 9.14m x 3m. Test results were compared to standards from ASHRAE and other studies. The aim was to evaluate indoor comfort quality and provide recommendations to improve thermal comfort and ensure a healthy indoor environment.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
INDUCTIVE LOGIC PROGRAMMING FOR INDUSTRIAL CONTROL APPLICATIONScsandit
Advanced Monitoring Systems of the processes constitute a higher level to the systems of control
and use specific techniques and methods. An important part of the task of supervision focuses on
the detection and the diagnosis of various situations of faults which can affect the process.
Methods of fault detection and diagnosis (FDD) are different from the type of knowledge about
the process that they require. They can be classified as data-driven, analytical, or knowledgebased
approach. A collaborative FDD approach that combines the strengths of various
heterogeneous FDD methods is able to maximize diagnostic performance. The new generation
of knowledge-based systems or decision support systems needs to tap into knowledge that is
both very broad, but specific to a domain, combining learning, structured representations of
domain knowledge such as ontologies and reasoning tools. In this paper, we present a decisionaid
tool in case of malfunction of high power industrial steam boiler. For this purpose an
ontology was developed and considered as a prior conceptual knowledge in Inductive Logic
Programming (ILP) for inducing diagnosis rules. The next step of the process concerns the
inclusion of rules acquired by induction in the knowledge base as well as their exploitation for
reasoning.
Comparative Analysis of Pso-Pid and Hu-PidIJERA Editor
PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter‟ of power system engineering. It is an important component in every control engineer‟s tool box. PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence on the PID controller
This document outlines the design methodology for process control in 15 steps: 1) understand the process, 2) identify operating parameters, 3) identify hazardous conditions, 4) identify measurables, 5) identify measurement points, 6) select measurement methods, 7) select control methods, 8) select control systems, 9) set control limits, 10) define control logic, 11) create redundancy, 12) define fail-safes, 13) set lead/lag criteria, 14) investigate effects of changes, and 15) integrate and test systems. It also lists the typical components of a control system and provides an example of a surge tank level control system.
Plant wide control design based on steady-state combined indexesISA Interchange
This work proposes an alternative methodology for designing multi-loop control structures based on steady-state indexes and multi-objective combinatorial optimization problems. Indeed, the simultaneous selection of the controlled variables, manipulated variables, input-output pairing, and controller size and interaction degree is performed by using a combined index which relies on the sum of square deviations and the net load evaluation assessments in conjunction. This unified approach minimizes both the dynamic simulation burden and the heuristic knowledge requirements for deciding about the final optimal control structure. Further, this methodology allows incorporating structural modifications of the optimization problem context (degrees of freedom). The case study selected is the well-known Tennessee Eastman process and a set of simulations are given to compare this approach with early works.
Pervasive Computing Based Intelligent Energy Conservation SystemEswar Publications
Most of the HVAC system in home is running based on static control algorithm; based on fixed work schedules. In that old system energy became waste when home contains low or no people occupancy. In this paper we presented new dynamic approach of HVAC system control, by combined with pervasive computing. Pervasive computing can be defined as availability of centralized system and information anywhere and anytime. We achieved our target by using occupancy sensors for collecting home status. Initially our occupancy sensors collect human presence and current HVAC status details and stored in centralized system. Then based on our user defined threshold value the centralized system maintains the building's heating, cooling and air quality conditions by controlling HVAC devices. I.e. this system turned off HVAC systems when a home is unoccupied, or put the system into an energy saving sleep mode when persons are asleep.
Experimental analysis and thermal comfort index of air conditioned meeting halleSAT Journals
Abstract India’s building energy consumption is increasing continuously. The subcontinent does not have custom made thermal comfort standards. We conducted thermal comfort field study in meeting hall, Madurai, tamilnadu, india. Experimental study of thermal comfort was conducted in three levels of load conditions and three levels of air flow in meeting hall during summer 2014. The above three level of load condition and air flow measurements values observed by using direct reading instruments. Thermal comfort indices were also calculated and compare to the natural ventilated meeting hall and outdoor environment. This study aims to evaluate the indoor comfort quality of the meeting hall. This paper is focused on improving the indoor air quality by controlling the vital parameters like carbon dioxide, carbon monoxide, oxygen, room temperature, relative velocity and relative humidity which were used as indicators for indoor air quality and comfort levels. The investigated meeting hall building is located in India, tamilnadu, Madurai and the volume of the building is about 10.60m (length) × 9.14m (width) × 3m (height). This paper provides the detailed specifications to improve the thermal comfort in the air conditioned living atmosphere and ensure safety and healthy living environment. This paper contained Experimental analysis and Calculation of thermal comfort index. Keywords: Meeting hall, Indoor Air Quality, various loads, various air flow rates, thermal comfort index.
Experimental analysis and thermal comfort index of air conditioned meeting halleSAT Publishing House
The document describes an experimental analysis of thermal comfort in an air conditioned meeting hall in Madurai, India. Measurements were taken under different load and air flow conditions during the summer of 2014. Parameters like temperature, humidity, CO2, CO, O2 and air velocity were measured to evaluate indoor air quality and comfort levels. The meeting hall volume was approximately 10.6m x 9.14m x 3m. Test results were compared to standards from ASHRAE and other studies. The aim was to evaluate indoor comfort quality and provide recommendations to improve thermal comfort and ensure a healthy indoor environment.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
INDUCTIVE LOGIC PROGRAMMING FOR INDUSTRIAL CONTROL APPLICATIONScsandit
Advanced Monitoring Systems of the processes constitute a higher level to the systems of control
and use specific techniques and methods. An important part of the task of supervision focuses on
the detection and the diagnosis of various situations of faults which can affect the process.
Methods of fault detection and diagnosis (FDD) are different from the type of knowledge about
the process that they require. They can be classified as data-driven, analytical, or knowledgebased
approach. A collaborative FDD approach that combines the strengths of various
heterogeneous FDD methods is able to maximize diagnostic performance. The new generation
of knowledge-based systems or decision support systems needs to tap into knowledge that is
both very broad, but specific to a domain, combining learning, structured representations of
domain knowledge such as ontologies and reasoning tools. In this paper, we present a decisionaid
tool in case of malfunction of high power industrial steam boiler. For this purpose an
ontology was developed and considered as a prior conceptual knowledge in Inductive Logic
Programming (ILP) for inducing diagnosis rules. The next step of the process concerns the
inclusion of rules acquired by induction in the knowledge base as well as their exploitation for
reasoning.
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.
This document discusses various advanced control configurations that can enhance the performance of single-loop PID controllers. It describes cascade control, which uses two measurements and one manipulated variable to improve disturbance rejection. It also discusses selective/override control, which shares one or more manipulated variables among multiple controlled variables. Other configurations covered include split-range control, ratio control, inferential control, feedforward control, and combinations of these methods. The key advantages and applications of each configuration are provided through examples.
The document discusses concepts related to automatic control systems including open loop and closed loop systems. It covers topics such as feedback, controllers like proportional, integral and proportional integral differential controllers. It also provides examples of automatic control systems used in various industries and applications. The document consists of lecture slides on control systems for a class.
A LEVEL TRAINING SET WITH BOTH A COMPUTER- BASED CONTROL AND A COMPACT CONTRO...ijesajournal
In engineering education, the combination with theoretical education and practical education is an essential problem. The taught knowledge can be quickly forgotten without an xperimental application. In addition, the theoretical knowledge’s cannot be easily associated applications by students when they start
working in industry. To eliminate these problems, a number of education tools have been developed in engineering education. This article presents a modelling, simulation and practice study of a newly designed liquid level training set developed for the control engineering students to simulate, examine and analyze
theoretically and experimentally the controllers widely used in the control of many industrial processes. The newly designed training set combines two control structures, which are a computer-based control and a digital signal processing-based control. The set displays the results related to experiments in real time as
well as. These features have made it a suitable laboratory component on which the students can both simulate and test the performance of liquid level control systems by using theoretical different control structures.
A Level Training Set with both a Computer-Based Control and a Compact Control...ijesajournal
In engineering education, the combination with theoretical education and practical education is an
essential problem. The taught knowledge can be quickly forgotten without an experimental application. In
addition, the theoretical knowledge’s cannot be easily associated applications by students when they start
working in industry. To eliminate these problems, a number of education tools have been developed in
engineering education. This article presents a modelling, simulation and practice study of a newly designed
liquid level training set developed for the control engineering students to simulate, examine and analyze
theoretically and experimentally the controllers widely used in the control of many industrial processes.
The newly designed training set combines two control structures, which are a computer-based control and
a digital signal processing-based control. The set displays the results related to experiments in real time as
well as. These features have made it a suitable laboratory component on which the students can both
simulate and test the performance of liquid level control systems by using theoretical different control
structures.
Combined ILC and PI regulator for wastewater treatment plantsTELKOMNIKA JOURNAL
Due to high nonlinearity with features of large time constants, delays, and interaction among variables, control of the wastewater treatment plants (WWTPs) is a very challenging task. Modern control strategies such as model predictive controllers or artificial neural networks can be used to deal with the non-linearity. Another characteristic of this system should be considered is that it works repetitively. Iterative learning control (ILC) is a potential candidate for such a demanding task. This paper proposes a method using ILC for WWTPs to achieve new results. By exploiting data from the previous iterations, the learning control algorithm can improve gradually tracking control performance for the next runs, and hence outperforms conventional control approaches such as feedback controller and model predictive control (MPC). The benchmark simulation model No.1-BSM1 has been used as a standard for performance assessment and evaluation of the control strategy. Control of the Dissolved Oxygen in the aerated reactors has been performed using the PD-type ILC algorithms. The obtained results show the advantages of ILC over a classical PI control concerning the control quality indexes, IEA and ISE, of the system. Besides, the conventional feedback regulator is designed in a combination with the iterative learning control to deal with uncertainty. Simulation results demonstrate the potential benefits of the proposed method.
The document provides an overview of basic instrumentation concepts including definitions, measuring means, controlling means, and process automation. It defines instrumentation as electrical or pneumatic devices used for measurement and control in a system. Common process variables that are measured include pressure, temperature, level, and flow. Controllers compare process variable measurements to setpoints and adjust manipulating variables to maintain process equilibrium. The document also discusses open and closed loop control systems, signal transmission methods, and key instrumentation terms.
An educational fuzzy temperature control system IJECEIAES
Control engineering is one of the important engineering topics taught at many engineering based universities around the world in most undergraduate and postgraduate courses. The control engineering curriculum includes both the classical feedback based control theory and the state space theory. The modern control theory is based on the intelligent control algorithms utilizing the soft computing techniques, such as the fuzzy control theory and neural networks. Laboratory work is an important part of any control engineering course. The problem with the modern control theory laboratories is that it is essential to offer simple experiments to students so that they can easily put the complex theories they have learned in their courses into practice and see and understand the results. This paper describes the design of a low-cost fuzzy based microcontroller temperature control system using off the shelf products. The developed system should provide a low-cost fuzzy control experiment in the laboratories for students studying control engineering.
This document discusses advanced control loop strategies including multivariable loops, feedforward control, cascade control, ratio control, batch control, and selective control. It was presented by Dr. S. Meenatchisundaram for the Process Instrumentation and Control course at MIT Manipal. Examples are provided for each control strategy and it is explained that factors like outside disturbances must be accounted for in combined feedforward and feedback systems. Tuning of primary and secondary loops is also addressed.
Plant-Wide Control: Eco-Efficiency and Control Loop ConfigurationISA Interchange
Since the eco-efficiency of all industrial processes/plants has become increasingly important, engineers need to find a way to integrate the control loop configuration and the measurements of eco-efficiency. A new measure of eco-efficiency, the exergy eco-efficiency factor, for control loop configuration, is proposed in this paper. The exergy eco-efficiency factor is based on the thermodynamic concept of exergy which can be used to analyse a process in terms of its efficiency associated with the control configuration. The combination of control pairing configuration techniques (such as the relative gain array, RGA and Niederlinski index, NI) and the proposed exergy eco-efficiency factor will guide the process designer to reach the optimal control design with low operational cost (i.e., energy consumption). The exergy eco-efficiency factor is implemented in the process simulation case study and the reliability of the proposed method is demonstrated by dynamic simulation results.
This document provides an overview of a chemical process control course. It includes:
- A list of course policies including exams, assignments, and grading.
- An outline of topics to be covered in the course ranging from process modeling to controller design.
- An introduction to process control concepts including different types of processes (continuous, batch, semibatch), control variables, and strategies for controlling processes like a blending operation.
The document provides details on the structure, content, and objectives of the chemical process control course.
This document provides an introduction to controlling electrohydraulic systems. It discusses the basic components of hydraulic systems including pumps, valves, and actuators. It emphasizes the importance of properly sizing these components to meet system demands while avoiding instability. The document also reviews some basic hydraulic principles including fluid properties, flow, and the necessity of feedback control systems to stabilize more complex electrohydraulic systems. It positions feedback control as key to enhancing system performance beyond what can be achieved with hydraulic components alone.
This document provides an overview of control systems. It defines a control system as a device or collection of devices that manage the behavior of other devices. It describes distributed control systems (DCS) which have controllers distributed throughout a machine instead of a central controller. The document then discusses the basics of control systems, including feedback and feedforward control. It provides examples of early control systems and describes the development of control theory over time. Finally, it discusses different types of modern control systems including open loop, closed loop, supervisory, direct digital, and hierarchy control systems.
This document provides an introduction to a course on Process Instrumentation and Control. It defines process control as dealing with mechanisms, architectures, and algorithms for controlling processes. Examples of controlled processes include controlling temperature with steam addition and maintaining fluid levels in tanks. The objectives of control are to maintain operational conditions, transition between conditions, and define key terminology like manipulated and disturbance inputs, control and output variables, and common control structures like SISO, MIMO, and PID controllers.
Enhancing the Performance of An Industrial Boiler Using Fuzzy Logic ControllerIJMREMJournal
This paper focus on the performance of an industrial boiler using fuzzy logic controller. The parameter of the
various industrial boilers are subjected to the change due to change in the environment or atmosphere. These
parameter may be categorized as steam, pressure and temperature of the industrial boiler in use. In this paper
work, a strategy of fuzzy logic controller called fuzzy supervisory is used which generates set points for the
conventional controllers. This work also compared the performance of a boiler evaporator system when the
system is controlled by a traditional proportional integral derivatives type strategy and when the system is
controlled using fuzzy logic blocs to provide set point for it. The main change consists of representing only the
behaviour of the drum evaporator system having a partial model of the combustion process with a simplified
combustion control system and a three element boiler feed water receives a supervisory signal that comes from
fuzzy logic to improve the performance of the overall control system.
Enhancing the Performance of An Industrial Boiler Using Fuzzy Logic ControllerIJMREMJournal
This document discusses enhancing the performance of an industrial boiler using a fuzzy logic controller. It compares the performance of a boiler evaporator system controlled by a traditional PID controller versus one controlled using fuzzy logic blocks. The fuzzy logic controller generates set points for the PID controller to improve overall control system performance. It presents data collected from a boiler system measuring parameters like steam, pressure and temperature over time. The fuzzy logic controller was able to better handle fluctuations and non-linearity compared to just using a PID controller.
Technical communication of automation control system in water treatment planthunypink
This paper presents technical communication of automation industry which describes the technical issues of
automation control system in operation development, improving management level and high efficiency process in water treatment system. Today’s water treatment plants are applied for water conservancy projects, emerged by the technology of automation control system is to ensure safe, continues, high quality water supply to municipal and for multi-purpose usage. Along with automation technology, computer technology, network communication development, advanced water treatment monitoring system is realized in Nantong pengyoa water purification plant. The Nantong pengyoa water purification plant has an important beneficial industry relationship to People’s Republic of China improving living status and environment condition mainly expounds the water supply, to build well-off society, comparatively improving the labor production growth & level of implementation of targets as well as high water quality requirements. In this paper, it develops the task and tells the technical solutions of water treatment plant which has been centralized in fully automated operation in some developed industries since many years. And also append short description of its current practices such as networking, and real-time monitoring control, composition & structure, process flow and automatic process control which are performed in water treatment plants to achieve high efficiency in quality of productivity.
Decentralised PI controller design based on dynamic interaction decoupling in...IJECEIAES
An enhanced method for design of decenralised proportional integral (PI) controllers to control various variables of flotation columns is proposed. These columns are multivariable processes characterised by multiple interacting manipulated and controlled variables. The control of more than one variable is not an easy problem to solve as a change in a specific manipulated variable affects more than one controlled variable. Paper proposes an improved method for design of decentralized PI controllers through the introduction of decoupling of the interconnected model of the process. Decoupling the system model has proven to be an effective strategy to reduce the influence of the interactions in the closed-loop control and consistently to keep the system stable. The mathematical derivations and the algorithm of the design procedure are described in detail. The behaviour and performance of the closed-loop systems without and with the application of the decoupling method was investigated and compared through simulations in MATLAB/Simulink. The results show that the decouplers - based closedloop system has better performance than the closed-loop system without decouplers. The highest improvement (2 to 50 times) is in the steady-state error and 1.2 to 7 times in the settling and rising time. Controllers can easily be implemented.
This document provides an overview of process control applications in chemical industries. It discusses what process control is, why it is needed, common control techniques like feedback control, and examples of control systems for units like chemical reactors and boilers. Process control is used to maximize safety, quality and efficiency in chemical processes. It allows preserving product quality while optimizing production. The document also reviews various software and instruments used for process control and highlights its importance across many industrial sectors.
This document is a summer training report submitted by Pradeep Solanki to fulfill the requirements for a bachelor's degree in electrical engineering. The report discusses automation using programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems. It provides an overview of automation technologies, including feedback control, sequential control, and computer control. The report also examines the history and applications of automation in various industries.
Analysis and Modeling of PID and MRAC Controllers for a Quadruple Tank System...dbpublications
Multivariable systems exhibit complex dynamics because of the interactions between input variables and output variables. In this paper an approach to design auto tuned decentralized PI controller using ideal decoupler and adaptive techniques for controlling a class of multivariable process with a transmission zero. By using decoupler, the MIMO system is transformed into two SISO systems. The controller parameters were adjusted using the Model Reference Adaptive reference Control. In recent process industries, PID and MRAC are the two widely accepted control strategies, where PID is used at regulatory level control and MRAC at supervisory level control. In this project, LabVIEW is used to simulate the PID with Decoupler and MRAC separately and analyze their performance based on steady state error tracking and overshoot.
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.
This document discusses various advanced control configurations that can enhance the performance of single-loop PID controllers. It describes cascade control, which uses two measurements and one manipulated variable to improve disturbance rejection. It also discusses selective/override control, which shares one or more manipulated variables among multiple controlled variables. Other configurations covered include split-range control, ratio control, inferential control, feedforward control, and combinations of these methods. The key advantages and applications of each configuration are provided through examples.
The document discusses concepts related to automatic control systems including open loop and closed loop systems. It covers topics such as feedback, controllers like proportional, integral and proportional integral differential controllers. It also provides examples of automatic control systems used in various industries and applications. The document consists of lecture slides on control systems for a class.
A LEVEL TRAINING SET WITH BOTH A COMPUTER- BASED CONTROL AND A COMPACT CONTRO...ijesajournal
In engineering education, the combination with theoretical education and practical education is an essential problem. The taught knowledge can be quickly forgotten without an xperimental application. In addition, the theoretical knowledge’s cannot be easily associated applications by students when they start
working in industry. To eliminate these problems, a number of education tools have been developed in engineering education. This article presents a modelling, simulation and practice study of a newly designed liquid level training set developed for the control engineering students to simulate, examine and analyze
theoretically and experimentally the controllers widely used in the control of many industrial processes. The newly designed training set combines two control structures, which are a computer-based control and a digital signal processing-based control. The set displays the results related to experiments in real time as
well as. These features have made it a suitable laboratory component on which the students can both simulate and test the performance of liquid level control systems by using theoretical different control structures.
A Level Training Set with both a Computer-Based Control and a Compact Control...ijesajournal
In engineering education, the combination with theoretical education and practical education is an
essential problem. The taught knowledge can be quickly forgotten without an experimental application. In
addition, the theoretical knowledge’s cannot be easily associated applications by students when they start
working in industry. To eliminate these problems, a number of education tools have been developed in
engineering education. This article presents a modelling, simulation and practice study of a newly designed
liquid level training set developed for the control engineering students to simulate, examine and analyze
theoretically and experimentally the controllers widely used in the control of many industrial processes.
The newly designed training set combines two control structures, which are a computer-based control and
a digital signal processing-based control. The set displays the results related to experiments in real time as
well as. These features have made it a suitable laboratory component on which the students can both
simulate and test the performance of liquid level control systems by using theoretical different control
structures.
Combined ILC and PI regulator for wastewater treatment plantsTELKOMNIKA JOURNAL
Due to high nonlinearity with features of large time constants, delays, and interaction among variables, control of the wastewater treatment plants (WWTPs) is a very challenging task. Modern control strategies such as model predictive controllers or artificial neural networks can be used to deal with the non-linearity. Another characteristic of this system should be considered is that it works repetitively. Iterative learning control (ILC) is a potential candidate for such a demanding task. This paper proposes a method using ILC for WWTPs to achieve new results. By exploiting data from the previous iterations, the learning control algorithm can improve gradually tracking control performance for the next runs, and hence outperforms conventional control approaches such as feedback controller and model predictive control (MPC). The benchmark simulation model No.1-BSM1 has been used as a standard for performance assessment and evaluation of the control strategy. Control of the Dissolved Oxygen in the aerated reactors has been performed using the PD-type ILC algorithms. The obtained results show the advantages of ILC over a classical PI control concerning the control quality indexes, IEA and ISE, of the system. Besides, the conventional feedback regulator is designed in a combination with the iterative learning control to deal with uncertainty. Simulation results demonstrate the potential benefits of the proposed method.
The document provides an overview of basic instrumentation concepts including definitions, measuring means, controlling means, and process automation. It defines instrumentation as electrical or pneumatic devices used for measurement and control in a system. Common process variables that are measured include pressure, temperature, level, and flow. Controllers compare process variable measurements to setpoints and adjust manipulating variables to maintain process equilibrium. The document also discusses open and closed loop control systems, signal transmission methods, and key instrumentation terms.
An educational fuzzy temperature control system IJECEIAES
Control engineering is one of the important engineering topics taught at many engineering based universities around the world in most undergraduate and postgraduate courses. The control engineering curriculum includes both the classical feedback based control theory and the state space theory. The modern control theory is based on the intelligent control algorithms utilizing the soft computing techniques, such as the fuzzy control theory and neural networks. Laboratory work is an important part of any control engineering course. The problem with the modern control theory laboratories is that it is essential to offer simple experiments to students so that they can easily put the complex theories they have learned in their courses into practice and see and understand the results. This paper describes the design of a low-cost fuzzy based microcontroller temperature control system using off the shelf products. The developed system should provide a low-cost fuzzy control experiment in the laboratories for students studying control engineering.
This document discusses advanced control loop strategies including multivariable loops, feedforward control, cascade control, ratio control, batch control, and selective control. It was presented by Dr. S. Meenatchisundaram for the Process Instrumentation and Control course at MIT Manipal. Examples are provided for each control strategy and it is explained that factors like outside disturbances must be accounted for in combined feedforward and feedback systems. Tuning of primary and secondary loops is also addressed.
Plant-Wide Control: Eco-Efficiency and Control Loop ConfigurationISA Interchange
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Analogous Electrical Model of Water Processing Plant as a Tool to Study “The Real Problem” of Process Control.
1. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
DOI : 10.5121/ijctcm.2013.3101 1
Analogous Electrical Model of Water Processing
Plant as a Tool to Study “The Real Problem” of
Process Control.
Ani Vincent Anayochukwu
Department of Electronic Engineering, University of Nigeria, Nsukka. Enugu State.
Nigeria.
vincent_ani@yahoo.com
+234 8054024629
Abstract
This paper presents an analogous electrical model for water processing plant. The three major components
of the plant; the connecting pipes, the water tanks and the filter have been modeled here by resistors,
capacitors and inductors of an electrical circuit as a model of the plant. The mechanical properties of these
components are thus represented by equivalent electrical properties as resistance, capacitance and
inductance, respectively. In these representations the resistance is expressed as a function of both the cross
sectional area (A) and the length (L) of the pipes; and the capacitance is expressed as a function of the
Area of the tanks (capacity of tank), while the inductance (constriction to debris) is expressed as a function
of number of wound string on the filter cartridge. From the results of the simulation, it was observed that
by varying the electrical parameters (resistors, capacitors, inductor), it is possible to study the way the
manipulation of equivalent parameters of the analogous mechanical components (the connecting pipes, the
water tanks and the water filter) of the water plant could influence the rate of water flow in the production
process. This work does demonstrate the possibility of knowing from the beginning the various sizes of
pipes, tanks and filter to be used and how these will affect the flow of water in the plant before going into
the physical construction of the plant. This method could be used in a more complex system.
Keywords
Analogous System, State equation, Process Control, modeling, Simulation, Nigeria.
1. INTRODUCTION
Process control research is a term that is often used but rarely defined. The basic concept of
process control obviously takes on a meaning that reflects the nature of its application. In spite of
the diverse meaning associated with the term “process control,” there is one rather basic idea that
tends to apply to all research situations. Process control research is the application of scientific
principles and methods to the study of the over-all system behaviour of the process.
Process control is a unique part of industrial production that deals with the control of variables
that in some way influence materials and equipment during the production of a product. In this
paper, it is the assessment of the rate of water flow through a water processing plant. Proper
2. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
2
process control for a bottled water production plant is concerned primarily with the control of rate
of water flow through the plant using modeling techniques. Process control model can be used in
the design of water treatment plants and prediction of treatment efficiency. It can also be used in
assessing the nature of flow through the plant.
The application of control theory has essentially two phases:
1.1 Dynamic analysis and Control system design.
The analysis phase is concerned with determination of the response of a plant to commands,
disturbances, and changes in the plant parameters.
If the dynamic response is satisfactory, there need be no second phase. If the response is
unsatisfactory and modification of the plant is unacceptable, a design phase is necessary to select
the control elements (the controller) needed to improve the dynamic performance to acceptable
levels [1]. Control theory itself has two categories:
1.1.1 Classical and Modern Control Theory.
Classical control theory, which had its start during World War II, can be characterized by the
transfer function concept with analysis and design principally in the Laplace and Frequency
domains.
Modern control theory has arisen with the advent of high-speed digital computers and can be
characterized by the state variables concept with emphasis on matrix algebra and with analysis
and design principally in the time domain. Modem control theory deals with the analysis and
synthesis of systems and devices for the control of complex multi-input multi-output systems. It
involves the application of mathematical techniques to analyze, synthesize, and optimize devices
for the control of a wide variety of systems. Modern control theory, despite its apparent
mathematical complexity, provides a unified approach to solving a wide variety of guidance and
control analysis, design, and optimization problems. The application of modern control theory to
the development of tactical guided weapons is a high interest, high potential technology.
Modem control theory is based on abstract mathematical concepts and its development uses a
system of notation and terminology largely incomprehensible to engineers and managers not
skilled in the art. As a body of knowledge, modern control theory encompasses all of classical
control system design, augmented with computational techniques largely developed over the past
two decades. Although these techniques are highly mathematical in nature, a knowledge of their
general approaches and some familiarity with their results is necessary to appreciate and
comprehend their intended applications.
A key notion in modem control theory is the use of a state variable model for the dynamic system.
The use of state variable methods has been all pervasive during the last two decades. The state
variables of a dynamic system are the smallest set of numbers which define the values of all
variables of interest relating to a dynamic system or mathematical model at a particular point in
time or space. State variable models are commonly applied in modern control theory to represent
dynamic systems and their components. The state variable technique is applicable to systems
described by linear or nonlinear continuous-time differential equations or discrete-time difference
equations. The main reason for the use of the state variable technique is that it permits the use of
matrix algebra and vector notation, resulting in highly compact mathematical descriptions of
modern control problems.
3. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
3
Modern Control theory can be used in
• Flight Control Systems, eg Modern commercial and military aircraft, Autoland systems,
and unmanned aerial vehicles (UAVs).
• Robotics, it can be used to study and design High accuracy positioning for flexible
manufacturing and Remote environments: space, sea, non-invasive surgery, etc.
• Chemical Process Control, modern control theory can be used to the Regulation of flow
rates, temperature, concentrations, etc. Can be used also in Long time scales, but only
crude models of process
• Heating Ventilation and Air Conditioning (HVAC) as Basic thermostatic controls to
building level systems.
• Automotive; it can be used to design Engine control, transmission control, cruise control,
climate control, etc
1.2 The underlying Control Theory.
Control systems have played, and are still playing a role of ever increasing importance in our
personal and industrial lives. This cannot be more evident than the areas they cover: from
timekeeping to transportation, and from pure mathematics to business and profit. The impact of
control on our daily lives is ubiquitous. Our homes are replete with control systems. They govern
water closet, water heater, refrigerators, washing machines, audio and video equipment, etc. The
subject of automatic controls is enormous, covering the control of variables such as temperature,
pressure, flow, level, and speed. Process control covers an area ranging from the control of a
simple domestic cooker to a complete production system or process, as may be found in a large
petrochemical complex [2].
Factories and processing plants cannot function without control systems. Control system has
played a vital role in the advance of engineering and science and has become an important and
integral part of modern manufacturing and industrial processes. In the processing industry,
controllers play a crucial role in keeping our plants running; virtually everything from simply
filling up a storage tank to complex separation processes and chemical reactors [3]. Control
systems are found in abundance in all sectors of industry such as process control and quality
control of manufacture of products, automatic assembly line tool control, space technology and
weapon systems, computer control, transportation systems, power systems, robotics and many
others. Control theory is very useful in technological applications, and is meeting an increasing
use in field such Economics and Sociology. Even such problems as inventory control and social
and economic systems control may be approached from the theory of control systems.
1.3 The Control Problem.
Over the years, man has constantly searched for reliable methods of controlling things around him
to suit his purpose. The successful operation of a system under changing conditions often requires
a control system. The flow of water in a Production Plant was previously monitored by having an
operator take a pressure reading in the treatment plant once or twice a day. Obviously, this daily
routine was wasteful and hardly accurate. It was impossible to maintain a stable flow even with
nearly continuous operator intervention. The correct amount of water needed to bottle or sachet
was very difficult, if not impossible, to ascertain from a pressure reading and thus overshooting of
desired flow was common [4].
4. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
4
Bottle or sachet water plants without process control are likely to experience problem such as the
water level in the filter cells in the tanks tend to fluctuate widely and create the potential for
partial drainage, overflow, and potential initial turbidity breakthrough at the beginning of the
filtration cycles thereby causes most of the products not to fill properly.
1.4 Analogous Systems
Use of the electric analog model in fluid flow is possible because of the mathematical similarity
between the flow of electricity in conducting materials and the flow of fluid in porous media [5],
[6], [7].
Electric analog methods are now regarded as one of the powerful computing tools available to the
hydrologist. Direct simulation of the hydrologic system by electrical methods simplifies the
computational process. Once the analog model is verified through the use of field data, all
electrical phenomena observed on the model can be directly related to hydrologic factors. Any
theoretical set of water flow conditions, including alternative solutions, can be modeled, and the
effects observed [8].
The electrical conductivity of the resistors is proportional to the hydraulic conductivity of the
plant, and the electrical capacitance is directly related to the storage coefficient of the plant. A
resistor impedes the flow of electricity in the same way as the piping materials impede the flow of
water through the plant; likewise, capacitor stores electricity in a manner similar to the way water
is stored in a tank.
If such a model is quantified, the electrical units of potential, charge, current, and model time
correspond to the hydraulic units of head, volume, flow rate, and real time. The concept of
analogous system is a very useful and powerful technique for system modeling [9].
To develop fully an analog model requires detailed analysis of the process parameters. The flow
system under equilibrium or steady-state conditions (before development) is described by a form
of law of conservation of mass (fluid flow continuity),
oi qqm −=& ………………………………………………………………………………… (1)
This simply states that the time rate of change m& of mass in a container must equal the total mass
inflow rate minus the total outflow rate. The quantity of water moving into the plant (recharge) is
about equal to the quantity of water moving out of the plant (discharge). Water levels in the plant
are a function of the magnitude of inflow and outflow and the characteristics of the materials
through which the water is moving.
In liquid flow system, the differential equation describing liquid flow is derived by law of
conservation of mass.
R
h
dt
dh
C = …………………………………………………………………………………. (2)
Where
C =storage coefficient (dimensionless)
5. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
5
dt
dh
=change in water level with time, in
metre per seconds.
h =water level, in metre
R =restriction in piping
t =time in hours
The equivalent equation for field in electricity is derived by Kirchhoff’s voltage and current laws
R
v
dt
dv
C = ……………..……………………………………………………………………. (3)
Where
C =electrical capacitance, in farads
dt
dv
=change in voltage with time
t =time, in seconds
R =electrical resistance, in ohms
v =electrical potential, in volts
The similarity between these two equations indicates that the cause-and-effect response in a
hydrologic system can be duplicated in an electrical system, provided the two are dimensionally
equivalent [8]. Mathematically, a solution to the response of developed plant requires use of
equations that are too complex for ordinary solution. However, an electric analog model can be
constructed that closely approximates the actual flow system because the flow of fluid through
porous media is analogous to the flow of current through conducting material.
A model that is quantitatively proportional to the liquid flow system (flow system) can be built by
selecting proper electrical components. Again, comparison of these two equations shows that the
behaviour of the two systems is determined by the same basic differential equation. The systems
are analogous and there is a one-one correspondence between the elements of the two systems.
The analogy is very close. The effect of variations in L, C and R on the water-levels in the
hydraulic system can thus be determined by simply observing the effect on the voltages in the
electric circuit of variation in L, C and R. The voltage across the capacitor and the current in the
circuit vary in the same way as the water-Level and velocity of the mass [10]. In complex water-
flow systems, it is impractical to measure all these parameters in great detail or with high
accuracy. If, however, they are known approximately, initial tests can be made with an analog
model. Usually model response on the first few trails bears little resemblance to actual water-level
changes. Through evaluation of model response and reconsideration of the original plant
parameters, the model design is revised until the water-level change computed by the model
agrees with observed changes [8].
6. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
6
However, electrical analogies have the advantage that they can be set up very easily in the
laboratory. A change in a particular parameter can be accomplished very easily in the electric
circuit to determine its overall effects and the electric circuit can be approximately adjusted for
the desired response. Afterwards, the parameters in the liquid level system can be adjusted by an
analogous amount to obtain the same desired response [11].
From figure 1, the Liquid – Levels, 1h , 2h , and 3h of the flow system are directly analogous to the
node voltages 1v , 2v and 3v of the electrical circuit in figure 2. Therefore, an electrical circuit is
analogous to fluid flow system/plant, implying that we can model the process control of a bottle
or sachet water plant by means of an electrical model.
Figure 1: Coupled tank flow System (Ani, 2007).
The electrical circuit representation of the system is shown below:
I1
R1
C1
R2
R3C2 C3
L1
vc1 vc2 vc3
Figure 2: Electrical Circuit Equivalent (Ani, 2007).
1.5 Mathematical Model
Interconnection of the elements imposes constraints on the variation of system variables, and the
convenient way of specifying these constraints is by a mathematical statement of the way in
7. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
7
which the various through-variables are related and the way in which the various across-variables
are related. This package of equations is a complete mathematical description of the system [12].
In this paper, a mathematical model describing the process control of bottle water plant is
desirable. Such a model may be used to establish the limits of flow system, as well as evaluate all
variables involved, leading to the optimization of the system parameters. Using principle of
analogy, which states that two different physical systems can be described by the same
mathematical model, these equations are converted into an Electrical circuit. This permits a
generalization of ideas specific to a particular field in order that a broader understanding of a
variety of apparently unrelated situations can be achieved. Using the method of analogy for which
Kirchhoff’s voltage and current laws are utilized, [4] obtain the state equation.
The network has four energy storage elements: three capacitors 321 ,, CCC , and an inductor L , and
this network are specified by the voltage across the capacitors and current through the inductor.
Since there are potential differences across the capacitors as well as current through the inductor,
this electrical picture leads to ordinary differential equations.
The differential equations governing the behaviour of the network are [4]:
( )
( )4C
1
211
1 LLLLLLLLLLLLLLLLLLLLLLLL
−
−=
R
vv
i
dt
vd ccc
( )
( )51
1
212
2 LLLLLLLLLLLLLLLLLLLLLLLLi
R
vv
dt
vd
C ccc
−
−
=
( ) ( )
( )6..32
2
2
1
LLLLLLLLLLLLLLLLLLLLLLcc
c
vv
dt
vd
R
L
dt
id
L −+=
( ) ( )
( )7.C
3
3
2
2
2
3
3 LLLLLLLLLLLLLLLLLLLLL
R
v
v
dt
vd
R
L
dt
vd c
c
cc
−+=
From equations (4-7) we have the process control equation in vector-matrix form, as [4]:
( )
( )
[ ] ( )8
0
0
0
C
1
CR
1
C
-
R
R-L
-
R
L
L
11
L
-
R
1
0
C
1
CR
1
CR
1
00
CR
1
CR
1
1
333223221
221
3221
22221
221
221
22121
1111
LLL
&
&
&
&
u
x
x
x
x
CR
L
CCR
CR
CCR
CRCRR
CRRL
CRx
x
x
x
4
3
2
1
+
−
−−
−
−−
−
=
4
3
2
1
The output equation is thus [4]:
8. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
8
[ ] ( )91000 LLLLLLLLLLLLLLLLLLLLLLLLLL
=
4
3
2
1
x
x
x
x
y
Where
The Analogous components and variables are thus:
ii = iQ (Input current; water flow into the process).
oi = oQ (Output current; water flow from the process).
1C = (Capacitance of capacitor 1; Capacity of tank 1 (sand treatment tank)).
2C = (Capacitance of capacitor 2; Capacity of tank 2 (carbon treatment tank)).
L = (Inductance of the inductor; filteration of the filter).
3C = (Capacitance of capacitor 3; Capacity of tank 3 (Treated water tank)).
1cv = 1h (voltage across capacitor 1; water level of tank 1).
2cv = 2h (voltage across capacitor 2; water level of tank 2).
3cv = 3h (voltage across capacitor 3; water level of tank 3).
1i = 1Q (flow of current through the inductor; rate of water flow through the filter).
1R = (Resistance of the resistor 1; Restriction of pipe 1).
2R = (Resistance of the resistor 2; Restriction of pipe 2).
3R = (Resistance of the resistor 3; Restriction of pipe 3).
From the model equation above, output y depends on the state equation x on 4x i.e. 4xy = .
Therefore, x and y are dependent variables to be simulated.
The equation to be solved is:
uBxAx ** +=& …………………………………………………………………………. (10)
Cxy = ………………………………………………………………………………….... (11)
9. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
9
The above equations contain all that we need to know about the theory of process control (flow)
in a water processing plant that is in study. Solving the time domain ( ) ( ) ( )tButAxtX +=& and
)()()( tDutCxtY += while D=0 using Ordinary Differential Equation (ODE 45) of MATLAB
Software.
1.6 Simulation of the Model Equation using MATLAB.
A program was developed to compute and study the effect of the state variables on the system
during transient state. To determine the conditions that must exist for the system to work
efficiently, trial values for the various parameters of the process control model were taken to
simulate the process of water flow in the system. More than 150 trial simulations were conducted
and the computed variables were picked and presented in this report.
Each group of simulation had a specific scenario in which a parameter was varied while the others
were kept constant. The parameters, the values of which were varied include Resistance,
Capacitance and Inductance. The variables ( 1cv , 2cv , i , 3cv ) representing the responses (water
levels) of individual sections of the process were tabulated.
1.7 Results
The results of the simulation in tabular form are given below:
Table 1: Table of values with 1R Varied while other parameters were kept constant
Table 2: Table of values with 2R Varied while other parameters were kept constant
Table 3: Table of values with 3R Varied while other parameters were kept constant
10. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
10
Table 4: Table of values with L Varied while other parameters were kept constant
Table 5: Table of values with 1C Varied while other parameters were kept constant
Table 6: Table of values with 2C Varied while other parameters were kept constant
Table 7: Table of values with 3C Varied while other parameters were kept constant
1.8. The Analysis of the Results of the Simulation.
The analysis of the results of the seven (7) simulations made by varying the values of the
parameters of the model, one after the other, and the interpretations of the observed values of the
variables ( 1cv , 2cv , i , and 3cv ) as related to the rate of water flow, are given below:
1.8.1. First Simulation
An increase in the Restriction ( 1R ) to flow between tanks 1 and 2 (see plant), while keeping the
other parameters constant, caused reasonable increase in the water level of tank 1, while water
11. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
11
levels of tanks 2, 3 decreased and flow ( 1i ) through the filter ( L ) also decreased as shown in
table 1. In essence, this may be regarded as attempting to fine tune the process so as to influence
the overall rate of water flow into the bottle (since varying 1R has an effect on the water levels of
tanks 1, 2, 3 and water flow ( 1i ) through the filter).
1.8.2. Second Simulation
An increase in the restriction ( 2R ) of water flow between tank 2 and filter (while keeping the
other parameters constant) caused reasonable increase in water levels of tanks 1, 2, with tank 2
affected most, while the flow of water through the filter and the water level of tank 3 decreased as
shown in table 2. This could be interpreted as meaning that an increase in 2R decreased the water
levels in tank 3, thereby decreasing the water flow into the bottle. As stated above, this can also
be interpreted as attempting to fine tune the process so as to influence the overall rate of water
flow into the bottle.
1.8.3. Third simulation
An increase in the restriction, ( 3R ) (while keeping the other parameters constant) caused an
increase in the water levels of all the tanks 1, 2, 3 [minimal changes (increase) in water levels of
tanks 1, 2 and reasonable changes (increase) in water level of tank 3], while flow of water through
the filter decreased as shown in table 3. The essence of this is to fine tune the process so as to get
the right flow of water from the plant into the bottle. More specifically, this parameter is used to
control the rate of water flow into the bottle.
1.8.4. Fourth simulation
Here varying L (the inductance) and keeping other parameters constant caused minimal changes
(increase) in the water levels of all the tanks 1, 2, 3, while the flow ( 1i ) of water through the filter
decreased showing that at high filtration, the flow of water through the filter lessened (decreased)
as shown in table 4. This could be interpreted as indicating that at high filtration (indicating
increase in number of wound string of the conductor), the flow ( 1i ) of water through the filter
decreases, thereby affecting the overall output of water flow into the bottles.
1.8.5. Fifth simulation
Varying 1C (increasing the capacity of tank 1) and keeping other parameters constant caused
changes (decrease) in all the water levels of tanks 1, 2, 3 and flow ( 1i ) through the filter as shown
in table 5. The essence of this variation is to study the effects of 1C (capacity of tank 1) on the
process (flow of water into the bottle) and its interaction with the other parameters of the model.
In this circumstance, the increase in the value of 1C could be interpreted as decreasing the rate of
water flow into the bottles.
1.8.6. Sixth simulation
Varying 2C (capacity of tank 2) and keeping other parameters constant caused changes
(decrease) in all the water levels of tanks 1, 2, 3 and flow ( 1i ) through the filter as shown in table
12. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
12
6. This is also to study the effects of 2C (capacity of tank 2) on the process (flow of water into
the bottle) and its interaction with the other parameters of the model.
1.8.7. Seventh simulation
Varying 3C (capacity of tank 3) and keeping other parameters constant caused changes
(decrease) in all the water levels of tanks 1, 2, 3, while it caused an increase in flow ( 1i ) through
the filter, showing that any variation of the capacity of tank 3 affects all the water levels of tanks
1, 2, 3 (decrease) and flow ( 1i ) of water through the filter (increase) as shown in table 7. The
essence of this variation is to study the effects of 3C (capacity of tank 3) to the process (flow of
water into the bottle) and its interaction with other parameters of the model.
1.9. OBSERVATIONS
1.9.1 The connecting pipes:
We observed that varying the restriction of the connecting pipes implies that either the radius
(size) of the pipe is decreased/increased or the length of the pipe is increased/decreased which
significantly influences the flow of water through the filter, but caused varying effects on the
water levels in all the tanks. It is an attempt to fine tune the process so as to influence the rate of
water flow (to get the right water flow from the plant).
1.9.2. The capacity of the tanks:
We observed that varying the value of the capacities (capacitances) of any of the tanks in the
process significantly influences (decreases/increases) the water levels in all the tanks, but caused
varying effects on the flow of water through the filter. This implies that an increase in the value of
C (capacity of tank) decreases the water level ( h ) in the tank and hence the rate of water flow in
the process, while a decrease in the value of C (capacity of tank) increases h and hence the rate
of water flow in the process depending on the type and size of pipe used which must obey the
relations stated in equations 4 - 7.
1.9.3. Inductance of the filter:
We observed that varying the inductance ( L ) of the filter influences the water levels in all the
tanks, but caused changes (decrease) in the flow of water through the filter, implying that an
increase in the inductance ( L ) (number of wound string on the filter cartridge) increases
filteration. An increase in filteration tends to decrease the flow of water through the filter, and this
in turn tends to influence the rate of water flow in the process.
CONCLUSIONS
This work presents an electrical model for studying the process behaviour of bottled water
production plant. In this model, the mechanical components of the plant such as the connecting
pipes, the water tanks and the water filter were represented by resistors, capacitors and an
inductor, respectively. A state equation was developed as a mathematical model of the electrical
circuit. In this equation, resistors, capacitors and inductor, representing the restriction of the pipes,
the capacity of the tanks and the filtration of the filter, respectively, were used as variable
parameters to generate the state variables (voltages/potential drops and current in the electrical
13. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
13
model (circuit)) of the state equation (mathematical model). This mathematical model was used to
simulate the effects of varying the electrical parameters ( R ,C , and L ) on the state variables
(voltage, v , and current,i ) representing the restriction (as function of the diameter and length) of
the connecting pipes, the water levels ( h ) in the water tanks and the filtration of the water filter,
respectively. From the Analysis, it was observed that by varying the electrical parameters
(resistors, capacitors, inductor), it is possible to study the way the manipulation of equivalent
parameters of the analogous mechanical components (the connecting pipes, the water tanks and
the water filter) of the sachet water plant could influence the rate of water flow in the production
process of bottled water. This model describes the natural process, yields reproducible results
when solved with simulation software and, consequently, used for predictive purposes in bottled
water production plants.
REFERENCES
[1]. Nemeth. H (2004) Nonlinear modeling and control for a Mechatronic Protection valve. Theses of
Ph.D dissertation, Budapest University of Technology and Economics, Hungary, pg 4-6,
[2]. Spirax Sarco - learning centre: An Introduction to Controls.
www.spiraxsarco.com/learn/html/5_1_01.htm
[3]. Neelamkavil. F. (1987) Computer Simulation and Modeling. John Wiley and sons Ltd., New York,
NY, pp.22-39,
[4]. Ani, V.A. (2007). Simulation of a Sachet Water Processing Plant Using an Analogous Electrical
Model. (M.Sc thesis) Department of Electronic Engineering, University of Nigeria Nsukka
[5]. Skibitzke. E.H. (1960) Electronic Computer as an aid to the Analysis of Hydrologic Problems:
Gentbrugge, Belgium, Internat.Assoc.Sci.Hydrology, Comm.Subterranean waters, Pub.52, pg.347-
358.
[6]. Walton, C.W. and Prickett, A.T. (1963) Hydrogeologic Electric Analog Computer: Am.Soc.Civil
Engineers, Jour.Hydraulics Div., V.89, no.HY6, Proc.Paper 3695, pg 67-91.
[7]. Patten, E.P (1965) Design, Construction, and use of electric analog models in A.L.wood and
K.R.Gabrysch. Analog model study of ground water in the Houston District, Texas: Texas water
Comm.Bull.6508, pg 41-60.
[8]. Hardt, F.W. (1971) Hydrologic Analysis of Mojave River Basin, California, using Electric Analog
Model, U.S.Geol.Survey Open-file rept., 97.
[9]. Dorf, C.Richard and.Bishop, H.Robert (1998) “Modern Control Systems”. Addison-Wesley
Longman, Inc., New York, 8th
edition, pg 38,
[10]. Ord-Smith, R.J and Stephenson, J. (1975) Computer Simulation of Continuous Systems. Cambridge
University Press, London.
[11]. Nzeako. A.N (2006) “Control Engineering” Lecture note on ECE 451, Department of Electronic
Engineering, University of Nigeria, Nsukka].
[12]. Basualdo, M.S. (1990) Ph. D Thesis ``Dynamic and Control of Distillation Columns’’, INTEC-
CONICET-UNL,
[13]. Douglas M.Considine. Process/Industrial Instruments & Controls Handbook. Mc Graw-Hill, Inc.,
USA. 4th
edition. 1993.
[14]. Dynamic Simulation of Chemical Process as a Tool to Teach “The Real Problem” of Identification
and Control. http://fie.engrng.pitt.edu/fie95/3b3/3b31/3b31.htm
[15]. F.A.Abott. Ordinary Level Physics. Heinemann Educational Books London. 1979.
APPENDIX
Expressions for determining the parameters of the system.
Derivation of Resistance R.
From Ohm’s law
14. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
14
i
v
R = --------------------------------------------------------------- (A1)
Also, Resistivity for material that obeys Ohm’s law is given as
J
E
=ρ -------------------------------------------------------------- (A2)
where
E =Electric field of a point,
J =the current density
If total current in the material is i and potential difference between plate isv . Also assume the
material is of uniform cross-sectional area A and lengthl .
Elv = ---------------------------------------------------------------- (A3)
JAi = ---------------------------------------------------------------- (A4)
Substituting equation (A3) and (A4) into equation (A2)
÷
=
A
i
l
v
ρ
A
i
l
v ρ
= ----------------------------------------------------------------- (A5)
R
A
l
i
v
==
ρ
A
l
R
ρ
=∴ [13], [14].
Where
ρ =Resistivity of material
A =cross-sectional Area
l =length of material
i =current
v =potential difference
R =Resistance
Derivation of Inductance L.
i
v
L =
15. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
15
v =induced emf
Induced emf
t
N
∆
∆
=
φ
ε
since i vary with time.
t
i
i
∆
∆
= .
i
t
t
N
L
∆
∆
×
∆
∆
=∴
φ
i
N
L
∆
∆
=
φ
---------------------self induction
But flux φφ cosBA=∆
Since 0=φ
i
NBA
L = --------------------------------------------------------------equation (A6)
From Bio-savart law
l
Ni
B roµµ
= -------------------------------------------------------------equation (A7)
Substituting the above (equation (A7)) into equation (A6)
( )
l
AN
il
ANiN
L roro µµµµ 2
==
Where
dl π2=
d
AN
L ro
π
µµ
2
2
=∴ [15].
Where
=oµ Permeability of free space
=rµ Relative Permeability of material
16. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
16
=N Number of turns
=l Mean length of coil
=A Surface area density
= 2
4 rπ
=B Magnetic field
Derivation of Capacitance C.
v
Q
C =
( )tdEtEv −+= 21
Where
ro
D
E
εε
=1
(in medium);
o
D
E
ε
=2
(in air).
Flux density
A
Q
D = ----------------------------------------------------------- (A8)
( )
−+=−+= td
tD
td
D
t
D
v
rooro εεεεε
−+= td
tD
v
ro εε
DAQ =
−−
×=
−+
==
r
o
ro
t
td
D
DA
td
tD
DA
v
Q
C
ε
ε
εε
1
−−
=
r
o
t
td
A
C
ε
ε
−−
==∴
r
o
t
td
A
v
Q
C
ε
ε
The above equation is formula for a capacitance having dielectric partly air and partly some other
medium [15].
17. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
17
d
A
v
Q
C oε
==
The above equation is formula for a capacitance having only air as dielectric.
Where
=E Electric intensity
=Q Electric charge
=t Thickness of the parallel plate
=D Flux density
=rε Relative Permittivity of the material
=oε Permittivity of air/vacuum
=d Height difference between the positive & negative side.
=A Area of the parallel plate
=C Capacitance of the capacitor
Capacitance:
−−
==
r
o
t
td
A
v
Q
C
ε
ε
The above equation is formula for a capacitance having dielectric partly air and partly some other
medium.
d
A
v
Q
C oε
==
The above equation is formula for a capacitance having only air as dielectric.
Where
=t Thickness of the parallel plate (thickness of the sand / carbon layer) =41inches for
sand & 45 inches for carbon.
=rε Relative Permittivity of the material (relative permittivity of sand WRT water /
relative permittivity of carbon WRT water).
=oε Permittivity of air/vacuum = (8.85x10-12
)
=d Height difference between the positive & negative side (distance from top to
bottom of the tank (total length)) =61.5.
r =radius of the parallel plate (radius of the tank) = varied.
=A Area of the parallel plate (Area of the tank) = 2
4 rπ .
=C Capacitance of the capacitor (Capacitance of the tank)
Relative Permittivity
( )
o
k
r
ε
ε
ε =
Where
18. International Journal of Control Theory and Computer Modeling (IJCTCM) Vol.3, No.1, January 2013
18
kε =Permittivity dielectric constant
oε =Permittivity of vacuum.
The relative Permittivity of sand with respect to water is thus:
( )
kw
o
o
ks
o
kw
o
ks
swr
ε
ε
ε
ε
ε
ε
ε
ε
ε ×==
( )
kw
ks
swr
ε
ε
ε =
where
( )swrε =Relative permittivity of sand with respect to water
ksε =Permittivity dielectric constant of sand.
kwε =Permittivity dielectric constant of water.
The relative Permittivity of carbon with respect to water is thus:
( )
kw
o
o
kc
o
kw
o
kc
cwr
ε
ε
ε
ε
ε
ε
ε
ε
ε ×==
( )
kw
kc
cwr
ε
ε
ε =
( )cwrε = Relative permittivity of carbon with respect to water.
kcε =Permittivity dielectric constant of carbon.
kwε =Permittivity dielectric constant of water.
Author’s Biography
Mr Vincent Anayochukwu Ani is a Ph.D candidate at the Department of Electronic
Engineering, University of Nigeria, Nsukka [UNN], Nigeria, where he also received his M.Sc
Degree in Control Engineering. He is a member of a Team of Researchers from three
universities, [University of Nigeria, Nsukka (UNN), Cross River University of Technology
(CRUTECH) and Ambrose Alli University, Ekpoma (AAU)] on Modeling, Simulation and
Optimization. e-mail: vincent_ani@yahoo.com