This paper presents a novel contribution of a low complexity control scheme for voltage control of a dynamic voltage restorer (DVR). The scheme proposed utilizes an error-driven proportional-integral-derivative (PID) controller to guarantee better power quality performance in terms of voltage enhancement and stabilization of the buses, energy efficient utilization, and harmonic distortion reduction in a distribution network. This method maintains the load voltage close to or equal to the nominal value in terms of various voltage disturbances such as balanced and unbalanced sag/swell, voltage imbalance, notching, different fault conditions as well as power system harmonic distortion. A grasshopper optimization algorithm (GOA) is used to tune the gain values of the PID controller. In order to validate the effectiveness of the proposed DVR controller, first, a fractional order PID controller was presented and compared with the proposed one. Further, a comparative performance evaluation of four optimization techniques, namely Cuckoo search (CSA), GOA, Flower pollination (FBA), and Grey wolf optimizer (GWO), is presented to compare between the PID and FOPID performance in terms of fault conditions in order to achieve a global minimum error and fast dynamic response of the proposed controller. Second, a comparative analysis of simulation results obtained using the proposed controller and those obtained using an active disturbance rejection controller (ADRC) is presented, and it was found that the performance of the optimal PID is better than the performance of the conventional ADRC. Finally, the effectiveness of the presented DVR with the controller proposed has been assessed by time-domain simulations in the MATLAB/Simulink platform.
This presentation gives the information about introduction to control systems
Subject: Control Engineering as per VTU Syllabus of Aeronautical Engineering.
Notes Compiled By: Hareesha N Gowda, Assistant Professor, DSCE, Bengaluru-78.
Disclaimer:
The contents used in this presentation are taken from the text books mentioned in the references. I do not hold any copyrights for the contents. It has been prepared to use in the class lectures, not for commercial purpose.
This paper presents a novel contribution of a low complexity control scheme for voltage control of a dynamic voltage restorer (DVR). The scheme proposed utilizes an error-driven proportional-integral-derivative (PID) controller to guarantee better power quality performance in terms of voltage enhancement and stabilization of the buses, energy efficient utilization, and harmonic distortion reduction in a distribution network. This method maintains the load voltage close to or equal to the nominal value in terms of various voltage disturbances such as balanced and unbalanced sag/swell, voltage imbalance, notching, different fault conditions as well as power system harmonic distortion. A grasshopper optimization algorithm (GOA) is used to tune the gain values of the PID controller. In order to validate the effectiveness of the proposed DVR controller, first, a fractional order PID controller was presented and compared with the proposed one. Further, a comparative performance evaluation of four optimization techniques, namely Cuckoo search (CSA), GOA, Flower pollination (FBA), and Grey wolf optimizer (GWO), is presented to compare between the PID and FOPID performance in terms of fault conditions in order to achieve a global minimum error and fast dynamic response of the proposed controller. Second, a comparative analysis of simulation results obtained using the proposed controller and those obtained using an active disturbance rejection controller (ADRC) is presented, and it was found that the performance of the optimal PID is better than the performance of the conventional ADRC. Finally, the effectiveness of the presented DVR with the controller proposed has been assessed by time-domain simulations in the MATLAB/Simulink platform.
This presentation gives the information about introduction to control systems
Subject: Control Engineering as per VTU Syllabus of Aeronautical Engineering.
Notes Compiled By: Hareesha N Gowda, Assistant Professor, DSCE, Bengaluru-78.
Disclaimer:
The contents used in this presentation are taken from the text books mentioned in the references. I do not hold any copyrights for the contents. It has been prepared to use in the class lectures, not for commercial purpose.
What Product/s do you want to produce?
•What will your capacity be?
•Milk Quality/Juice with preservatives?
•How Flexible do you want to be?
•Will you contract pack?
•Product Shelf Life/cold chain
Batch Distillation
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 BACKGROUND TO THE DESIGN
4.1 General
4.2 Choice of batch/continuous operation
4.3 Boiling point curve and cut policy
4.4 Method of design
4.5 Scope of calculations required for design
5 SIMPLE BATCH DISTILLATION
6 FRACTIONAL BATCH DISTILLATION
6.1 General
6.2 Approximate methods
6.3 Rigorous design - use of a computer model
6.4 Other factors influencing the design
6.4.1 Occupation
6.4.2 Choice of Batch Rectification or Stripping
6.4.3 Batch size
6.4.4 Initial estimate of cut policy
6.4.5 Liquid Holdup
6.4.6 Total reflux operation and heating-up time
6.4.7 Column operating pressure
6.5 Optimum Design of the Batch Still
6.6 Special design problems
7 GENERAL ASPECTS OF EQUIPMENT DESIGN
7.1 Kettle reboilers
7.2 Column Internals
7.3 Condensers and reflux split boxes
8 PROCESS CONTROL AND INSTRUMENTATION IN
BATCH DISTILLATION
9 MECHANICAL DESIGN FEATURES
10 BIBLIOGRAPHY
APPENDICES
A McCABE - THIELE METHOD - TYPICAL EXAMPLE
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Design and Simulation of Continuous Distillation ColumnsGerard B. Hawkins
Design and Simulation of Continuous Distillation Columns
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 FRACTIONAL DISTILLATION
5 ROUGH METHOD OF COLUMN DESIGN
5.1 Sharp Separations
5.2 Sloppy Separations
6 DETAIL DESIGN USING THE CHEMCAD DISTILLATION PROGRAM
6.1 Sharp Separations
6.2 Sloppy Separations
7 COMPLEX COLUMNS
7.1 Multiple Feeds
7.2 Sidestream Take-Offs
8 DESIGN USING A LABORATORY COLUMN
SIMULATION
9 DESIGN USING ACTUAL PLANT DATA
9.1 Uprating or Debottlenecking Exercises
10 REFERENCES
APPENDICES
A WORKED EXAMPLE
B SLOPPY SEPARATIONS
C SIMULATION USING PLANT DATA : CASE HISTORIES
TABLES
Control of Continuous Distillation Columns
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 GENERAL DESCRIPTION OF A DISTILLATION COLUMN
5 REGULATORY CONTROL
5.1 Composition Control
5.2 Mass Balance Control
5.3 Design of Feedback Control Systems
5.4 Pressure and Condensation Control
5.5 Reboiler Control
6 DISTURBANCE COMPENSATION
6.1 Feed-forward Control
6.2 Cascade Control
6.3 Internal Reflux Control
7 CONSTRAINT CONTROL
7.1 Override Controls
7.2 Flooding
7.3 Limiting Control
8 MORE ADVANCED TOPICS
8.1 Temperature Position Control
8.2 Inferential Measurement
8.1 Floating Pressure Control
8.2 Model Based Predictive Control
8.1 Control of Side-streams
8.2 Extractive/Azeotropic Systems
9 REFERENCES
TABLES
1 SYMPTOMS OF IMBALANCE AND THE REGULATORY VARIABLES
2 PRACTICAL LINKAGES BETWEEN CONTROL
(P, R, B, C) AND REGULATION VARIABLES
(h, r, d, b, c, v)
3 COMPOSITION REGULATION
4 COMPOSITION REGULATION - VERY SMALL FLOWS
Innovative energy-saving technology of distillation in the tray column. Its use in an industrial environment has resulted in a 2 to 4 times more efficient separation of components and a reduction of 1.5 to 2 times the power input into the process.
Practical Advanced Process Control for Engineers and TechniciansLiving Online
In today's environment, the processing, refining and petrochemical business is becoming more and more competitive and every plant manager is looking for the best quality products at minimum operating and investment costs. The traditional PID loop is used frequently for much of the process control requirements of a typical plant. However there are many drawbacks in using these, including excessive dead time which can make the PID loop very difficult (or indeed impossible) to apply.
Advanced Process Control (APC) is thus essential today in the modern plant. Small differences in process parameters can have large effects on profitability; get it right and profits continue to grow; get it wrong and there are major losses. Many applications of APC have pay back times well below one year. APC does require a detailed knowledge of the plant to design a working system and continual follow up along the life of the plant to ensure it is working optimally. Considerable attention also needs to be given to the interface to the operators to ensure that they can apply these new technologies effectively as well.
WHO SHOULD ATTEND?
Automation engineers
Chemical engineers
Chemical plant technologists
Electrical engineers
Instrumentation and control engineers
Process control engineers
Process engineers
Senior technicians
System integrators
MORE INFORMATION: http://www.idc-online.com/content/practical-advanced-process-control-engineers-and-technicians-26
New Techniques of wastewater ManagementPrashant Ojha
Wastewater treatment broadly describes water treatment preparing water no longer needed or suitable for its most recent use for return to the water cycle with minimal environmental issues. Wastewater treatment is distinguished from water treatment by focus on disposal rather than use. Water reclamation implies avoidance of disposal by use of wastewater as a raw water supply. Treatment means removing impurities from water being treated; and some methods of treatment are applicable to both water and wastewater. Production of waste brine, however, may discourage wastewater treatment removing dissolved inorganic solids from water by methods like ion exchange, reverse osmosis, and distillation.
Electrocoagulation (EC), is a rapidly growing area of wastewater treatment, less well known as radio frequency diathermy or short wave electrolysis, is a technique used for wash water treatment, wastewater treatment, industrial processed water, and medical treatment. Electricity-based electrocoagulation technology removes contaminants that are generally more difficult to remove by filtration or chemical treatment systems, such as emulsified oil, total petroleum hydrocarbons, refractory organics, suspended solids, and heavy metals. There are many brands of electrocoagulation devices available and they can range in complexity from a simple anode and cathode to much more complex devices with control over electrode potentials, passivation, anode consumption, cell REDOX potentials as well as the introduction of ultrasonic sound, ultraviolet light and a range of gases and reactants to achieve so-called Advanced Oxidation Processes for refractory or recalcitrant organic substances.
OBDII data generated by a vehicle sensor network can be considered as a canonical proxy for Industrial IoT. Vehicle analytics, in addition to being useful in its own right (e.g. vehicle health monitoring, diagnostics, driver behavior modeling etc.), exhibits the same data characteristics (e.g. highly nonlinear data that varies rapidly in real-time, time delay effects in the data etc.) as an Industrial IoT application. In this case study, we demonstrate our analytics capabilities on a passenger car OBDII data. In particular, we demonstrate how one can use an "AI sensor" - a prediction system in places where no direct sensor measurement is available. Anand Deshpande Aniruddha Pant
What Product/s do you want to produce?
•What will your capacity be?
•Milk Quality/Juice with preservatives?
•How Flexible do you want to be?
•Will you contract pack?
•Product Shelf Life/cold chain
Batch Distillation
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 BACKGROUND TO THE DESIGN
4.1 General
4.2 Choice of batch/continuous operation
4.3 Boiling point curve and cut policy
4.4 Method of design
4.5 Scope of calculations required for design
5 SIMPLE BATCH DISTILLATION
6 FRACTIONAL BATCH DISTILLATION
6.1 General
6.2 Approximate methods
6.3 Rigorous design - use of a computer model
6.4 Other factors influencing the design
6.4.1 Occupation
6.4.2 Choice of Batch Rectification or Stripping
6.4.3 Batch size
6.4.4 Initial estimate of cut policy
6.4.5 Liquid Holdup
6.4.6 Total reflux operation and heating-up time
6.4.7 Column operating pressure
6.5 Optimum Design of the Batch Still
6.6 Special design problems
7 GENERAL ASPECTS OF EQUIPMENT DESIGN
7.1 Kettle reboilers
7.2 Column Internals
7.3 Condensers and reflux split boxes
8 PROCESS CONTROL AND INSTRUMENTATION IN
BATCH DISTILLATION
9 MECHANICAL DESIGN FEATURES
10 BIBLIOGRAPHY
APPENDICES
A McCABE - THIELE METHOD - TYPICAL EXAMPLE
We are manufacturers of quartz water distillation unit, water distillation unit suppliers, quartz water distillation plant, water distillation plants India, steel water distillation plant India, quartz water distillation unit India, water distillation unit manufacturers India, water distillation plants suppliers. For More Information Please Logon http://cutt.us/vSgb
Design and Simulation of Continuous Distillation ColumnsGerard B. Hawkins
Design and Simulation of Continuous Distillation Columns
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 FRACTIONAL DISTILLATION
5 ROUGH METHOD OF COLUMN DESIGN
5.1 Sharp Separations
5.2 Sloppy Separations
6 DETAIL DESIGN USING THE CHEMCAD DISTILLATION PROGRAM
6.1 Sharp Separations
6.2 Sloppy Separations
7 COMPLEX COLUMNS
7.1 Multiple Feeds
7.2 Sidestream Take-Offs
8 DESIGN USING A LABORATORY COLUMN
SIMULATION
9 DESIGN USING ACTUAL PLANT DATA
9.1 Uprating or Debottlenecking Exercises
10 REFERENCES
APPENDICES
A WORKED EXAMPLE
B SLOPPY SEPARATIONS
C SIMULATION USING PLANT DATA : CASE HISTORIES
TABLES
Control of Continuous Distillation Columns
0 INTRODUCTION/PURPOSE
1 SCOPE
2 FIELD OF APPLICATION
3 DEFINITIONS
4 GENERAL DESCRIPTION OF A DISTILLATION COLUMN
5 REGULATORY CONTROL
5.1 Composition Control
5.2 Mass Balance Control
5.3 Design of Feedback Control Systems
5.4 Pressure and Condensation Control
5.5 Reboiler Control
6 DISTURBANCE COMPENSATION
6.1 Feed-forward Control
6.2 Cascade Control
6.3 Internal Reflux Control
7 CONSTRAINT CONTROL
7.1 Override Controls
7.2 Flooding
7.3 Limiting Control
8 MORE ADVANCED TOPICS
8.1 Temperature Position Control
8.2 Inferential Measurement
8.1 Floating Pressure Control
8.2 Model Based Predictive Control
8.1 Control of Side-streams
8.2 Extractive/Azeotropic Systems
9 REFERENCES
TABLES
1 SYMPTOMS OF IMBALANCE AND THE REGULATORY VARIABLES
2 PRACTICAL LINKAGES BETWEEN CONTROL
(P, R, B, C) AND REGULATION VARIABLES
(h, r, d, b, c, v)
3 COMPOSITION REGULATION
4 COMPOSITION REGULATION - VERY SMALL FLOWS
Innovative energy-saving technology of distillation in the tray column. Its use in an industrial environment has resulted in a 2 to 4 times more efficient separation of components and a reduction of 1.5 to 2 times the power input into the process.
Practical Advanced Process Control for Engineers and TechniciansLiving Online
In today's environment, the processing, refining and petrochemical business is becoming more and more competitive and every plant manager is looking for the best quality products at minimum operating and investment costs. The traditional PID loop is used frequently for much of the process control requirements of a typical plant. However there are many drawbacks in using these, including excessive dead time which can make the PID loop very difficult (or indeed impossible) to apply.
Advanced Process Control (APC) is thus essential today in the modern plant. Small differences in process parameters can have large effects on profitability; get it right and profits continue to grow; get it wrong and there are major losses. Many applications of APC have pay back times well below one year. APC does require a detailed knowledge of the plant to design a working system and continual follow up along the life of the plant to ensure it is working optimally. Considerable attention also needs to be given to the interface to the operators to ensure that they can apply these new technologies effectively as well.
WHO SHOULD ATTEND?
Automation engineers
Chemical engineers
Chemical plant technologists
Electrical engineers
Instrumentation and control engineers
Process control engineers
Process engineers
Senior technicians
System integrators
MORE INFORMATION: http://www.idc-online.com/content/practical-advanced-process-control-engineers-and-technicians-26
New Techniques of wastewater ManagementPrashant Ojha
Wastewater treatment broadly describes water treatment preparing water no longer needed or suitable for its most recent use for return to the water cycle with minimal environmental issues. Wastewater treatment is distinguished from water treatment by focus on disposal rather than use. Water reclamation implies avoidance of disposal by use of wastewater as a raw water supply. Treatment means removing impurities from water being treated; and some methods of treatment are applicable to both water and wastewater. Production of waste brine, however, may discourage wastewater treatment removing dissolved inorganic solids from water by methods like ion exchange, reverse osmosis, and distillation.
Electrocoagulation (EC), is a rapidly growing area of wastewater treatment, less well known as radio frequency diathermy or short wave electrolysis, is a technique used for wash water treatment, wastewater treatment, industrial processed water, and medical treatment. Electricity-based electrocoagulation technology removes contaminants that are generally more difficult to remove by filtration or chemical treatment systems, such as emulsified oil, total petroleum hydrocarbons, refractory organics, suspended solids, and heavy metals. There are many brands of electrocoagulation devices available and they can range in complexity from a simple anode and cathode to much more complex devices with control over electrode potentials, passivation, anode consumption, cell REDOX potentials as well as the introduction of ultrasonic sound, ultraviolet light and a range of gases and reactants to achieve so-called Advanced Oxidation Processes for refractory or recalcitrant organic substances.
OBDII data generated by a vehicle sensor network can be considered as a canonical proxy for Industrial IoT. Vehicle analytics, in addition to being useful in its own right (e.g. vehicle health monitoring, diagnostics, driver behavior modeling etc.), exhibits the same data characteristics (e.g. highly nonlinear data that varies rapidly in real-time, time delay effects in the data etc.) as an Industrial IoT application. In this case study, we demonstrate our analytics capabilities on a passenger car OBDII data. In particular, we demonstrate how one can use an "AI sensor" - a prediction system in places where no direct sensor measurement is available. Anand Deshpande Aniruddha Pant
Improving continuous process operation using data analytics delta v applicati...Emerson Exchange
Quality parameters are available through lab measurements and the final product quality changes may go undetected until a lab sample is taken. Continuous data analytics tool provided on-line prediction of quality parameters and fault detection. Field trial results from a carbon dioxide absorption/stripping process at the UT/Austin Separations Research Program will be presented in this workshop.
Intelligent Production: Deploying IoT and cloud-based machine learning to opt...Amazon Web Services
Alex Robart, CEO of Ambyint, presents their AI-driven production optimization platform for the Oil and Gas Industry.
Their IoT-based innovative hardware and software solution, delivers a revolutionary approach to monitoring Oil and Gas production operations, by updating traditional SCADA-based telemetry, cloud-enabling them, and bringing in Artificial Intelligence capabilities. Presented at the AWS Oil and Gas Industry Day in Calgary, 2017.
Advances In Digital Automation Within RefiningJim Cahill
Emerson's Tim Olsen presents to Argentinean refiners on the changes in automation technologies and how they are being applied to improve efficiency and reduce costs.
Permanent magnet direct current motors (PMDCM) are widely used in various applications such as space technologies, personal computers, medical, military, robotics, electrical vehicles, etc. In this paper, the mathematical model of PMDCM is designed and simulated using MATLAB software. The PMDCM speed is controlled using rate feedback controller due to its ability of improving system damping. To improve the controller performance, it’s parameters are tuned using genetic algorithm (GA) and direct search (DS) techniques. The tuning process based on different performance criteria. The most four common performance criteria used in this paper are JIAE (Integral of Absolute Error), JISE (Integral of Square Error), JITAE (Integral of Time-Weighted Absolute Error), and JITSE (Integral of Time-Weighted Square Error). The results obtained from these evolutionary techniques are compared. The results show an obvious improvement in system performance including enhancing the transient and steady state of PMDCM speed responses for all performance criteria.
Quadrotor control is needed so that the quadrotor can float close to the stationary state. For that we need control techniques. One control technique that can be designed and implemented in quadrotor is PID control. PID parameter tuning using the Genetic Algorithm technique can speed up the manual tuning process. The weakness in the application of the Genetic Algorithm rule is that it often rejects important information found in other individuals and causes premature convergence, especially at the beginning of the generation. These problems can be overcome by using crossover and mutation rules with different probability levels according to fitness values and evolutionary processes. The results of the study using fast genetic algorithm techniques obtained constants Kp, Ki and Kd with the lowest rise time and overshoot, namely 0.010, 0.001 and 0.036 at the pitch angle. At the roll angle, they are 0.010, 0.001 and 0.03. At yaw angle 0.018, 0.006 and 0.043. Comparison of PID tuning simulations using fast genetic algorithm with genetic algorithm standards, shows that fast genetic algorithm has increased optimum generation achievement faster by 26.67% at pitch angle, 44% at roll angle and 20% at yaw angle. This condition has an effect on increasing simulation execution time, where fast genetic algorithm is 26.4% faster at pitch angle, 38.05% at roll angle, and 24.19% at yaw angle
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.
OBD GPS Tracker for Car safety and securityhappyme20
OBD2 GPS Vehicle Tracker, a real-time GPS tracking device is manufactured by ThinkRace Technology, who manufactures innovative GPS trackers and wearable products. All you have to do is plug this device into your car's on-board diagnostic (OBD) port, and you're ready to view real-time location updates. you can view your tracker's location on any internet-enabled device.
for more visit: Thinkrace Technology
1. Advanced Process Control and Continuous Processing in
Pharmaceutical Manufacturing: What Can We Learn from
Other Industries
Paul Brodbeck/Control Associates Inc.
2. What is Process Control?
How?
Why?
Benefits?
Why Advanced Process Control?
Advanced Controls
◦ MPC
◦ Kalman Filter
◦ Neural Networks
◦ LP Optimization
3. Controlling process variable to a desired SP.
◦ Reactor Temperature
◦ Heat Exchanger Flow Rate
◦ Boiler Pressure
◦ OTC Tablet (API) Concentration.
◦ Dryer Moisture Content
◦ House Temperature
◦ Car Speed
◦ Distillation Column Production Rate
Controller
4. First Feed Back Controller – Humans
Closed Loop Control – Level, Press, Flow, API Concentration (%)
Vary output – Valve, Pump, Agitator, Fan
5. WHY?
Manual vs. Automatic
Easy
Quality – Temperature Variability
Temperature Cycling
◦ Poor Quality
◦ Inefficient
◦ Wear and Tear on Heater and Parts
Auto change SP at day/night Cost Savings
Control Improves Quality & Reduces Costs
6. WHY?
Manual vs. Automatic
Quality – Constant Speed
Speed Cycling
◦ Poor QualityInefficient
◦ Wear and Tear on Car and Parts
Get there faster!
◦ Set Speed closer to speed limit
◦ Less Risk/Less Speeding Tickets
Control Improves Quality & Reduces Costs
7. WHY?
Manual vs. Automatic
Production
Yields
Profit
Reduces Costs
◦ Labor
◦ Energy
Safer
Lower Risk
9. Reduce Variability!
◦ Almost at end in itself.
Edward Deming – Quality Program Founder
Japan Post WWWII Better Quality
◦ Autos, Semi-Conductors, Steel…
1980’s American Manufacturing Poor Quality
Statistical Process Control Introduced into US
Get Process under Control (Statistically)
◦ Control Charts
Reduce Variability
Increase Quality
24.
22 2
1 1 1 1 1 1
1 1
y u u
n n nP M M
y set u u
j j j j j j j j
i j i j i j
J w y k i y k i w u k i w u k i u
y: Controlled variable
u: Actuator
△u: Predicted adjustment
manipulated
variable
deviations
Controlled variable
deviations
controller adjustments
Singh, R., Ierapetritou, M., Ramachandran, R. (2013). European
Journal of Pharmaceutics and Biopharmaceutics,
http://dx.doi.org/10.1016/j.ejpb.2013.02.019.
Tuning parameters
1. Output weights (wy
j)
2. Rate weights ( )
3.Input weight ( )
4. Prediction horizon
5. Control horizon
u
jw
u
jw
35. Statistically Optimal Estimator
Numerous Applications
◦ De facto Standard Robotics
◦ Aerospace
◦ Missile Guidance
◦ Economics
◦ Signal Processing
State Prediction based on:
◦ Noisy Data
◦ Physical Model (Error increases w/ Time)
36. Takes a statistical average of:
◦ Measured Variable
◦ Model
Acts Recursively to continuously predict most
probable state.
First used by NASA to predict location of rockets
◦ Uncertain GPS Signal
◦ Physical Model error increases with time
Use measurement signal to correct errors with
model.
Use model to validate measured values.
47. Linear Programming
A mathematical/computer optimization
technique – Simplex Method
Solve a system of linear equations
Can be used to find the minimum and
maximum states of process control
Can be made subject to multiple constraints
