Instrumentation and process control fundamentalshossam hassanein
Basic course covers:
-Basic understanding of process control
-Important process control terminology
-Major components of a process loop
-Instrumentation P&ID symbols
Instrumentation and process control fundamentalshossam hassanein
Basic course covers:
-Basic understanding of process control
-Important process control terminology
-Major components of a process loop
-Instrumentation P&ID symbols
Process load,process lag,self regulation,error,control lag,dead time,cycling,discontinious control modes,two position control modes,flaoting control modes,propotional band,offset,propotional control, integral control,derivative control,pid control,pi control,pd control,tuning of pid control
This Course basics of instrumentation and control systems used in oil and gas and petrochemical industry,
The course the following topics
Basics of Instrumentation
Field Instruments
Control Valves
Process Control
Control systems
,the control system ,negative feedback versus positive feedback ,servo problem versus regulator problem ,development of block diagram ,measuring element ,controller and final control element
Process load,process lag,self regulation,error,control lag,dead time,cycling,discontinious control modes,two position control modes,flaoting control modes,propotional band,offset,propotional control, integral control,derivative control,pid control,pi control,pd control,tuning of pid control
This Course basics of instrumentation and control systems used in oil and gas and petrochemical industry,
The course the following topics
Basics of Instrumentation
Field Instruments
Control Valves
Process Control
Control systems
,the control system ,negative feedback versus positive feedback ,servo problem versus regulator problem ,development of block diagram ,measuring element ,controller and final control element
A software based gain scheduling of pid controllerijics
In this paper, a scheduled-gain SG-PID controller using LabVIEW-based scheduling technique, which
consists of a set of virtual instruments, has been designed and experimentally tested for heating process.
Gain scheduling is realized by automatic setting of the controller parameters using three sets of
programmed parameters, depending on the relative error between actual process temperature and setpoint
(desired temperature). Experimental results show that the proposed controller, compared with conventional
C-PID controller, responds faster to the changes in the setpoint temperature, reduces the overshoots in
temperature during transient period and makes the system more stable. It was noticed that the dynamic and
steady-state errors in the system have been reduced.
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Giving feedback to students is often mutually unsatisfactory: it requires a great deal of time, yet it isn't always accessed. Can we do something better? This presentation was used to kick off a practitioner workshop back in 2014.
Chapter 1 basic components of control systemHarish Odedra
This presentation is on basic of control engineering subject which is offered to 5th sem Mechanical Engineering Department in Gujarat Technological University.
ME 313 Mechanical Measurements and Instrumentation is a followup course on ME-312 Machine Design. Design and implementation of measurement systems, signal conditioning and formatting. Dr. Bilal Siddiqui teaches this course every spring at DHA Suffa University.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
2. Problems with other controls
systems
• Controlled variable cannot be measured or has
large sampling period.
3. Possible solutions
• Control a related variable (e.g., temperature instead of
composition).
• Inferential control: Control is based on an estimate of
the controlled variable.
• The estimate is based on available measurements.
• Examples: empirical relation, Kalman filter
• Modern term: soft sensor
4. • In inferential control, the controlled variables
that are difficult to measure are estimated from
some easy to measure process variables and then
used in feedback control.
• Inferential control system has many excellent
performances such as disturbance resisting and
set-point tracking, however, the application is
restricted when strong load disturbance exists or
stable control accuracy and response speed are
highly required in the system.
5. • Uses easily measure process variables (T, P, F) to
infer more difficult to measure quantities such
as compositions and molecular weight.
• Can substantially reduce analyzer delay.
• Can be much less expensive in terms of capital
and operating costs
• Can provide measurements that are not
available any other way.
6. Estimate is based on available measurements
- Inferential Reactor Conversion Control
7. Software sensors (soft sensors)
• The inferred values of the primary variables are
used as feedback signals to an "external"
controller, such as a P+I controller, a predictive
controller, non-linear or even an adaptive
controller. Thus the inferential estimator serves
simply as a software based sensor (soft sensor) .
9. Advantage
• Enable the use of a desired control loop despite
the lack of measurement devices.
• Free from dependence on delayed data (off-line
analysis), leading to better control.
Disadvantages
• Knowledge on the process must be known.
• Wrong estimation leads to wrong control action
and hence detrimental to process operation.
10. How to improve
• Provide measured data periodically (off-line
analysis etc.)
• Better Mode