scholarly journals Application of Virtualization for Process Control Experiments

2022 ◽  
Vol 2160 (1) ◽  
pp. 012034
Author(s):  
Haijun Zhou
Keyword(s):  
Process Control ◽  
Model Identification ◽  
Control Object ◽  
Implementation Process ◽  
Identification Problem ◽  
Teaching Profession ◽  
Heating Process ◽  
Water Tank ◽  
Teaching Platform ◽  
Industry Applications

Abstract The virtualization of the Festo process control teaching platform and the implementation process of extending it with real industry applications are introduced. Taking the heating process of the water tank as an example, the model extraction method of the real object is analyzed in detail, and the model identification problem of the low-order linear control object is solved. Through the introduction of the creation process of the object model on the virtual platform, a feasible way is pointed out for similar applications. On this basis, it is proposed to integrate the teaching platform with the specific industrial industry in the virtualized environment, broaden the breadth of process control teaching, and point out new ideas for building a teaching profession with industry support.

scholarly journals Supervisi Pendidikan

2019 ◽  
Author(s):  
Trisvi
Keyword(s):  
School Leadership ◽  
Teaching And Learning ◽  
School Principals ◽  
Implementation Process ◽  
Teaching Profession ◽  
Administrative Staff ◽  
Continuous Assessment ◽  
Basic Principles ◽  
Starting Point ◽  
Educational Supervision

Supervision is guidance given to all school staff so that they can improve their ability to develop better teaching and learning situations. The person who carries out supervision is called a supervisor. Supervision can be interpreted as coaching. While the target of coaching can be for school principals, teachers, administrative staff. But what becomes the target of supervision also means teacher guidance.The aim of educational supervision is to develop better teaching and learning situations through coaching and enhancing the teaching profession. The functions and objectives of educational supervision include: As the direction of education, goals as an end point, goals as a starting point to achieve other goals. In this case, the educational objectives of one another are inseparable.Supervision has a very important goal to achieve, therefore supervision certainly has very important benefits. Among the benefits of supervision are Coordinating all school businesses, Equipping school leadership, Extending the experience of teachers, Stimulating creative school businesses, Providing continuous assessment and facilities and many more benefits or functions of the educational supervision. In addition to having goals and functions, supervision also has basic principles in the implementation process. Then supervision also has various types, among which are autocracy, democratic, pseudo-democratic, diplomatic manipulation of Laissez-faire

Demonstration System of Agricultural Informatization Courses for Web Teaching

2013 ◽  
Vol 336-338 ◽  
pp. 2087-2090
Author(s):  
Chuan Sheng Yang ◽  
Hai Feng Liu ◽  
You Fu Jiang
Keyword(s):  
Implementation Process ◽  
System Implementation ◽  
Practical Application ◽  
Detailed Design ◽  
Teaching System ◽  
Teaching Platform ◽  
Quality Of Teaching ◽  
Web Teaching ◽  
Main Module

Using our school's agricultural informatization degree functions and characteristics of the teaching platform, we introduce the agricultural informatization network course teaching demonstration system implementation process, the main module, database and platform construction. At the same time the detailed design is discussed. According to practical application, it shows that the teaching system can improve the teaching research, guarantee the quality of teaching, and have good demonstration effect.

scholarly journals Synthesis Algorithms for Adaptive Process Control Systems Based on Associative Memory Technology

2019 ◽  
Vol 9 (2) ◽  
pp. 38-42
Keyword(s):  
Process Control ◽  
Control Systems ◽  
Associative Memory ◽  
Control Object ◽  
Single Step ◽  
Complex Objects ◽  
Adaptive Process ◽  
Process Control Systems ◽  
Technological Control ◽  
Adaptive Process Control

The results of the regular synthesis algorithms development for adaptive process control systems based on the associative memory technology are presented in the article. The single-step and multi-step, deterministic and stochastic algorithms used for solving control and tracking problems are considered. The relative simplicity of these algorithms makes possible to recommend them for managing complex objects in conditions of uncertainty using concepts and associative memory technology. Based on the technology of associative memory, synthesis algorithms for adaptive production process control systems are proposed to function under unpredictable uncertainties and ensure the rate of the structure adapting process of the main system circuit, commensurate with the rate of transients in the technological control object operating under stochastic independent disturbances.

Practical Solution for Temperature Control for Inertial Object

2016 ◽  
Vol 251 ◽  
pp. 146-151
Author(s):  
Jordan Mężyk
Keyword(s):  
Temperature Control ◽  
Test Chamber ◽  
Control Object ◽  
Erosive Wear ◽  
Heating Process ◽  
Engineering Practice ◽  
Final Solution ◽  
Literature Research ◽  
Temperature Controller ◽  
Practical Applications

The issue of temperature control in the research devices is well known and well described in the literature. Yet in the practical applications, in every-day engineering practice there are situations where theoretical knowledge is not applicable in a straight forward manner and the proper settings of the controller parameters constitutes a problem to be addressed and solved with and individual approach. This article presents an example of commissioning of a heated test chamber that is a part of a research apparatus for testing the erosive wear of materials, developed and built at the Institute for Sustainable Technologies – National Research Institute in Radom.The test apparatus is used for eroding the surface of the tested sample with a stream of a mixture of air and an abrasive medium. The test can be performed in room temperature and in raised temperature, both for the ambient of the sample as well as for the air used to make a mixture. The maximal temperature allowed in the machine is 600°C, so the test chamber is equipped with a hermetic door and heat insulation. The construction of the chamber makes it very inertial in terms of heating and cooling, which makes the temperature control a bit challenging.The system is controlled with a PLC with software PID controller implemented in Temperature Controller library. The use of standard set-up of PID parameters resulted in heating with significant overshoot and long settling time which was unacceptable. Trials to tune the parameters with use of built-in algorithm resulted in slower heating and still didn’t eliminate the overshoot. The literature research and tests were performed to identify the problem and to implement simple solution.The article presents the selected results of the literature research and details on the performed tests. The algorithm of the final solution is presented and the characteristics of the heating process after the modification of the control algorithm. The final solution of the issue is an algorithm that uses the standard available temperature controller and the temperature monitor to influence the actions of the controller. The difficulties in identification of the control object (the chamber) caused the inability to solve the problem analytically, but the practical and empirical approach and several trials and errors allowed for the almost optimal control characteristics, which allows no overshoot and as fast as possible heating of the object.

A General Theory for Analysis of Catch and Effort Data

1985 ◽  
Vol 42 (3) ◽  
pp. 414-429 ◽  
Author(s):  
Jon Schnute
Keyword(s):  
General Theory ◽  
Model Identification ◽  
Identification Problem ◽  
Mathematical Proofs ◽  
Structured Population ◽  
Analysis Technique ◽  
Special Cases ◽  
Age Structured ◽  
Almost All ◽  
Special Case

This paper presents a general theory for analysis of catch and effort data from a fishery. Almost all previous methods are shown to be special cases, including those of Schaefer, Pella and Tomlinson, Schnute, and Deriso, as well as the stock reduction analysis technique of Kimura and Tagart and Kimura, Balsiger, and Ito. Like that of Deriso, the theory here is based on natural equations for an age structured population. However, instead of a fixed single model, this paper gives a general model that can be tailored to any particular fishery. The problem of determining the appropriate special case is conceptually identical to the model identification problem described by Box and Jenkins in the context of time series analysis, Identification necessarily begins with a suitable class of models. This paper defines such a class, unique to fisheries, complete with mathematical proofs and biological explanations of all important equations.

Nonlinear Model Identification From Multiple Data Sets Using an Orthogonal Forward Search Algorithm

2013 ◽  
Vol 8 (4) ◽  
Author(s):  
Ping Li ◽  
Hua-Liang Wei ◽  
Stephen A. Billings ◽  
Michael A. Balikhin ◽  
Richard Boynton
Keyword(s):  
Search Algorithm ◽  
Model Identification ◽  
Identification Problem ◽  
Data Sets ◽  
Nonlinear Dynamic Model ◽  
Data Set ◽  
Multiple Data ◽  
Orthogonal Search ◽  
Data Points ◽  
Multiple Data Sets

A basic assumption on the data used for nonlinear dynamic model identification is that the data points are continuously collected in chronological order. However, there are situations in practice where this assumption does not hold and we end up with an identification problem from multiple data sets. The problem is addressed in this paper and a new cross-validation-based orthogonal search algorithm for NARMAX model identification from multiple data sets is proposed. The algorithm aims at identifying a single model from multiple data sets so as to extend the applicability of the standard method in the cases, such as the data sets for identification are obtained from multiple tests or a series of experiments, or the data set is discontinuous because of missing data points. The proposed method can also be viewed as a way to improve the performance of the standard orthogonal search method for model identification by making full use of all the available data segments in hand. Simulated and real data are used in this paper to illustrate the operation and to demonstrate the effectiveness of the proposed method.

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