Use of microelectronics to upgrade process control into production control

1983 ◽  
Vol 60 (2Part2) ◽  
pp. 443-447 ◽  
Author(s):  
C. B. von Klösterlein ◽  
H. J. Vos
Keyword(s):  
Process Control ◽  
Production Control

scholarly journals INFORMATION SYSTEM AND PRODUCTION PROCESS CONTROL

2019 ◽  
Author(s):  
Angelo Zanella ◽  
Egidio Cascini
Keyword(s):  
Information System ◽  
Quality Control ◽  
Process Control ◽  
Production Control ◽  
Reference Conditions ◽  
Specific Reference ◽  
Theoretic Model ◽  
Perishable Goods ◽  
Control Production ◽  
Accounting Control

We start with the definition of experimental data. First place we consider the controllable variables, whose levels are established by technical production experts with regard to a theoretic “model” defining the production components, their connections and working rules in sight of the envisaged achievements. Then, the responses or results of production activities, which typically show random variability. The controllable variables are used to regulate the means of their distributions and aspects of variability (Taguchi approach). An information system represents a data collection, which is directed to specific purposes like: financial control, accounting control, personnel control, business control, production control, framed around production programs, control of production lines and related maintenance, quality control, framed around control of nonconformities regarding some specification limits and ensuring product good quality over time. Qualitative guidelines for choosing the data for a quality control information system are that it must be as near as possible to a sufficient system which means that it is capable of explaining any nonconformity in a product characteristic, as well as being complete, that is, such that it is also capable of suggesting how to remove them. In a natural way quality control implies a dynamic approach based on a systematic improvement of production activities, starting from specific reference conditions. We consider two examples appropriated to explain the fundamentals of a methodology useful to cope with this complex subject. The first example refers to a real industrial application regarding the process control of polyvinyl chloride production, found in literature [9]. The second example concerns the distribution process of perishable goods, which is considered to be an innovation. In this case we suppose that there is a finite set of operating situations (points of sale) and that we can define an indicator Q=Q1 /c = Quality of a reference product/a corresponding economic quantity (cost or earning). In the case considered, by means of the ordering of the ratios total cost/total earning, we come to the most profitable situation.

Research on Visual Production Process Control System Oriented to Discrete Manufacturing

2011 ◽  
Vol 474-476 ◽  
pp. 1914-1919
Author(s):  
Wan Gao Li ◽  
Xiao Le Liu ◽  
De Chang Sun
Keyword(s):  
Control System ◽  
Process Control ◽  
Data Acquisition ◽  
Production Process ◽  
Web Application ◽  
Production Control ◽  
Process Control System ◽  
Seamless Integration ◽  
Event Tracking ◽  
Discrete Manufacturing

Firstly, the requirements of production control and the features of discrete manufacturing process were researched , the production process control system architecture was built. Then, the key technologies used in the system realization were studied. Automatic identification technology was used in data acquisition; product tracking and event tracking technologies were used to implement production monitor, abnormal monitor respectivly in application server; Web technology was used in production statistics and analysis in presentation layer. Data acquisition and statement analysis implementation were separately based on the Windows and Web application of NET platform; products tracing and event tracking implementtation were based on the configuration visual software platform: GE Cimplicity. Every layer is distinct and seamless integration in the entire system, the whole architecture has strongly robustness and expandability. Finally, a case proved the validity of this system.

scholarly journals Sensors and Process Control in Copper Smelters: A Review of Current Systems and Some Opportunities

Minerals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Luis Arias ◽  
Eduardo Balladares ◽  
Roberto Parra ◽  
Daniel Sbarbaro ◽  
Sergio Torres
Keyword(s):  
Process Control ◽  
Primary Production ◽  
Hyperspectral Imaging ◽  
Industry 4.0 ◽  
Production Control ◽  
Iron Sulfides ◽  
Advanced Level ◽  
Entire Process ◽  
Current Systems ◽  
Equivalent Conditions

Despite the widespread and extended use of sensor systems in different industries, there is an important gap to reach equivalent conditions in pyrometallurgical processes for primary production. In the specific case of copper pyrometallurgy, the situation is particularly challenging to incorporate the Industry 4.0 concept for the optimization of their operations. Currently, only two instruments can be identified at the commercial level: the Noranda pyrometer and the Online Production Control (OPC) system. The iron-making and steelmaking industries, however, present an advanced level of control based on monitoring and sensing networks throughout the entire process. This reality has served as a basis for developing a series of solutions based on radiometric sensors for copper pyrometallurgy. We present two types of sensing concept. The first one is applied to smelting and converting reactors based on the measurements of the radiation of the oxidation of different copper and iron sulfides. The second one considers hyperspectral imaging of molten phases flow during operations. The idea of this proposal is to transfer some commercial sensing technologies already in use in the steelmaking industry. In this article, the fundamentals of the sensor design, proofs of concept, and the initial industrial validations are reviewed. Finally, a discussion on the contribution of this knowledge and development opportunities within the framework of Industry 4.0 are addressed.

Research of Discrete Manufacturing Industry Production Control System Based on RFID Technology

2014 ◽  
Vol 926-930 ◽  
pp. 1493-1496
Author(s):  
Xiang Qian Ding ◽  
Wei Dong Zhang ◽  
Rui Chun Hou
Keyword(s):  
Control System ◽  
Process Control ◽  
Production Process ◽  
Real Time ◽  
Manufacturing Process ◽  
Manufacturing Industry ◽  
Production Control ◽  
Process Control System ◽  
Rfid Technology ◽  
Discrete Manufacturing

In view of the current discrete manufacturing workshop production process control problem of poor real-time performance, reliability, this paper proposes a discrete manufacture based on rfid technology workshop production process control system solutions. For discrete manufacturing enterprise workshop layer of the production process control system emphasizes the manufacturing process of real-time data acquisition, validity and enforceability of the production plan. Developed a discrete manufacturing process control system based on RFID, provides the foundation for just-in-time production of discrete manufacturing.

scholarly journals INFORMATION SYSTEM AND PRODUCTION PROCESS CONTROL

2019 ◽  
Author(s):  
Angelo Zanella ◽  
Egidio Cascini
Keyword(s):  
Information System ◽  
Quality Control ◽  
Process Control ◽  
Production Control ◽  
Reference Conditions ◽  
Specific Reference ◽  
Theoretic Model ◽  
Perishable Goods ◽  
Control Production ◽  
Accounting Control

We start with the definition of experimental data. First place we consider the controllable variables, whose levels are established by technical production experts with regard to a theoretic “model” defining the production components, their connections and working rules in sight of the envisaged achievements. Then, the responses or results of production activities, which typically show random variability. The controllable variables are used to regulate the means of their distributions and aspects of variability (Taguchi approach). An information system represents a data collection, which is directed to specific purposes like: financial control, accounting control, personnel control, business control, production control, framed around production programs, control of production lines and related maintenance, quality control, framed around control of nonconformities regarding some specification limits and ensuring product good quality over time. Qualitative guidelines for choosing the data for a quality control information system are that it must be as near as possible to a sufficient system which means that it is capable of explaining any nonconformity in a product characteristic, as well as being complete, that is, such that it is also capable of suggesting how to remove them. In a natural way quality control implies a dynamic approach based on a systematic improvement of production activities, starting from specific reference conditions. We consider two examples appropriated to explain the fundamentals of a methodology useful to cope with this complex subject. The first example refers to a real industrial application regarding the process control of polyvinyl chloride production, found in literature [9]. The second example concerns the distribution process of perishable goods, which is considered to be an innovation. In this case we suppose that there is a finite set of operating situations (points of sale) and that we can define an indicator Q=Q1 /c = Quality of a reference product/a corresponding economic quantity (cost or earning). In the case considered, by means of the ordering of the ratios total cost/total earning, we come to the most profitable situation.

INSTRUMENTATION - Mass Spectrometry Process Control

1956 ◽  
Vol 48 (2) ◽  
pp. 81-84
Author(s):  
William Priestley ◽  
B. Dudenbostel, Jr.
Keyword(s):  
Mass Spectrometry ◽  
Process Control
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