Petri net-based closed-loop control and on-line scheduling of the batch process plant

1998 ◽  
Author(s):  
H. Yu
Keyword(s):  
Petri Net ◽  
Closed Loop ◽  
Batch Process ◽  
Closed Loop Control ◽  
Loop Control ◽  
Process Plant ◽  
On Line

Closed-loop control of fed-batch cultures of recombinantEscherichia coli using on-line HPLC

1994 ◽  
Vol 44 (7) ◽  
pp. 819-829 ◽  
Author(s):  
Claire Turner ◽  
Malcolm E. Gregory ◽  
Nina F. Thornhill
Keyword(s):  
Closed Loop ◽  
Closed Loop Control ◽  
Fed Batch ◽  
Loop Control ◽  
Batch Cultures ◽  
On Line

scholarly journals Designing optimized industrial process analysers for closed loop control

1991 ◽  
Vol 13 (4) ◽  
pp. 139-142 ◽  
Author(s):  
Bernard Grevesmuehl ◽  
Cynthia Kradjel ◽  
Hanno Kellner
Keyword(s):  
Closed Loop ◽  
Continuous Measurement ◽  
Process Analysis ◽  
Industrial Process ◽  
Process Efficiency ◽  
Closed Loop Control ◽  
Loop Control ◽  
On Line ◽  
Line Process ◽  
Increasing Process

Manufacturers are now looking closely at ways of optimizing ‘quality’ and increasing process efficiency while reducing manufacturing costs. Near infra-red (NIR) technology is a popular solution to this challenge: it provides manufacturers with rapid and reliable in-process analysis and thousands of systems have already been installed in the food, chemical, pharmaceutical and agricultural markets.For over 10 years, NIR has been successfully applied to at-line process analysis. Rugged and easy-to-operate filter analysers are traditionally located in the control room–process operators can then ‘grab samples’ and obtain results in less than a minute. There are many practical advantages to using at-line filter systems. Products from many lines can be run on one system, and, since there is no direct process interface, installation, operation and maintenance are quite simple.Many manufacturers, however, are now striving to achieve on-line closed loop control, in these cases the benefit of obtaining continuous measurement is well worth the effort required to automate the analysis.

A Real-Time Calibrated Thermal Model for Closed-Loop Weld Bead Geometry Control

1985 ◽  
Vol 107 (1) ◽  
pp. 25-33 ◽  
Author(s):  
B. E. Bates ◽  
D. E. Hardt
Keyword(s):  
Thermal Model ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Conduction Model ◽  
Loop Control ◽  
On Line ◽  
Prediction And Control ◽  
And Control

A distributed source conduction model is presented as a means for prediction and control of weld width and depth. Model inputs of arc efficiency and distribution are continuously calibrated by on-line comparison of predicted topside temperatures to measured profiles, giving low errors in weld size prediction. The technique is intended for closed-loop control of MIG and TIG partial penetration welds from topside measurements.

The Impact of Visual Feedback Type on the Mastery of Visuo-Motor Transformations

2012 ◽  
Vol 220 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Sandra Sülzenbrück
Keyword(s):  
Empirical Research ◽  
Visual Feedback ◽  
Closed Loop ◽  
Motor Planning ◽  
Visual Input ◽  
Closed Loop Control ◽  
Loop Control ◽  
Feedback Type ◽  
Effective Use ◽  
The Impact

For the effective use of modern tools, the inherent visuo-motor transformation needs to be mastered. The successful adjustment to and learning of these transformations crucially depends on practice conditions, particularly on the type of visual feedback during practice. Here, a review about empirical research exploring the influence of continuous and terminal visual feedback during practice on the mastery of visuo-motor transformations is provided. Two studies investigating the impact of the type of visual feedback on either direction-dependent visuo-motor gains or the complex visuo-motor transformation of a virtual two-sided lever are presented in more detail. The findings of these studies indicate that the continuous availability of visual feedback supports performance when closed-loop control is possible, but impairs performance when visual input is no longer available. Different approaches to explain these performance differences due to the type of visual feedback during practice are considered. For example, these differences could reflect a process of re-optimization of motor planning in a novel environment or represent effects of the specificity of practice. Furthermore, differences in the allocation of attention during movements with terminal and continuous visual feedback could account for the observed differences.

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