scholarly journals Towards incremental control structure optimisation for process control

2007 ◽  
Author(s):  
Wolfgang Birk
Keyword(s):  
Process Control ◽  
Control Structure

scholarly journals Control Structure Design in Process Control Systems

2000 ◽  
Vol 33 (10) ◽  
pp. 135-140
Author(s):  
Karl Henrik Johansson ◽  
Tore Hägglund
Keyword(s):  
Process Control ◽  
Control Systems ◽  
Control Structure ◽  
Structure Design ◽  
Process Control Systems

Fault detection for control of wastewater treatment plants

2006 ◽  
Vol 53 (4-5) ◽  
pp. 375-382 ◽  
Author(s):  
O. Schraa ◽  
B. Tole ◽  
J.B. Copp
Keyword(s):  
Wastewater Treatment ◽  
Process Control ◽  
Real Time ◽  
Control Structure ◽  
Wastewater Treatment Plants ◽  
Process Models ◽  
Sensor Data ◽  
Time Data ◽  
Model Based Control ◽  
Model Based

Interest in real-time model-based control is increasing as more and more facilities are being asked to meet stricter effluent requirements while at the same time minimizing costs. It has been identified that biological process models and automated process control technologies are being used at wastewater treatments plants throughout the world and that great potential for optimising biotreatment may exist with the integration of these two technology areas. According to our experience, wastewater treatment plants are indeed looking for ways to successfully integrate their modelling knowledge into their process control structure; however, there are practical aspects of this integration that must be addressed if the benefits of this integration are to be realised. This paper discusses the practical aspects of monitoring, filtering and analysing real sensor data with an aim to produce a reliable real-time data stream that might be used within a model-based control structure. Several real case study examples are briefly discussed in this paper.

A Neuro-Mimetic Dynamic Scheduling Algorithm for Control: Analysis and Applications

1997 ◽  
Vol 9 (3) ◽  
pp. 479-502 ◽  
Author(s):  
Harpreet S. Kwatra ◽  
Francis J. Doyle ◽  
Ilya A. Rybak ◽  
James S. Schwaber
Keyword(s):  
Process Control ◽  
Dynamic Scheduling ◽  
Control Structure ◽  
Control Function ◽  
Nonlinear Process ◽  
Baroreceptor Reflex ◽  
Control Analysis ◽  
Continuous Stirred Tank Reactor ◽  
Level Control ◽  
Nonlinear Process Control

A simple neuronal network model of the baroreceptor reflex is analyzed. From a control perspective, the analysis suggests a dynamic scheduled control mechanism by which the baroreflex may perform regulation of the blood pressure. The main objectives of this work are to investigate the static and dynamic response characteristics of the single neurons and the network, to analyze the neuromimetic dynamic scheduled control function of the model, and to apply the algorithm to nonlinear process control problems. The dynamic scheduling activity of the network is exploited in two control architectures. Control structure I is drawn directly from the present model of the baroreceptor reflex. An application of this structure for level control in a conical tank is described. Control structure II employs an explicit set point to determine the feedback error. The performance of this control structure is illustrated on a nonlinear continuous stirred tank reactor with van de Vusse kinetics. The two case studies validate the dynamic scheduled control approach for nonlinear process control applications.

scholarly journals Selection of process control structure based on economics

1994 ◽  
Vol 18 ◽  
pp. S511-S515 ◽  
Author(s):  
Lawrence Narraway ◽  
John Perkins
Keyword(s):  
Process Control ◽  
Control Structure ◽  
Selection Of

Process control structure selection based on economics

AIChE Journal ◽  
2000 ◽  
Vol 46 (10) ◽  
pp. 1998-2016 ◽  
Author(s):  
Jonathan A. Heath ◽  
Ioannis K. Kookos ◽  
John D. Perkins
Keyword(s):  
Process Control ◽  
Control Structure ◽  
Structure Selection
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