Scenario for a Yield Model Based on Reliable Defect Density Data and Linked to Advanced Process Control

2019 ◽  
Vol 2 (2) ◽  
pp. 433-452 ◽  
Author(s):  
Andreas Nutsch ◽  
R. Oechsner
Keyword(s):  
Process Control ◽  
Defect Density ◽  
Yield Model ◽  
Density Data ◽  
Advanced Process Control ◽  
Model Based

Model-based advanced process control of coagulation

2002 ◽  
Vol 45 (4-5) ◽  
pp. 9-17 ◽  
Author(s):  
C.W. Baxter ◽  
R. Shariff ◽  
S.J. Stanley ◽  
D.W. Smith ◽  
Q. Zhang ◽  
...  
Keyword(s):  
Water Treatment ◽  
Process Control ◽  
New Technology ◽  
Drinking Water Treatment ◽  
Pilot Scale ◽  
Operating Conditions ◽  
Ann Model ◽  
Real Time Control ◽  
Advanced Process Control ◽  
Model Based

The drinking water treatment industry has seen a recent increase in the use of artificial neural networks (ANNs) for process modelling and offline process control tools and applications. While conceptual frameworks for integrating the ANN technology into the real-time control of complex treatment processes have been proposed, actual working systems have yet to be developed. This paper presents development and application of an ANN model-based advanced process control system for the coagulation process at a pilot-scale water treatment facility in Edmonton, Alberta, Canada. The system was successfully used to maintain a user-defined set point for effluent quality, by automatically varying operating conditions in response to changes in influent water quality. This new technology has the potential to realize significant operational cost saving for utilities when applied in full-scale applications.

Increasing revenue and reducing cost through the use of advanced process control technology in the sugar industry

2016 ◽  
pp. 620-624
Author(s):  
Scott Kahre
Keyword(s):  
Process Control ◽  
Sugar Industry ◽  
Operating Cost ◽  
Control Technology ◽  
Advanced Process Control ◽  
Output Controller ◽  
Complex Processes ◽  
Unit Operations ◽  
Sugar Unit ◽  
Optimum Cost

Advanced process control technology can provide sugar processors the ability to realize major revenue enhancements and/or operating cost reductions with low initial investment. One technology in particular, model predictive control (MPC), holds the potential to increase production, reduce energy costs, and reduce quality variability in a wide variety of major sugar unit operations. These include centrifugal stations, pulp dryers, extractors, diffusers, mills, evaporating crystallizers, juice purification, and more. Simple payback periods as low as two months are projected. As a PC-based add-on to existing distributed control systems (DCS) or programmable logic controller (PLC) systems, MPC acts as a multi-input, multi-output controller, utilizing predictive process response models and optimization functions to control complex processes to their optimum cost and quality constraints.

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