Next generation advanced process control: Leveraging big data and prediction

Author(s):  
James Moyne ◽  
Brad Schulze ◽  
Jimmy Iskandar ◽  
Michael Armacost
2015 ◽  
Vol 48 (8) ◽  
pp. 1-5 ◽  
Author(s):  
R. Amrit ◽  
W. Canney ◽  
P. Carrette ◽  
R. Linn ◽  
A. Martinez ◽  
...  

2013 ◽  
Author(s):  
J. Foucher ◽  
R. Thérèse ◽  
Y. Lee ◽  
S.-I. Park ◽  
S.-J. Cho

2016 ◽  
pp. 620-624
Author(s):  
Scott Kahre

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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