scholarly journals Platform for Advanced Control and Estimation (PACE): Shell's and Yokogawa's Next Generation Advanced Process Control Technology

2015 ◽  
Vol 48 (8) ◽  
pp. 1-5 ◽  
Author(s):  
R. Amrit ◽  
W. Canney ◽  
P. Carrette ◽  
R. Linn ◽  
A. Martinez ◽  
...  
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.


Author(s):  
Д.А. Никитин ◽  
Д.Е. Цыбин ◽  
А.М. Хафизов ◽  
С.А. Мишин ◽  
А.Ф. Тайчинов ◽  
...  

В данной статье рассматривается разработка адаптивного виртуальногоанализаторов (ВА) показателей качества для проекта системы усовершенствованного управления Advanced Process Control (СУУТП, далее «APC»). Построение моделей ВА произведено с помощью регрессионных методов по параметрам, влияющих в наибольшей степени на выброс монооксида углерода, содержание кислорода в дымовых газах, температуры змеевика. В качестве метода оценки моделей предложен векторный критерий, определяющий значения ширины окна ВА с наиболее оптимальными значениями коэффициента детерминации и среднеквадратичной ошибкой. Интеграция моделей ВА в систему управления обеспечивает оптимизацию параметров работы с целью улучшения качественных и количественных показателей производимой продукции, а также позволяет существенно снизить затраты в области оборудования поточной аналитики. This article discusses the development of adaptive virtual analyzers (VA) of quality indicators for the project of the advanced control system Advanced Process Control (SUUTP, hereinafter referred to as "APC"). The construction of VA models was carried out using regression methods for the parameters that affect to the greatest extent the emission of carbon monoxide, the oxygen content in the flue gases, and the coil temperature. As a method for evaluating models, a vector criterion is proposed that determines the values ​​of the VA window width with the most optimal values ​​of the determination coefficient and the root-mean-square error. Integration of VA models into the control system ensures optimization of operating parameters in order to improve the qualitative and quantitative indicators of manufactured products, as well as significantly reduce costs in the field of flow analytics equipment.


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

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