Multivariate feed forward process control and optimization of an industrial, granulation based tablet manufacturing line using historical data

2020 ◽  
Vol 591 ◽  
pp. 119988
Author(s):  
Rita Mathe ◽  
Tibor Casian ◽  
Ioan Tomuţă
Keyword(s):  
Process Control ◽  
Historical Data ◽  
Feed Forward ◽  
Manufacturing Line ◽  
Tablet Manufacturing ◽  
Forward Process

Energy Savings at Phoenix 23rd Avenue Wastewater Treatment Plant Using Feed-Forward Process Control

2009 ◽  
Vol 2009 (13) ◽  
pp. 3722-3729 ◽  
Author(s):  
Thomas Walz ◽  
J.R. Coughenour ◽  
Kevin Williams ◽  
John Jacobs ◽  
Larry Shone ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Process Control ◽  
Wastewater Treatment Plant ◽  
Energy Savings ◽  
Treatment Plant ◽  
Feed Forward ◽  
Forward Process

Energy Reduction and Nutrient Removal in WWTPs Using Feed-Forward Process Control

2011 ◽  
Vol 2011 (6) ◽  
pp. 224-236
Author(s):  
Tilo Stahl ◽  
George Lee ◽  
Matthew Gray ◽  
Steven Kestel
Keyword(s):  
Process Control ◽  
Nutrient Removal ◽  
Energy Reduction ◽  
Feed Forward ◽  
Forward Process

scholarly journals High Level Waste (HLW) Vitrification Experience in the US: Application of Glass Product/Process Control to Other HLW and Hazardous Wastes

2008 ◽  
Vol 1107 ◽  
Author(s):  
Carol M. Jantzen ◽  
James C. Marra
Keyword(s):  
Process Control ◽  
High Level Waste ◽  
Glass Product ◽  
Mechanistic Models ◽  
Glass Waste ◽  
Statistical Process ◽  
Feed Composition ◽  
Feed Forward ◽  
Bonding Structure ◽  
High Level

AbstractVitrification is currently the most widely used technology for the treatment of high level radioactive wastes (HLW) throughout the world. At the Savannah River Site (SRS) actual HLW tank waste has successfully been processed to stringent product and process constraints without any rework into a stable borosilicate glass waste since 1996. A unique “feed forward” statistical process control (SPC) has been used rather than statistical quality control (SQC). In SPC, the feed composition to the melter is controlled prior to vitrification. In SQC, the glass product is sampled after it is vitrified. Individual glass property models form the basis for the “feed forward” SPC. The property models transform constraints on the melt and glass properties into constraints on the feed composition. The property models are mechanistic and depend on glass bonding/structure, thermodynamics, quasicrystalline melt species, and/or electron transfers. The mechanistic models have been validated over composition regions well outside of the regions for which they were developed because they are mechanistic. Mechanistic models allow accurate extension to radioactive and hazardous waste melts well outside the composition boundaries for which they were developed.

Implementation and Results of a Feed-Forward Process Controller

2012 ◽  
Vol 2012 (10) ◽  
pp. 5622-5641
Author(s):  
Matthew Gray ◽  
Jim McQuarrie ◽  
Steven Kestel ◽  
Heather Phillips ◽  
Tilo Stahl
Keyword(s):  
Feed Forward ◽  
Forward Process

scholarly journals Spatial constancy mechanisms in motor control

2011 ◽  
Vol 366 (1564) ◽  
pp. 476-491 ◽  
Author(s):  
W. Pieter Medendorp
Keyword(s):  
Motor Control ◽  
Eye Movements ◽  
Three Dimensional ◽  
Spatial Updating ◽  
Feedback Processing ◽  
Feed Forward ◽  
Motor Information ◽  
Induced Motion ◽  
Forward Process ◽  
The Brain

The success of the human species in interacting with the environment depends on the ability to maintain spatial stability despite the continuous changes in sensory and motor inputs owing to movements of eyes, head and body. In this paper, I will review recent advances in the understanding of how the brain deals with the dynamic flow of sensory and motor information in order to maintain spatial constancy of movement goals. The first part summarizes studies in the saccadic system, showing that spatial constancy is governed by a dynamic feed-forward process, by gaze-centred remapping of target representations in anticipation of and across eye movements. The subsequent sections relate to other oculomotor behaviour, such as eye–head gaze shifts, smooth pursuit and vergence eye movements, and their implications for feed-forward mechanisms for spatial constancy. Work that studied the geometric complexities in spatial constancy and saccadic guidance across head and body movements, distinguishing between self-generated and passively induced motion, indicates that both feed-forward and sensory feedback processing play a role in spatial updating of movement goals. The paper ends with a discussion of the behavioural mechanisms of spatial constancy for arm motor control and their physiological implications for the brain. Taken together, the emerging picture is that the brain computes an evolving representation of three-dimensional action space, whose internal metric is updated in a nonlinear way, by optimally integrating noisy and ambiguous afferent and efferent signals.

scholarly journals A Combined Feed-Forward/Feed-Back Control System for a QbD-Based Continuous Tablet Manufacturing Process

Processes ◽  
2015 ◽  
Vol 3 (2) ◽  
pp. 339-356 ◽  
Author(s):  
Ravendra Singh ◽  
Fernando Muzzio ◽  
Marianthi Ierapetritou ◽  
Rohit Ramachandran
Keyword(s):  
Control System ◽  
Manufacturing Process ◽  
Feed Forward ◽  
Tablet Manufacturing ◽  
Combined Feed

scholarly journals PEMODELAN KERANGKA ADAPTIVE THRESHOLD UNTUK MEMONITOR PRODUKSI MINYAK SAWIT NASIONAL BERBASIS STATISTICAL PROCESS CONTROL DAN ARTIFICIAL NEURAL NETWORK-BACKPROPAGATION

2016 ◽  
Vol 11 (2) ◽  
pp. 113-122
Author(s):  
Wahyu Widji Pamungkas ◽  
Syamsul Maarif ◽  
Tun Tedja Irawadi ◽  
Yandra Arkeman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Control ◽  
Statistical Process Control ◽  
Palm Oil ◽  
Historical Data ◽  
Adaptive Threshold ◽  
The Other ◽  
Production Volume ◽  
Statistical Process

Indonesia is the largest exporter of palm oil in the world, as the largest producer Indonesia still havemany problems. The problem caused by incomparable between the growth of upstream and downstreampalm oil industries. This impact to low added value of palm oil, then Indonesia exports palm oil in crudeform. On the other hand, On the other hand , orientation export of this commodity is also prone of barrier,because Indonesia was not the price setter of this commodity in the international market. Therefore it isimportant to monitor and predict the development of national palm oil production volume in order to takegood anticipation. This research develop a framework model adaptive threshold to monitor the growing ofnational palm oil production volume with techniques of statistical process control (SPC) and back propagationartificial neural network (ANN - BP) methods. Historical data production volume period from 1967 to 2015was used as a base of the behavior as data to determine the threshold and prediction volume for nextperiods. The formation of the threshold value was based on the behavior of the historical data, which areoriented by the epicenter of the average value in the last two periods .Through mapping of data historicalperiod values, existing and forecast values with adaptive threshold can show tolerant level for the threshold.Furthermore, based on the analysis, it is known that the prediction of 2016 to 2018 period, there will behappen the dynamics production volume of national palm oil within tolerance threshold. The values of thesepredictions generated from the simulation model predictions of ANN-BP with the level very good of validationmodel, demonstrated the level of squared errors is very small1 in the MSE = 0.00021136 with a degree ofoutput correlation and the target is very strong2 with R Validation is 99.98 percent.Keywords: adaptive threshold, statistical process control, artificial neural network, national palm oilproduction.

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