Improvement of the recycling of plastics in lightweight packaging treatment plants by a process control concept

2019 ◽  
Vol 37 (2) ◽  
pp. 120-126 ◽  
Author(s):  
Alexander Feil ◽  
Erdogan Coskun ◽  
Marcel Bosling ◽  
Sebastian Kaufeld ◽  
Thomas Pretz
Keyword(s):  
Process Control ◽  
Material Flow ◽  
Treatment Process ◽  
Input Stream ◽  
Entire Process ◽  
Material Recycling ◽  
Packaging Waste ◽  
Separation Result ◽  
Control Concept ◽  
Sorting Process

In Germany, only approximately 30% by mass of plastics from lightweight packaging waste is recycled; 65% by mass is transferred to inferior residual fractions (sorting residue and mixed plastics), which are currently only utilized thermally. An increase in the recycling of valuable resources in the sense of material recycling would both contribute to the saving of resources and improve the economic situation of plant operators. It is generally known from operating and planning experience that fluctuation in the amount of material loaded into the sorting process is one of the main reasons for suboptimal recycling quotas. In particular, overfilling in the input stream leads to a deterioration of the separation result of the entire process. A novel process control concept envisages equalizing the material flow in such a way that all separation steps are operated in the intended design range. For the example of a lightweight packaging treatment process, the requirements and technological solutions for a sensor-based process control concept will be presented.

Supervisory Control of Drilling

1996 ◽  
Vol 118 (1) ◽  
pp. 10-19 ◽  
Author(s):  
R. J. Furness ◽  
A. Galip Ulsoy ◽  
C. L. Wu
Keyword(s):  
Process Control ◽  
Torque Control ◽  
Feed Speed ◽  
Machining Time ◽  
Control Approach ◽  
Drilling Operation ◽  
Control Functions ◽  
Control Concept ◽  
Supervisory Process ◽  
And Control

A supervisory process control approach to machining is presented in this paper, and demonstrated by application to a drilling operation. The supervisory process control concept incorporates optimization and control functions in a hierarchical structure. This approach utilizes feedback measurements to parameterize the constraints of a process optimization problem whose solution determines both strategies and references for process control. For this particular drilling operation, a three-phase strategy (utilizing a combination of feed, speed, and torque control) evolved due to inherent variation in constraint activity as a function of hole depth. A controller comparison study was conducted which demonstrates the advantages of this approach compared to (1) uncontrolled “conventional” drilling, (2) feed and speed controlled drilling, and (3) torque and speed controlled drilling. Benefits of reduced machining time, improved hole quality, and the elimination of tool breakage are demonstrated, and the potential economic impact is highlighted for an example production application.

Solar photo-Fenton treatment—Process parameters and process control

2006 ◽  
Vol 64 (1-2) ◽  
pp. 121-130 ◽  
Author(s):  
W. Gernjak ◽  
M. Fuerhacker ◽  
P. Fernández-Ibañez ◽  
J. Blanco ◽  
S. Malato
Keyword(s):  
Process Control ◽  
Process Parameters ◽  
Treatment Process

Drinking water quality and treatment: the use of artificial neural networks

2001 ◽  
Vol 28 (S1) ◽  
pp. 26-35 ◽  
Author(s):  
C W Baxter ◽  
Q Zhang ◽  
S J Stanley ◽  
R Shariff ◽  
R -RT Tupas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Process Control ◽  
Treatment Process ◽  
Drinking Water Treatment ◽  
Water Treatment Process ◽  
Artificial Neural

To improve drinking water quality while reducing operating costs, many drinking water utilities are investing in advanced process control and automation technologies. The use of artificial intelligence technologies, specifically artificial neural networks, is increasing in the drinking water treatment industry as they allow for the development of robust nonlinear models of complex unit processes. This paper highlights the utility of artificial neural networks in water quality modelling as well as drinking water treatment process modelling and control through the presentation of several case studies at two large-scale water treatment plants in Edmonton, Alberta.Key words: artificial neural networks, water treatment process control, water treatment modelling.

scholarly journals Absorbance based water quality indicators as parameters for treatment process control with respect to organic substance removal

2017 ◽  
Vol 17 ◽  
pp. 00091
Author(s):  
Sławomir Szerzyna ◽  
Marek Mołczan ◽  
Małgorzata Wolska ◽  
Wojciech Adamski ◽  
Jacek Wiśniewski
Keyword(s):  
Water Quality ◽  
Process Control ◽  
Quality Indicators ◽  
Organic Substance ◽  
Treatment Process ◽  
Water Quality Indicators
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