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

Optimization of Alkali Treatment Process Parameters for Kenaf Fiber: Experiments Design

2020 ◽  
pp. 1-10
Author(s):  
R. Sathish Kumar ◽  
Nivedhitha Muralidharan ◽  
Ravishankar Sathyamurthy
Keyword(s):  
Process Parameters ◽  
Treatment Process ◽  
Alkali Treatment ◽  
Kenaf Fiber

Systems Analysis of Process Control Capabilities for Forging Accuracy

2017 ◽  
Vol 265 ◽  
pp. 1110-1115 ◽  
Author(s):  
V.G. Shibakov ◽  
D.L. Pankratov ◽  
R. Khairullin
Keyword(s):  
Process Control ◽  
Process Parameters ◽  
Systems Analysis ◽  
Simulation Modeling ◽  
Systems Approach ◽  
Dimensional Accuracy ◽  
Significant Process ◽  
Expert Analysis ◽  
Forging Force

The significance matrix for the parameters of “material-billet-equipment-process-tool-personnel-environment” system was compiled using the systems approach to the assurance of forging dimensional accuracy, and the expert analysis revealed the most significant process parameters that affect the accuracy. The application of simulation modeling helped to establish the dependence of forging force on the dimensions of an incoming billet. The paper suggests a solution to increase the accuracy of the sized forgings.

The Effect of the Initial Stress and Strain State in Sheet Metals on the Roller Levelling Process

2015 ◽  
Vol 651-653 ◽  
pp. 1023-1028 ◽  
Author(s):  
Markus Grüber ◽  
Marius Oligschläger ◽  
Gerhard Hirt
Keyword(s):  
Process Control ◽  
Initial Stress ◽  
Sheet Metal ◽  
Process Parameters ◽  
Kinematic Hardening ◽  
Sheet Material ◽  
Stress And Strain ◽  
Fe Model ◽  
Sheet Metals ◽  
Initial State

Due to increasing requirements regarding the flatness of sheet metals, the process of roller levelling is of particular importance. The process itself is influenced by a high number of parameters such as machine design, sheet dimension, and material properties. Therefore, it is desirable to provide an online process control to react on changes of those process parameters. One possible approach for the layout of a process control and the identification of reference values is the use of the Finite Element Method (FEM). Considering the alternate bending a sheet metal undergoes when passing through a roller leveller, kinematic hardening of the sheet material must be taken into account. Additionally, the initial stress and strain distribution of the sheet metal – e.g. induced by coiling – has an influence on the material behaviour and consequently on the process parameters. With respect to these effects, a coupled FE model, which accounts for the initial state of the sheet metal, is introduced. An inverse calculation of material parameters describing the behaviour under cyclic load conditions has been done for an aluminium alloy AA5005 and a mild steel DC01. Based on this numerical setup, the influence of the initial stress state in the pre-levelled sheet metal on the roller levelling process has been deduced. Accompanying experiments on a down-sized roller leveller were carried out for a validation of the numerical setup.

Experimental Investigation of Residual Stresses after Heat Treatment and Grinding Processes in the Production of Ball Bearing Rings

2008 ◽  
Vol 571-572 ◽  
pp. 27-32 ◽  
Author(s):  
Volkan Güley ◽  
A. Erman Tekkaya ◽  
Turhan Savaş ◽  
Feridun Özhan
Keyword(s):  
Heat Treatment ◽  
Experimental Investigation ◽  
Residual Stresses ◽  
Process Parameters ◽  
Treatment Process ◽  
Ball Bearing ◽  
Grinding Process ◽  
Heat Treatment Process ◽  
Grinding Processes ◽  
Bearing Rings

Experimental investigation of residual stresses after heat treatment and grinding processes in the production of ball bearing rings has been carried out. The residual stresses were measured by X-ray diffraction method utilizing chromium radiation, which has an average penetration depth of 5 μm incident on 100Cr6 (AISI-E52100) ball bearing steel. The process parameters of heat treatment and grinding processes were varied so as to represent the extreme values that can be applied in the respective processes. Hardness and percent retained austenite limit the heat treatment process parameters; while roundness, surface roughness and form the grinding process. Tensile surface residual stresses on the raceway of ball bearing rings changes to compression after grinding in both circumferential and axial directions. In grinding relatively higher compressive stresses were measured in axial direction compared to the circumferential direction. This experimental investigation also showed that the influence of heat treatment process parameters on the magnitude and distribution of residual stresses survived even after grinding process; i.e. heat treatment and grinding processes cannot be evaluated independently in process design for favourable residual stresses.

An Effect of Process Parameters to Anodic Thickness in Hard Anodizing Process

2016 ◽  
Vol 872 ◽  
pp. 168-172
Author(s):  
Aunyanat Rattanasatitkul ◽  
Suksan Prombanpong ◽  
Pongsak Tuengsook
Keyword(s):  
Experimental Design ◽  
Oxide Film ◽  
Aluminum Oxide ◽  
Process Parameters ◽  
Cycle Time ◽  
Treatment Process ◽  
Aluminum Surface ◽  
Design Technique ◽  
Defect Rate ◽  
Hard Anodizing

The anodizing process is an aluminum surface treatment process which an aluminum oxide film forms on an aluminum substrate. Typically, the anodic thickness is a required specification which depends upon current density and anodizing cycle time. In addition, another important factor is ramp time which must be proper set to prevent a burn defect. Thus, this paper investigates a relationship among these three factors to determine the setting condition which minimizes the anodizing cycle time. Moreover, the required thickness must be obtained without increasing the burn defect rate. The experimental design technique is proposed to achieve this goal and it is found that the current of 35 amp, ramp time of 340sec and anodizing time at 1400 sec ensure the obtained anodic thickness greater than 30 micron.

Robuste W-Temper-Umformung von hochfestem Aluminium/Robust W-Temper forming of high-strength aluminum – How time and quenching method affect the mechanical properties of EN AW-7075

2020 ◽  
Vol 110 (10) ◽  
pp. 697-703
Author(s):  
Janosch Günzel ◽  
Timon Suckow ◽  
Ciarán-Victor Veitenheimer ◽  
Joachim Hauß ◽  
Peter Groche
Keyword(s):  
Mechanical Properties ◽  
Aluminum Alloys ◽  
Process Control ◽  
Temperature Control ◽  
Process Parameters ◽  
High Strength ◽  
Multi Stage ◽  
Quenching Method ◽  
High Strength Aluminum Alloys

Aufgrund ihrer geringen Kaltumformbarkeit werden hochfeste Aluminiumlegierungen in temperaturunterstützten Prozessrouten umgeformt. Bei mehrstufigen Prozessen führt dies zu komplexen und störanfälligen Prozessfolgen. Eine Umformung im W-Temper-Zustand vereinfacht die Temperaturführung und steigert die Robustheit. Die hierbei möglichen Prozessführungen sowie die Einflüsse der relevanten Prozessparameter (Zeit und Abschreckmethode) sind Inhalt dieses Beitrags.   Due to their low cold formability, high-strength aluminum alloys are formed in temperature-supported process routes. This leads to complex and failure-prone process sequences in multi-stage processes. Forming in the W-Temper state simplifies temperature control and increases robustness. This paper deals with the possible process control as well as the influences of the relevant process parameters (time and quenching method).

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.

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