Roll to Roll Sputtered Titanium and TiO2 From a Dual Rotatable Cathode, Using Open & Closed Loop Process Control and Comparing AC and Square Wave Bipolar Power Delivery

Author(s):  
David Wickens ◽  
◽  
N. Butcher ◽  
Author(s):  
Maia R. Bageant ◽  
David E. Hardt

Microfluidic technologies hold a great deal of promise in advancing the medical field, but transitioning them from research to commercial production has proven problematic. We propose precision hot embossing as a process to produce high volumes of devices with low capital cost and a high degree of flexibility. Hot embossing has not been widely applied to precision forming of hard polymers at viable production rates. To this end we have developed experimental equipment capable of maintaining the necessary precision in forming parameters while minimizing cycle time. In addition, since equipment precision alone does not guarantee consistent product quality, our work also focuses on real-time sensing and diagnosis of the process. This paper covers both the basic details for a novel embossing machine, and the utilization of the force and displacement data acquired during the embossing cycle to diagnose the state of the material and process. The precision necessary in both the forming machine and the instrumentation will be covered in detail. It will be shown that variation in the material properties (e.g. thickness, glass transition temperature) as well as the degree of bulk deformation of the substrate can be detected from these measurements. If these data are correlated with subsequent downstream functional tests, a total measure of quality may be determined and used to apply closed-loop cycle-to-cycle control to the entire process. By incorporating automation and specialized precision equipment into a tabletop “microfactory” setting, we aim to demonstrate a high degree of process control and disturbance rejection for the process of hot embossing as applied at the micron scale.


Author(s):  
Brad Bullington

The power block for a conventional Concentrated Solar Power (CSP) Plant without thermal storage follows standard power block design practices. A closed loop heat transfer fluid (HTF) is heated in the solar field, which consists of multiple solar collector assemblies (SCAs). Heat exchangers use the heat from the HTF to generate and superheat steam. The steam is sent to a steam turbine, which generates electricity. The cooled HTF is recirculated back to the solar field. In an effort to shift the period of power generation or to maintain full power output during non-peak periods of operation, a thermal energy storage (TES) system can be added. This entails adding a second closed loop fluid that is heated by the HTF during sufficient radiation hours, which in turn can heat the HTF that is supplied to the power block during periods of non-peak radiation. This article discusses the process control and design issues for the integrated solar field, TES system and power block for these plants. The article will address the following: 1) Operations with the Solar field on-line, TES system off-line, and STG on-line. 2) Operations with the Solar field on-line, TES system charging, and STG on-line. 3) Operations with the Solar field on-line, the TES system discharging, and STG on-line. 4) Operations with the solar field off-line, the TES system discharging, and the STG on-line. 5) Operations with the Solar field on-line, the TES system charging, and STG off-line. 6) Steam Turbine Issues. 7) Freeze protection. 8) HTF/TES Heat Exchanger. 9) Circulating Water and Surface Condenser.


2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jianhua Zhang ◽  
Junghui Chen

A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID) iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.


2011 ◽  
Vol 183 (11) ◽  
pp. 1282-1295 ◽  
Author(s):  
Andrew D. Sappey ◽  
Pat Masterson ◽  
Eric Huelson ◽  
Jim Howell ◽  
Mike Estes ◽  
...  

2008 ◽  
Author(s):  
Björn Sass ◽  
Ralf Schubert ◽  
Thomas Jakubski ◽  
Sebastian Mauermann ◽  
Pavel Nesladek ◽  
...  

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