Selected Examples of the Dimensional-analytical Treatment of Processes in the Field of Mechanical Unit Operations

2006 ◽  
pp. 125-179
Author(s):  
D.E. Jesson ◽  
S. J. Pennycook

It is well known that conventional atomic resolution electron microscopy is a coherent imaging process best interpreted in reciprocal space using contrast transfer function theory. This is because the equivalent real space interpretation involving a convolution between the exit face wave function and the instrumental response is difficult to visualize. Furthermore, the crystal wave function is not simply related to the projected crystal potential, except under a very restrictive set of experimental conditions, making image simulation an essential part of image interpretation. In this paper we present a different conceptual approach to the atomic imaging of crystals based on incoherent imaging theory. Using a real-space analysis of electron scattering to a high-angle annular detector, it is shown how the STEM imaging process can be partitioned into components parallel and perpendicular to the relevant low index zone-axis.It has become customary to describe STEM imaging using the analytical treatment developed by Cowley. However, the convenient assumption of a phase object (which neglects the curvature of the Ewald sphere) fails rapidly for large scattering angles, even in very thin crystals. Thus, to avoid unpredictive numerical solutions, it would seem more appropriate to apply pseudo-kinematic theory to the treatment of the weak high angle signal. Diffraction to medium order zero-layer reflections is most important compared with thermal diffuse scattering in very thin crystals (<5nm). The electron wave function ψ(R,z) at a depth z and transverse coordinate R due to a phase aberrated surface probe function P(R-RO) located at RO is then well described by the channeling approximation;


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.


2016 ◽  
Vol 7 (11) ◽  
pp. 837-846
Author(s):  
A. El-Sayed ◽  
M. Megahed ◽  
Y. Ramadan ◽  
A. El-Beba

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