Brief Report - (2022) Volume 9, Issue 12
Received: 01-Dec-2022, Manuscript No. JLOP-23-86667;
Editor assigned: 05-Dec-2022, Pre QC No. P-86667;
Reviewed: 19-Dec-2022, QC No. Q-86667;
Revised: 24-Dec-2022, Manuscript No. R-86667;
Published:
30-Dec-2022
, DOI: 10.37421/2469-410X.2022.9.57
Citation: Rowling, George. “Instrumentation and Flow Monitoring
Using Process Tomography.” J Laser Opt Photonics 9 (2022): 57.
Copyright: © 2022 Rowling G. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Examples of such operations include the defined sequence of operations reaction, separation, crystallization, solidification, mixing, and drying. In order to accomplish this, the modern process industry frequently makes use of control systems that include local sensors for, among other things, temperature, pressure, flow, and filling level [1]. Utilizing more complex sensors in industrial control systems is becoming increasingly popular as sensor technology advances. Process tomography sensors are a member of this category.
A well-known imaging technique, process tomography is used to gather information in two dimensions or three dimensions about the distribution and flow of materials in pipes and vessels. In contrast to its counterparts in medical diagnostics and non-destructive testing, process tomography frequently prioritizes quick scanning speeds over high spatial resolution. In recent years, a number of process tomography methods have been developed [2]. A few examples include optical tomography, electrical tomography, magnetic tomography, ultrasonic tomography, microwave tomography, and so on. However, in order to examine industrial processes, several traditional tomography modalities, including X-ray tomography, emission tomography, and magnetic resonance imaging, have been accelerated. Since real-time reconstruction and feature extraction were difficult to achieve, tomographic imaging has not received much attention in industrial process control in the past [3].
On the other hand, the game has changed as a result of recent advancements in powerful and intelligent huge parallel computing architectures. For tomography-based process control, process tomography is currently a powerful sensor component. Researchers from all fields are encouraged to submit their most recent technological and scientific advancements in the field of process control employing process tomography techniques for consideration in the Special Issue. A comprehensive demonstration of this technology for common industrial processes, ideally in chemical, environmental, and energy engineering, is the focus of this Special Issue [4]. The demonstration can take place in a laboratory or realworld industrial systems. Tomography sensors, data processing, and control algorithms should all be part of the technical solutions that are presented, as well as at least a proof of concept. Novel theoretical control concepts for the use of tomography sensors in control loops and novel hardware and software concepts, such as real-time tomographic sensing and data processing, may also be addressed in contributions [5].
There are two distinct advantages of industrial process tomography over conventional sensing techniques. Process tomography systems provide information on the 2D or 3D distributions of the variables of interest to begin. Second, tomography examines processes without physically entering them, making it possible to sense even in the most extreme conditions and without interfering with the activity of the processes. The potential for closed-loop control applications is one of the primary driving motivations for the advancement of industrial tomography. These benefits open up new possibilities in the field of process control. Tomography closed-loop control applications are still uncommon despite these advantages and decades of development.
Attempting it since real-time reconstruction and feature extraction were challenging to accomplish, tomographic imaging has historically received little attention in industrial process control. On the other hand, recent developments in robust and clever massively parallel computer systems have changed the game. Currently, process tomography is a powerful sensor for tomography-based process control. The Special Issue invites researchers from across the academic spectrum to submit their most current findings in the fields of process control and process tomography. This Special Issue has a strong emphasis on a thorough description of this technology for typical industrial processes.
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