Soft Sensor Applications in Industrial Vinylacetate‐ethylene (VAE) Polymerization Processes

Frauendorfer, Eric; Hergeth, Wolf‐Dieter

doi:10.1002/mren.201700008

Cited by 3 publications

(1 citation statement)

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“…SSs' industrial use ranges over a number of different types of processes, such as refineries [6][7][8][9][10][11], chemical plants [12], cement kilns [13,14], power plants [15,16], pulp and paper mills [17,18], food processing [2,19], nuclear plants [20,21], pollution monitoring [22], polymerization processes [23][24][25] or wastewater treatment systems [26][27][28][29], just to mention a few. In Figure 2, a distillation column process from a refinery is shown through its control software: displayed online measurements are collected in a historical database for future use.…”

Section: Introductionmentioning

confidence: 99%

Input Selection Methods for Soft Sensor Design: A Survey

2020

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Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-time estimation of hard-to-measure variables as a function of available data obtained from online sensors. SSs are generally built using industries historical databases through data-driven approaches. A critical issue in SS design concerns the selection of input variables, among those available in a candidate dataset. In the case of industrial processes, candidate inputs can reach great numbers, making the design computationally demanding and leading to poorly performing models. An input selection procedure is then necessary. Most used input selection approaches for SS design are addressed in this work and classified with their benefits and drawbacks to guide the designer through this step.

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Section: Introductionmentioning

confidence: 99%

Input Selection Methods for Soft Sensor Design: A Survey

2020

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Design and Applications of Soft Sensors in Polymer Processing: A Review

Abeykoon

2019

IEEE Sensors J.

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In manufacturing industry, process monitoring is a key to observe the product quality, operational health, safety and also for achieving good/satisfactory process control performance. In polymer processing, the level of control of the process operational quality is vastly dependent upon the performance of the process monitoring techniques as it is quite difficult to observe the inside of an extruder barrel (or processing chamber) during the process operation. Currently, a number of physical sensing devices/techniques are widely available for industrial applications for monitoring of parameters such as melt temperature, melt pressure, screw/processing speed, product dimensions, etc in real-time. However, there may be some limitations in using physical sensors in piratical process measurements (such as melt viscosity and melt temperature profile) due to several constraints such as their access requirements, disruptive effects to the melt flow, fragility, complexity, etc. Therefore, soft sensing techniques should be highly useful for improved process monitoring and modelling, and hence for advanced process control. In this work, a general description is presented on the state-of-the-art of soft sensor design. Then, a comprehensive review is made on the use of soft sensing techniques in polymer processing applications while identifying their capabilities and limitations. Eventually, the importance of developing of such soft sensing techniques (together with some sort of built-on intelligence) for the advancement of process monitoring is discussed while indicating some of the possible directions for future industry.

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