On monitoring industrial processes under feedback control

Monitoring Industrial Processes with Robust Control Charts

• The Shewhart control charts, used for monitoring industrial processes, are the most popular tools in Statistical Process Control (SPC). They are usually developed under the assumption of independent and normally distributed data, an assumption rarely true in practice, and implemented with estimated control limits. But in general, we essentially want to control the process mean value and the p...

Online Monitoring for Industrial Processes Quality Control Using Time Varying Parameter Model

A novel data-driven soft sensor is designed for online product quality prediction and control performance modification in industrial units. A combined approach of time variable parameter (TVP) model, dynamic auto regressive exogenous variable (DARX) algorithm, nonlinear correlation analysis and criterion-based elimination method is introduced in this work. The soft sensor performance validation...

The Opportunities Afforded by Embedded Computer Systems for Monitoring and Control of Industrial Processes in Less-Industrialised Countries (TECHNICAL NOTE)

The dramatic changes in integrated-circuit technology over the last two decades have been of great benefit to countries such as Zimbabwe. High volume production of VLSI chips has produced a supply of intelligent, versatile electronic processing devices at very low cost. In particular the facilities of the microcontroller have steadily developed to the accompaniment of a reduction in price. Sinc...

Statistical Process Monitoring of Industrial Batch Processes

The manufacture of high-value products involves many different batch processes, for example industrial fermenters. Such processes require high levels of consistency in their operation to ensure minimal losses of raw materials, utilities and product. Recent application studies have indicated that multivariate statistical technology can provide some support when trying to maintain consistent oper...

Pollution Control in Industrial Processes