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AbstractIn the past decades, process engineers are facing increasingly more data analytics challenges and having difficulties obtaining valuable information from a wealth of process variable data trends. The raw data of different formats stored in databases are not useful until they are cleaned and transformed. Generally, data cleaning consists of four steps: missing data imputation, outlier detection, noise removal, and time alignment and delay estimation. This paper discusses available data cleaning methods that can be used in data pre-processing and help overcome challenges of “Big Data”.

Major advances that improve control in the process industry have been made over the last ten years in the basic PID technology of modern distributed control systems. This paper addresses the impact that international standards have on control implementation and the tools utilized in industry for monitoring and commissioning PID control. Examples are used to illustrate how new technologies, such as model switching for process identification, have allowed manufacturers to introduce a new level of ease-of-use in tools developed for on-demand and adaptive tuning.This paper discusses PID modifications that improve the speed of recovery from process saturation conditions that are common in industrial applications. Also, details are provided on PID modifications that enable effective control with non-periodic measurement updates by wireless transmitters. Finally, prospective future directions for industrial PID controllers are sketched.