Industrial process data are naturally complex time series with high nonlinearities and
dynamics. To model nonlinear dynamic processes, a long short-term memory (LSTM) …

Significant research has been performed on credit risk evaluation, with many machine
learning and data mining techniques being employed for financial decision-making. The …

Expert and intelligent control schemes have recently emerged out as a promising solution
with robustness which can efficiently deal with the nonlinearities, along with various types of …

In nonlinear industrial processes, some product qualities or key variables are usually difficult
to measure online automatically due to the lack of sensors. In this work, a novel data-driven …

The goal of this paper is to design and implementation of a new data-driven soft sensor that
uses state dependent parameter (SDP) models to improve product quality monitoring. The …

Digital Twins (DTs) are a core enabler of Industry 4.0 in manufacturing. Cognitive Digital
Twins (CDTs), as an evolution, utilize services and tools towards enabling human-like …

With low oil prices, the existing coal-to-olefin enterprises are forced to improve profitability by
reducing energy consumption. This paper studies the comprehensive energy analysis and …

In this work, data-driven soft sensors are developed for the debutanizer column for online
monitoring of butane content in the debutanizer column bottom product. The data set …

The energy efficiency in the debutanizer reboiler combustion can be monitored from the
oxygen content of the flue gas of the reboiler. The measurement of the oxygen content can …

This paper proposes a soft sensor to estimate phase flow rates utilizing common
measurements in oil and gas production wells. The developed system addresses the limited …