With the predicted depletion of natural resources and alarming environmental issues,
sustainable development has become a popular as well as a much-needed concept in …

Data-driven modeling and applications in plant-wide processes have recently caught much
attention in both academy and industry. This paper provides a systematic review on data …

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

Biodiversity loss is happening at an unprecedented rate, especially in countries like
Vietnam, with rich biodiversity and a high population growth rate. One of the main causes of …

Soft sensor has been extensively utilized in industrial processes for prediction of key quality
variables. To build an accurate virtual sensor model, it is very significant to model the …

Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …

In modern industrial processes, soft sensors have played an important role for effective
process control, optimization, and monitoring. Feature representation is one of the core …

Deep learning is a recently developed feature representation technique for data with
complicated structures, which has great potential for soft sensing of industrial processes …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Unpredicted faults occurring in automation systems deteriorate the performance of soft
sensors and may even lead to incorrect results. To address the problem, this study develops …