Among the greatest challenges in soft sensor development for bioprocesses are variable
process lengths, multiple process phases, and erroneous model inputs due to sensor faults …

This paper reviews system-level transformations converging into the next generation of
Process Intensification strategies defined as PI4. 0. Process Intensification 4.0 uses data …

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 …

For effective monitoring and control of the fermentation process, an accurate real-time
measurement of important variables is necessary. These variables are very hard to measure …

Soft sensors are mathematical models employed to estimate hard-to-measure variables from
available easy-to-measure variables. These sensors are typically developed using either …

Deep belief network (DBN) has recently been applied for soft sensor modeling with its
excellent feature representation capacity. However, DBN cannot guarantee that the …

Batch process quality prediction is an important application in manufacturing and chemical
industries. The complexity of batch processes is characterized by multiphase, nonlinearity …

In modern industrial processes, dynamics and nonlinearities are two main difficulties for soft
sensing of key quality variables. Thus, nonlinear dynamic models like long short-term …

Most applications of soft sensors in process industries require learning from a stream of
data, which may exhibit nonstationary dynamics, or concept drift. In this study, we develop a …

Soft sensor technique has become a promising solution to enable real-time estimations of
difficult-to-measure quality variables in industrial processes. However, traditional soft sensor …