Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …

It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …

This study evaluated the impact of startup technology innovations and customer relationship
management (CRM) performance on customer participation, value co-creation, and …

Missing data widely exist in industrial processes and lead to difficulties in modeling,
monitoring, fault diagnosis, and control. In this article, we propose a nonlinear method to …

While machine learning (ML) has made significant contributions to the biopharmaceutical
field, its applications are still in the early stages in terms of providing direct support for quality …

Data pre-processing is a technique that transforms the raw data into a useful format for
applying machine learning (ML) techniques. Feature selection (FS) and feature extraction …

Alluvial sand and gravel (ASG) aquifers are highly heterogeneous and exhibit strong,
spatially variable anisotropy, often interspersed by buried paleo‐channels of increased …

In this work, we present an integrated data-driven modeling and global optimization-based
multi-period nonlinear production planning framework that is applied to a real-life refinery …

The crop yield prediction (CYP) has a high significance in agriculture. Early crop yield
predictions assist the farmers, decision-makers in making timely decisions during the actual …

As batteries become widespread applications across various domains, the prediction of
battery cycle life has attracted increasing attention. However, the intricate internal …