Abstract In the Industry 4.0 era, the chemical industry is embracing broad adoption of
artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic …

Batch and continuous reactors both enable exploration of a chemical design space. The
former rely on transient experiments, thus experiencing a wide variety of operating …

This study presents a novel multi-step lookahead Bayesian optimization method which
strives for optimal active learning by balancing exploration and exploitation over multiple …

This study aims to design both the batch time and the process variable (PV) profiles (x) to
ensure that the endpoints (y), such as the product quality and the material properties …

Hit rate is an important quantitative criterion for the process product quality prediction of the
integrated industrial processes. The hit rate indicates the percentage of product quantities …

In the current study, the ability of two data‐driven machine learning tools, the extreme
learning machine (ELM) and the adaptive neuro‐fuzzy inference system (ANFIS), to predict …

In this paper, we use two data-driven modeling methodologies that we have been recently
developed: the design of dynamic experiments (DoDE)(Georgakis 2013) and the dynamic …

In this work, a hierarchical‐linked batch‐to‐batch optimization based on transfer learning is
proposed to realize the effective optimization of a new synthesis process. Optimization …

For the fed-batch ethanol production process, the feed rate is the key to determining the total
yield. This paper optimizes the feed rate based on the Kriging model by using the design of …

Hit rate is a key performance metric in predicting process product quality in integrated
industrial processes. It represents the percentage of products accepted by downstream …