Drilling wells in challenging oil/gas environments implies in large capital expenditure on
wellbore's construction. In order to optimize the drilling related operation, real-time decisions …

This paper presents a literature review of reinforcement learning (RL) and its applications to
process control and optimization. These applications were evaluated from a new …

In this paper we introduce the current thrust of development in machine learning and
artificial intelligence, fueled by advances in statistical learning theory over the last 20 years …

This work examines applying deep reinforcement learning to a chemical production
scheduling process to account for uncertainty and achieve online, dynamic scheduling, and …

Abstract β-Carotene has a positive impact on human health as a precursor of vitamin A.
Building a kinetic model for its production using Saccharomyces cerevisiae in a batch …

Advanced model‐based controllers are well established in process industries. However,
such controllers require regular maintenance to maintain acceptable performance. It is a …

In this paper, we present both academic and industrial perspectives on the research and
applications of Process Systems Engineering (PSE). After a brief introduction on the history …

Hydraulic fracturing is a technique to extract oil and gas from shale formations, and
obtaining a uniform proppant concentration along the fracture is key to its productivity …

Supply chain operations and scheduling are well-studied problems in the literature.
Although these problems are related, they are often solved sequentially. This uncoordinated …

This paper explores a reinforcement learning (RL) approach that designs automatic control
strategies in a large-scale chemical process control scenario as the first step for leveraging …