This paper evaluates the four leading techniques proposed in the literature for construction
of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian …

Artificial neural networks (ANN) have been widely used for thermal analysis of heat
exchangers during the last two decades. In this paper, the applications of ANN for thermal …

Chemical engineers rely on models for design, research, and daily decision-making, often
with potentially large financial and safety implications. Previous efforts a few decades ago to …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Artificial neural networks are used in various applications for data-driven black-box
modeling and subsequent optimization. Herein, we present an efficient method for …

Many quality improvement (QI) programs including six sigma, design for six sigma, and
kaizen require collection and analysis of data to solve quality problems. Due to advances in …

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 …

One of the conditions of submission of a paper for publication is that authors declare
explicitly that their work is original and has not been submitted to nor appeared in another …

Energy is a key driver of the modern economy, therefore modeling and simulation of energy
systems has received significant research attention. We review the major developments in …

Abstract System health monitoring is a set of activities performed on a system to maintain it
in operable condition. Monitoring may be limited to the observation of current system states …