Papermaking wastewater contains a diverse range of organic carbon, nitrogen and other
pollutants, which could lead to the emission of greenhouse gases (GHG) during biochemical …

We provide insights for the development of a fast, dynamic, and adaptive way of modeling
and optimizing chemical processes. We investigate two data-driven methodologies to …

This work provides an overview of recent applications of Machine Learning (ML) to process
systems engineering problems related to sustainability. The review is organized by the type …

Modular chemical process simulators are widespread in chemical industries to design and
optimize production processes with sufficient accuracy. However, convergence issues and …

Surrogate modeling is used to replace computationally expensive simulations. Neural
networks have been widely applied as surrogate models that enable efficient evaluations …

Thoroughly utilizing the first principles of chemical processes, industrial big data, and
artificial intelligence algorithms has been a deterministic trend in process modeling …

This work proposes a framework for simulation-based and surrogate-based reduced space
Bayesian optimization of process flowsheets. The framework uses global sensitivity analysis …

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 …

Optimization plays an important role in chemical engineering, impacting cost-effectiveness,
resource utilization, product quality, and process sustainability metrics. This chapter broadly …

Data-driven systems have been increasingly applied in industries to improve process
production and pattern analysis. To enhance an industrial vacuum metallization process, we …