This work addresses recent developments for solving problems in process systems
engineering based on machine learning algorithms. A general description of most popular …

Abstract Among digital technologies, Artificial Intelligence (AI) and Big Data (BD) have
proven capability to support different processes, mainly in discrete manufacturing. Despite …

The surface tension at the critical micelle concentration (ST CMC) is an important descriptor
for surfactants in applications including cosmetics, pharmaceuticals and food. A predictive …

Abstract pH adjustment is crucial for many industrial products, yet this step is typically
performed by manual trial‐and‐error. A particularly industrially relevant yet challenging …

Liquid formulations are ubiquitous yet have lengthy product development cycles owing to
the complex physical interactions between ingredients making it difficult to tune formulations …

Environmental management systems (EMS) are essential in promoting sustainable practices
and mitigating the adverse effects of human activities on the environment. As technology …

The ability to predict transport properties of fluids, such as the self-diffusion coefficient and
viscosity, has been an ongoing effort in the field of molecular modeling. While there are …

Computational workflows are important methods for automating complex data-generation
and analysis pipelines. Workflows are composed of sub-graphs that perform specific tasks …

With Moore's law progressively running out of steam, heterogeneous computing
architectures have been powering the top supercomputers in the world for many years and …

We present a new chemical representation (the CCS fingerprint) and data set (ccs-98) for
carbon capture solvents. We then assess the chemical space, data availability and utility of …