Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …

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

This paper presents a novel derivative-free global optimization algorithm Branch-and-Model
(BAM). The BAM algorithm partitions the search domain dynamically, builds surrogate …

The study and application of contemporary optimization techniques considerably enhance
the efficiency of chemical research and manufacturing. With the dynamic progression of …

Modern thermodynamic models, such as the PC-SAFT equation of state, are very accurate
but also computationally intensive, which limits their applicability to process design …

As property and process models with many variables need to be considered, integrated
computer‐aided molecular and process design (CAMPD) problems are computationally …

Scheduling flexible sources to promote the integration of renewable generation is one
fundamental problem for operating active distribution networks (ADNs). However, existing …

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

The separation of ternary azeotropes with a high content of one component using extractive
distillation is energy intensive, and the introduction of preconcentration can significantly …

Complex physical or numerical systems may exhibit distinct behaviors in various zones of
their design spaces. We present an algorithm that uses multiple cluster-based surrogates for …