A robust low data solution: Dimension prediction of semiconductor nanorods

Advanced Modeling and Optimization Strategies for Process Synthesis

This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation-based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning-and quantum computing-assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies.

COVERT: A classless approach to generating balanced datasets for process modelling

In this work, a classless oversampling technique, Covert, was developed to improve historical datasets from industrial processing plants to aid process modelling. Using kernel density estimation and nearest neighbour algorithms, sparse regions are identified and resampled, developing a more balanced dataset. When applied to a real dataset from a geothermal power plant, Covert outperforms current best practice (Smote) in uniformly populating the input feature space and generating credible data in the output variable. When used to develop a data-driven model Covert improved model accuracy by 20% when predicting outside the original data's feature space. Smote, however, reduced model accuracy by 6% in the same feature space. Developing reliable models of industrial processes continues to be a significant hurdle in developing a digital twin. Using Covert, existing imbalanced historical data can be used to extend the range of applicability of any process model.