CDS&E: On-Line Extraction and Separation Method Development using Surrogate Optimization and Machine Learning

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Kevin Schug and his analytical chemistry group will collaborate with Victoria Chen, Shouyi Wang, Chen Kan, and Jay Rosenberger with the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at the University of Texas at Arlington to explore advanced optimization procedures for on-line analytical instrumentation. Trace chemical analysis often requires that a sample be prepared beforehand to selectively extract a certain class of compounds or to reduce interferences.

Manual sample preparation is common, but can introduce error relative to an automated approach. The Schug group focuses on an automated on-line sample preparation and chemical analysis platform utilizing powerful methods known as supercritical fluid extraction and chromatography. This analytical system is very broadly applicable to the measurement of a wide variety of chemical compounds.

However, there are many variables that need to be optimized, and their combined effect on the outcome of the measurement is difficult to rationalize. To address this challenge, the team is developing a machine learning-based surrogate optimization procedure to efficiently explore optimal conditions. The results of the surrogate optimization runs will be modeled using novel multi-task learning for “small data,” so as to provide general knowledge for approximating optimal instrumentation conditions.

The graduate and undergraduate students involved in this project will gain complementary experience in advanced data handling and analytical measurements. Results of the work will be widely disseminated at local, regional, and national conferences, and in peer-reviewed publications.

Online supercritical fluid extraction (SFE) coupled with supercritical fluid chromatography (SFC) offers automated sample preparation and chemical analysis over a very broad application space. The team is integrating statistics, machine learning, and operations research to enable SFE-SFC method development. SFE-SFC, especially combined with mass spectrometry (MS) detection, has near-universal capabilities for comprehensive analysis of small molecules contained in solid samples.

In pursuit of these aims, the team is (1) exploring innovative representations for input features such as sample material, analyte, and SFC column chemistry; (2) developing new experimental design (DoE) processes over the input feature space and over the experimental parameter space that comply with the constraints of the SFE-SFC system; (3) implementing the DoE process within a rigorous multiple-objective surrogate optimization that incorporates provably-optimal techniques from mixed integer linear and quadratic programming; and (4) deriving "small data" multi-task learning algorithms integrating nonparametric Bayesian modeling with Gaussian processes to construct a flexible multi-response predictive model for the optimized parameters over the input feature space. SFE-SFC has the potential to supplant other methods that are off-line, less efficient, and less “green.” This research aims to vastly reduce the future burden for those who implement SFE-SFC.

Further, the developed DoE, machine learning, and surrogate optimization methodologies for "small data" will be generally applicable and available for a wide range of societal challenges in domains such as healthcare and “green” building, for which the sample of patients or buildings is typically limited.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.