Systematic Data-Driven Modeling of Bimetallic Catalyst Performance for the Hydrogenation of 5-Ethoxymethylfurfural with Variable Selection and Regularization.

Catalyst development for biorefining applications involves many challenges. Mathematical modeling can be seen as an essential tool in assisting to explain catalyst performance. This paper presents studies on several machine learning (ML) methods that can model the performance of heterogeneous catalysts with relevant descriptors. A systematic approach for selecting the most appropriate ML method is taken with focus on the variable selection. Regularization algorithms were applied to variable selection. Several different candidate model structures were compared in modeling with interpretation of results. The systematic modeling approach presented aims to highlight the necessary tools and aspects to unexperienced users of ML. Literature datasets for the hydrogenation of 5-ethoxymethylfurfural with simple bimetal catalysts, including main metals and promoters, were studied with the addition of catalyst descriptors found in the literature. Good results were obtained with the best models for estimating conversion, selectivity, and yield with correlations between 0.90 and 0.98. The best identified model structures were support vector regression, Gaussian process regression, and decision tree methods. In general, the use of variable selection procedures was found to improve the performance of models. The modeling methods applied thus seem to exhibit a strong potential in aiding catalyst development based mainly on the information content of descriptor datasets.

Experience in commissioning the halcyon linac.

PURPOSE: This manuscript describes the experience of two institutions in commissioning the new HalcyonTM platform. Its purpose is to: (a) validate the pre-defined beam data, (b) compare relevant commissioning data acquired independently by two separate institutions, and (c) report on any significant differences in commissioning between the Halcyon linear accelerator and other medical linear accelerators. METHODS: Extensive beam measurements, testing of mechanical and imaging systems, including the multi-leaf collimator (MLC), were performed at the two institutions independently. The results were compared with published recommendations as well. When changes in standard practice were necessitated by the design of the new system, the efficacy of such changes was evaluated as compared to published approaches (guidelines or vendor documentation). RESULTS: Given the proper choice of detectors, good agreement was found between the respective experimental data and the treatment planning system calculations, and between independent measurements by the two institutions. MLC testing, MV imaging, and mechanical system showed unique characteristics that are different from the traditional C-arm linacs. Although the same methodologies and physics equipment can generally be used for commissioning the Halcyon, some adaptation of previous practices and development of new methods were also necessary. CONCLUSIONS: We have shown that the vendor pre-loaded data agree well with the independent measured ones during the commission process. This verifies that a data validation instead of a full-data commissioning process may be a more efficient approach for the Halcyon. Measurement results could be used as a reference for future Halcyon users.