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- W2386068052 abstract "A new heterogeneous catalysis modeling methodology, namely support vector machine (SVM) and phase space reconstruction (PSR) was presented, for catalyst compositional models and catalytic reaction mechanism models, for reducing both high temporal costs and financial costs, and accelerating the process of industrialization synthesis of dimethyl ether (DME). In the SVM-PSR approach, a novel machine learning algorithms, namely support vector regression, was utilized for developing catalytic kinetic models. A SVM model was constructed for correlating process data comprising input variables of catalyst compositional, operating and output variables of performance of catalyst (catalyst selectivity and conversion). The input space of SVM model was alternated by phase space reconstruction (PSR), and one-dimensional time sequence was mapped into hyperspace for improving the forecasting precision. Finally, this algorithm was verified experimentally to be feasible. As a new strategy for heterogeneous catalysis modeling and optimization, the major advantage of the SVM-PSR is that modeling and optimization can be conducted exclusively from the historic small sample space data wherein the detailed knowledge of process phenomenology (reaction mechanism, rate constants, etc.) is not required and difficult to get. Another advantage is that it has avoided the blindness and contingency of the traditional catalyst trial and error method. The predicted performance numbers of catalyst is not only rich of experimental data, also agreed well with the experimentally obtained data. All of these results clearly demonstrate that SVM-PSR strategy is an effective tool in the heterogeneous catalysis modeling." @default.
- W2386068052 created "2016-06-24" @default.
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- W2386068052 date "2009-01-01" @default.
- W2386068052 modified "2023-09-24" @default.
- W2386068052 title "Heterogeneous catalysis modeling strategies integrating support vector machine and phase space reconstruction" @default.
- W2386068052 hasPublicationYear "2009" @default.
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