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Performance Comparison Of Different Regression Methods For A Polymerization Process With Adaptive Sampling
Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a processdoi:10.5281/zenodo.1126728 fatcat:swhl7r22jjb75dscqox623az2i