Part of the monthly webinar series from the RSC Process Chemistry and Technology group
Over the past decade, the rapid growth of machine learning techniques and the increasing volume of data generated by the process industry have led to a significant shift from physical modelling to more data-driven modelling for reaction engineering applications.鈥� This quote is taken from the preface of a new RSC book: Machine Learning and Hybrid Modelling for Reaction Engineering. Phil Kay, Principal Systems Engineer at JMP Statistical Discovery, co-authored a chapter of this book and in this webinar he will use the same case study to illustrate the value of sequential statistical design of experiments in reaction modelling and optimisation. He will introduce the broader themes of the book and discuss the drive for more use of machine learning and hybrid modelling in reaction engineering. He will also talk briefly about his role as committee chair for the Process Chemistry and Technology Group and the aims of this interest group of the 萝莉社.
Over the past decade, the rapid growth of machine learning techniques and the increasing volume of data generated by the process industry have led to a significant shift from physical modelling to more data-driven modelling for reaction engineering applications.鈥� This quote is taken from the preface of a new RSC book: Machine Learning and Hybrid Modelling for Reaction Engineering. Phil Kay, Principal Systems Engineer at JMP Statistical Discovery, co-authored a chapter of this book and in this webinar he will use the same case study to illustrate the value of sequential statistical design of experiments in reaction modelling and optimisation. He will introduce the broader themes of the book and discuss the drive for more use of machine learning and hybrid modelling in reaction engineering. He will also talk briefly about his role as committee chair for the Process Chemistry and Technology Group and the aims of this interest group of the 萝莉社.
