Conference: Foundations of Process/Product Analytics and Machine learning (FOPAM 2023)

University Of California-Davis, Davis CA

Investigators

Abstract

The conference on Foundations of Process/Product Analytics and Machine learning (FOPAM 2023) follows on the success of FOPAM 2019 and aims to bring together an international group of industrial and academic participants to discuss the status and future directions in chemical process data analytics and machine learning. NSF funding will support travel expenses for under-represented groups, early career faculty, postdocs, and Ph.D. students who otherwise would find it difficult to secure sufficient funds to attend the conference. The conference organizers’ aim is to influence how they will shape the future of data science for the process industries. Group interactions will be promoted via invited talks, discussions, poster sessions, and other unstructured activities in the afternoons. The conference starts with a plenary and welcome reception on the evening of July 31, 2023. The mornings and evenings of the next three days of the conference will be single-track oral sessions of invited speakers providing their perspectives on subtopics within the field of data analytics and machine learning, supplemented by question-and-answer periods and panel discussions. Two poster sessions will be included in the conference schedule, and the conference will be preceded by one and one-half days of optional workshops. The Chemical Process Industries (CPI) have seen unprecedented increases in the quantity and variety of data available for making decisions, increasing product quality, and gaining process and supply chain efficiencies. There are many unsolved theoretical and practical problems to address as the historically separate fields of machine learning, process design, computational chemical product development, process operation optimization, and supply-chain analysis converge. One major outcome of the Foundations of Process/Product Analytics and Machine learning (FOPAM 2023) conference will be to identify technology gaps in data analytics and machine learning relevant to the CPI. Researchers from each field will learn from each other and new directions will be set through discussions following oral and poster presentations, aided by conference rapporteurs. Position papers will set directions for research in process systems engineering and related areas for years to come. The conference will bring together industry and university researchers and application engineers in process data analytics and machine learning to assess what has been accomplished in the past five years and to examine where the field is heading. The following topics will be explored: 1) emerging methods in machine learning and data science; 2) industrial data-science applications; 3) machine learning for process and product computational chemistry; 4) data science for process design, optimization, and control; and 5) past and future of process analytics and machine learning, including education and workforce development. The funds requested from the National Science Foundation will be used to support 25 participants. Half of the funding will be used to support under-represented groups in science and engineering and the other half will be used to support early career researchers, which will be a combination of faculty members, postdocs, and graduate students. 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.