Data-Driven Process System Engineering–Contributions to its consolidation following the path laid down by George Stephanopoulos
Computers & Chemical Engineering, ISSN: 0098-1354, Vol: 159, Page: 107675
2022
- 4Citations
- 82Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The number and diversity of Process Analytics applications is growing fast, impacting areas ranging from process operations to strategic planning or supply chain management. However, this field has not reached yet a maturity level characterized by a stable, organized and consolidated body of knowledge for handling the main classes of problems that need to be faced. Data-Driven Process Systems Engineering and Process Analytics only recently received wider recognition, becoming a regular presence in journals and conferences. As a tribute to the groundbreaking Process Analytics contributions of George Stephanopoulos, namely through his academic tree, to which we are proud to belong, this article aims to contribute to the systematization and consolidation of this field in the broad PSE scope, starting from a fundamental understanding of the key challenges facing it, and constructing from them a workflow that can flexibly be adapted to handle different problems, aimed at supporting value creation through good decision-making. In this path, we base our foresight and conceptual framework on the authors’ experience, as well as on contributions from other researchers that, across the world, have been collectively pushing forward Data-Driven Process Systems Engineering.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0098135422000199; http://dx.doi.org/10.1016/j.compchemeng.2022.107675; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85123602087&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0098135422000199; https://dx.doi.org/10.1016/j.compchemeng.2022.107675
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know