TY - JOUR
T1 - Data-Driven Process System Engineering–Contributions to its consolidation following the path laid down by George Stephanopoulos
AU - Reis, Marco S.
AU - Saraiva, Pedro M.
N1 - Reis, M. S., & Saraiva, P. M. (2022). Data-Driven Process System Engineering–Contributions to its consolidation following the path laid down by George Stephanopoulos. Computers and Chemical Engineering, 159, 1-15. [107675]. https://doi.org/10.1016/j.compchemeng.2022.107675--------Funding Information: The authors acknowledge support from the Chemical Process Engineering and Forest Products Research center (CIEPQPF), which is financed by national funds from FCT/MCTES (reference UID/EQU/00102/2019 ).
PY - 2022/3/1
Y1 - 2022/3/1
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Data science
KW - Data-Driven PSE
KW - Industry 4.0
KW - Process Analytics
KW - Process Systems Engineering 4.0
UR - http://www.scopus.com/inward/record.url?scp=85123602087&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000754571300008
U2 - 10.1016/j.compchemeng.2022.107675
DO - 10.1016/j.compchemeng.2022.107675
M3 - Article
AN - SCOPUS:85123602087
SN - 0098-1354
VL - 159
SP - 1
EP - 15
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 107675
ER -