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BRIEF RESEARCH REPORT article

Front. Chem. Eng.
Sec. Computational Methods in Chemical Engineering
Volume 7 - 2025 | doi: 10.3389/fceng.2025.1490825

Data Analysis-based Framework for the Design and Assessment of Chemical Process Plants: A Case Study in Amine Gas-Treating Systems

Provisionally accepted
Rahul Gupta Rahul Gupta 1Gladys Navas Gladys Navas 2Daniela Galatro Daniela Galatro 1*
  • 1 University of Toronto, Toronto, Canada
  • 2 IUTFRP/UNETRANS, Caracas, Venezuela

The final, formatted version of the article will be published soon.

    This work presents a process-integrity assessment framework to chemical process design that combines first principles, heuristics, vendor specifications, standards/codes, data analysis, and machine learning modelling, hypothesized as an efficient route for optimal process design. Our case study, a gas treating unit, illustrates its implementation compared with traditional process guidelines. Surrogate models are fitted with hybrid data from process simulation and plant values, supporting the integration between process and integrity values, as well as equipment sizing and cost estimation. Considerable errors are obtained when estimating design duty (1.4 -8.7%) and power requirements (11.1 -33.5%) of the main equipment. Potential sources of these deviations might be attributable to the inherent simplification of process guidelines and intrinsic noise of the plant data used for fitting surrogate models. The process design is then assessed by evaluating process variables and corrosion rate within an operational envelope, showing the synergy and integration of these variables. The benefits and challenges of this approach are drawn while future work in engineering education is presented for its future implementation and effectiveness assessment in enhancing the process design workflow.

    Keywords: data analysis, Chemical process design, surrogate models, Plant integrity, Amine gas treating

    Received: 04 Sep 2024; Accepted: 02 Jan 2025.

    Copyright: © 2025 Gupta, Navas and Galatro. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Daniela Galatro, University of Toronto, Toronto, Canada

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.