Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems
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 g...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Chemical Engineering |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fceng.2025.1490825/full |
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| author | Rahul Gupta Gladys Navas Daniela Galatro |
| author_facet | Rahul Gupta Gladys Navas Daniela Galatro |
| author_sort | Rahul Gupta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f3a162cfdc414402b2de777c130cdbf7 |
| institution | Kabale University |
| issn | 2673-2718 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemical Engineering |
| spelling | doaj-art-f3a162cfdc414402b2de777c130cdbf72025-01-20T05:23:43ZengFrontiers Media S.A.Frontiers in Chemical Engineering2673-27182025-01-01710.3389/fceng.2025.14908251490825Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systemsRahul Gupta0Gladys Navas1Daniela Galatro2Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, CanadaMaterial Science, IUTFRP/UNETRANS, Caracas, VenezuelaDepartment of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, CanadaThis 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.https://www.frontiersin.org/articles/10.3389/fceng.2025.1490825/fulldata analysischemical process designsurrogate modelsplant integrityamine gas treating |
| spellingShingle | Rahul Gupta Gladys Navas Daniela Galatro Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems Frontiers in Chemical Engineering data analysis chemical process design surrogate models plant integrity amine gas treating |
| title | Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems |
| title_full | Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems |
| title_fullStr | Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems |
| title_full_unstemmed | Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems |
| title_short | Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems |
| title_sort | data analysis based framework for the design and assessment of chemical process plants a case study in amine gas treating systems |
| topic | data analysis chemical process design surrogate models plant integrity amine gas treating |
| url | https://www.frontiersin.org/articles/10.3389/fceng.2025.1490825/full |
| work_keys_str_mv | AT rahulgupta dataanalysisbasedframeworkforthedesignandassessmentofchemicalprocessplantsacasestudyinaminegastreatingsystems AT gladysnavas dataanalysisbasedframeworkforthedesignandassessmentofchemicalprocessplantsacasestudyinaminegastreatingsystems AT danielagalatro dataanalysisbasedframeworkforthedesignandassessmentofchemicalprocessplantsacasestudyinaminegastreatingsystems |