{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/41738"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/41738","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Degradation analyses of empirical inferential predictors for the development of improved dynamic mechanistic/empirical equations","abstract":"The paper presents an in-depth exploration of a debutaniser distillation column, a critical component in a typical separation train. The primary function of this unit is to separate the upstream distillation column product flow into LPG and a heavier stream of catalytic naphtha. The operation of the Debutaniser is crucial for maintaining the total C5 vol% and RVP within specified limits, ensuring optimal operation of downstream units. Given the high costs associated with real-time analysers, the study explores the development of various modelling techniques, including principal component analyses, decision trees, random forests, gradient boosting, neural networks and partial least squares, to optimize the prediction accuracy and process control. By leveraging these models, the study aims to enhance the automation and optimization of process units within chemical process plants, ultimately contributing to the overall efficiency of the chemical process plant.","abstract_html":"The paper presents an in-depth exploration of a debutaniser distillation column, a critical component in a typical separation train. The primary function of this unit is to separate the upstream distillation column product flow into LPG and a heavier stream of catalytic naphtha. The operation of the Debutaniser is crucial for maintaining the total C5 vol% and RVP within specified limits, ensuring optimal operation of downstream units. Given the high costs associated with real-time analysers, the study explores the development of various modelling techniques, including principal component analyses, decision trees, random forests, gradient boosting, neural networks and partial least squares, to optimize the prediction accuracy and process control. By leveraging these models, the study aims to enhance the automation and optimization of process units within chemical process plants, ultimately contributing to the overall efficiency of the chemical process plant.","abstract_has_math":false,"creators":["Mammen, Ashlen"],"institution":"Department of Chemical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Moller, Klaus"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-22T22:23:05Z","subjects":["Degradation analyses"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/41738","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Moller, Klaus"]},{"key":"dc:creator","label":"Author","values":["Mammen, Ashlen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-10T07:38:31Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-10T07:38:31Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Chemical Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Thesis / Dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Degradation analyses"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/41738"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The paper presents an in-depth exploration of a debutaniser distillation column, a critical component in a typical separation train. The primary function of this unit is to separate the upstream distillation column product flow into LPG and a heavier stream of catalytic naphtha. The operation of the Debutaniser is crucial for maintaining the total C5 vol% and RVP within specified limits, ensuring optimal operation of downstream units. Given the high costs associated with real-time analysers, the study explores the development of various modelling techniques, including principal component analyses, decision trees, random forests, gradient boosting, neural networks and partial least squares, to optimize the prediction accuracy and process control. 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