Plantwide control of an SO2 abatement plant

dc.contributor.advisorDe Vaal, Philip L.
dc.contributor.coadvisorBrooks, Kevin
dc.contributor.emailminet.uys@tuks.co.zaen_US
dc.contributor.postgraduateCrafford, Minèt
dc.date.accessioned2024-02-12T09:38:18Z
dc.date.available2024-02-12T09:38:18Z
dc.date.created2024-04
dc.date.issued2023
dc.descriptionDissertation (MEng (Chemical Engineering))--University of Pretoria, 2023.en_US
dc.description.abstractThis study focused on an SO2 abatement plant for a platinum group metal (PGM) smelting electric furnace. A systematic approach, using a simulated model of the plant, was followed to investigate plantwide control measures and thereby refine the plant's control philosophy. A steady-state model of a Wet gas Sulfuric Acid plant was developed using Aspen HYSYS software. The model was converted to a dynamic model to enable the evaluation of interactions within the process. This dynamic model was used while implementing a top-down, bottom-up plantwide control procedure. The results produced a control structure by which the first converter’s inlet temperature controls the final SO2 concentration. The feed gas heater’s (second heater in the system) outlet temperature is controlled by varying the steam flow rate, which is used as a means of disturbance rejection. Furthermore, using a dynamic model to implement a systematic plantwide control procedure eliminates the need to develop complex mathematical models while providing the opportunity to continuously validate the decisions made and selected manipulated and controlled variable pairings. Additional benefits of using a dynamic simulation model to implement a plantwide control model are: • It provides a link between steady-state optimisation and process control. • Self-optimising control is considered. • Improved understanding of the process and interactions in the process. • Provides a base model with the possibility to apply the solution to similar plants with minimal adjustment. • The opportunity of implementing dynamic matric control or model predictive control models to live plants (software dependent). • Constant consideration of the control and operation of the plant as well as the overall (plantwide) control objective.en_US
dc.description.availabilityUnrestricteden_US
dc.description.degreeMEng (Chemical Engineering)en_US
dc.description.departmentChemical Engineeringen_US
dc.description.facultyFaculty of Engineering, Built Environment and Information Technologyen_US
dc.description.sdgSDG-09: Industry, innovation and infrastructureen_US
dc.identifier.citation*en_US
dc.identifier.doi10.25403/UPresearchdata.25202060en_US
dc.identifier.otherA2024en_US
dc.identifier.urihttp://hdl.handle.net/2263/94485
dc.identifier.uriDOI: https://doi.org/10.25403/UPresearchdata.25202060.v1
dc.language.isoenen_US
dc.publisherUniversity of Pretoria
dc.rights© 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
dc.subjectUCTDen_US
dc.subjectPlantwide control strategiesen_US
dc.subjectAcid plant
dc.subjectDynamic model
dc.subjectSO2 abatement
dc.subjectSimulation
dc.subject.otherSustainable Development Goals (SDGs)
dc.subject.otherEngineering, built environment and information technology theses SDG-09
dc.subject.otherSDG-09: Industry, innovation and infrastructure
dc.subject.otherEngineering, built environment and information technology theses SDG-12
dc.subject.otherSDG-12: Responsible consumption and production
dc.titlePlantwide control of an SO2 abatement planten_US
dc.typeDissertationen_US

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