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Empirical modelling in non-linear predictive control : a coffee roaster application
Dissertation (MEng (Control Engineering))--University of Pretoria, 2023.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2024
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| _version_ | 1867613667676127232 |
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| access_status_str | Open Access |
| author2 | de Vaal, Philip L. |
| author_browse | de Vaal, Philip L. |
| author_facet | de Vaal, Philip L. |
| collection | Thesis |
| dc_rights_str_mv | © 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. |
| description | Dissertation (MEng (Control Engineering))--University of Pretoria, 2023. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/94529 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:47.699Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/94529 Empirical modelling in non-linear predictive control : a coffee roaster application de Vaal, Philip L. u17031096@tuks.co.za Bolt, Cameron E. UCTD Coffee roasting Model predictive control Process optimisation Hybrid modelling Machine learning Sustainable development goals (SDGs) Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Dissertation (MEng (Control Engineering))--University of Pretoria, 2023. This dissertation presents the development and implementation of a model predictive control (MPC) system for a coffee roasting process, to optimise roasting quality while minimising energy consumption. The study involved analysing historical temperature profile data and roaster inputs to develop a hybrid model, combining empirical and first principles techniques, which predicts the measured bean temperature as a function of the available roaster inputs. The combination of the first-principles model with empirical modelling techniques reduced validation data error by increasing measured temperature prediction accuracy. Subsequently, a nonlinear MPC was designed and tuned through a series of simulations, adjusting prediction and control horizons while limiting input changes relative to the real-time input value. The optimal configuration achieved a sig nificant reduction in the average usage of liquefied petroleum gas (LPG) while maintaining a wide input range. The impact of the intelligent modelling and control system on the reduction of raw material waste, the improvement of the quality of the final product, and the overall efficiency of the roasting process was evaluated, showing significant improvements in all three areas. The proposed system enables operators to perform simulations of roasts and reduce raw material wastage when developing roast profiles, providing a valuable contribution to the coffee roasting industry. Future work includes further investigation of hybrid modelling and nonlinear optimisation techniques. Chemical Engineering MEng (Control Engineering) Unrestricted Faculty of Engineering, Built Environment and Information Technology 2024-02-13T09:40:07Z 2024-02-13T09:40:07Z 2024-04 2023-12-12 Dissertation * A2024 http://hdl.handle.net/2263/94529 10.25403/UPresearchdata.25207259 en © 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. application/pdf University of Pretoria |
| spellingShingle | UCTD Coffee roasting Model predictive control Process optimisation Hybrid modelling Machine learning Sustainable development goals (SDGs) Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Empirical modelling in non-linear predictive control : a coffee roaster application |
| title | Empirical modelling in non-linear predictive control : a coffee roaster application |
| title_full | Empirical modelling in non-linear predictive control : a coffee roaster application |
| title_fullStr | Empirical modelling in non-linear predictive control : a coffee roaster application |
| title_full_unstemmed | Empirical modelling in non-linear predictive control : a coffee roaster application |
| title_short | Empirical modelling in non-linear predictive control : a coffee roaster application |
| title_sort | empirical modelling in non linear predictive control a coffee roaster application |
| topic | UCTD Coffee roasting Model predictive control Process optimisation Hybrid modelling Machine learning Sustainable development goals (SDGs) Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production |
| url | http://hdl.handle.net/2263/94529 |