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Advancing environmental, social, and governance outcomes through process optimisation and control
Thesis (PhD (Electronic Engineering))--University of Pretoria, 2024.
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University of Pretoria
2025
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| _version_ | 1867613518593785856 |
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| access_status_str | Open Access |
| author2 | Le Roux, Johan Derik |
| author_browse | Le Roux, Johan Derik |
| author_facet | Le Roux, Johan Derik |
| collection | Thesis |
| dc_rights_str_mv | © 2024 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 | Thesis (PhD (Electronic Engineering))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/104105 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:37:25.592Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| 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:2263/104105 Advancing environmental, social, and governance outcomes through process optimisation and control Le Roux, Johan Derik burchell.john@gmail.com Craig, Ian K. Burchell, John James UCTD Sustainable Development Goals (SDGs) Evolutionary algorithm Input blending Level averaging control Modeling Nonlinear model predictive control Optimisation Process control Refinery Simulation Tailings reprocessing Thesis (PhD (Electronic Engineering))--University of Pretoria, 2024. Organisations are compelled to integrate Environmental, Social, and Governance (ESG) considerations into their core strategy, with the tightening of regulatory requirements and the mounting pressure from stakeholders for sustainable practices driving a trend toward socially responsible investing. Advanced process optimisation and control provides innovative solutions to support ESG objectives. This thesis explores two case studies aimed at enhancing the consistency of material flow and composition into metallurgical operations to improve overall processing efficiency. The first case study introduces a (μ+λ)-Evolutionary Strategy (ES) to solve the input blending problem for a base metal refinery (BMR), where variability in the feed of contaminants to the operation impact negatively on plant throughput, product quality, and harmful emissions. The algorithm outperforms baseline blending strategies demonstrating a significant improvement in the blended consistency of contaminant feed. In the second case study, a nonlinear Model Predictive Controller (NMPC) is developed and implemented on a surge tank for level averaging control in an industrial tailings reprocessing circuit. A rigorous dynamic model is derived to describe the rate of change of both the volume and density in these surge tanks. By simulation with industrial data it is demonstrated that the significant input disturbances typical to tailings reprocessing circuits drive a gain inversion in the density model of the surge tank. This gain inversion and the multivariable objectives of both density and flow disturbance attenuation motivates for a NMPC solution. Results presented show significant improvements in both the water recovery and the stability of mass flow of tailings in the circuit. These advanced optimisation and control solutions support ESG objectives across multiple dimensions. Improved input stability with the (μ +λ)-ES enhances the efficiency of downstream processes where contaminants are extracted, resulting in lower emissions, especially when hazardous reagents are involved in the extraction process. By improving the efficiency of contaminant extraction the need for rework of product that fail to meet specifications is minimised, which leads to a reduction in waste generation, conservation of resources, and lower energy consumption. Improved water recovery with the NMPC lowers the overall environmental footprint of the tailings reprocessing circuit by reducing water consumption and energy usage, while stability improvements positively impact recoveries, thereby reducing waste and supporting responsible resource management. Electrical, Electronic and Computer Engineering PhD (Electronic Engineering) Unrestricted Faculty of Engineering, Built Environment and Information Technology SDG-09: Industry, innovation and infrastructure SDG-12: Responsible consumption and production 2025-09-01T12:44:18Z 2025-09-01T12:44:18Z 2024-04 2024-02 Thesis * A2024 http://hdl.handle.net/2263/104105 https://doi.org/10.25403/UPresearchdata.30023134 © 2024 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 Sustainable Development Goals (SDGs) Evolutionary algorithm Input blending Level averaging control Modeling Nonlinear model predictive control Optimisation Process control Refinery Simulation Tailings reprocessing Advancing environmental, social, and governance outcomes through process optimisation and control |
| title | Advancing environmental, social, and governance outcomes through process optimisation and control |
| title_full | Advancing environmental, social, and governance outcomes through process optimisation and control |
| title_fullStr | Advancing environmental, social, and governance outcomes through process optimisation and control |
| title_full_unstemmed | Advancing environmental, social, and governance outcomes through process optimisation and control |
| title_short | Advancing environmental, social, and governance outcomes through process optimisation and control |
| title_sort | advancing environmental social and governance outcomes through process optimisation and control |
| topic | UCTD Sustainable Development Goals (SDGs) Evolutionary algorithm Input blending Level averaging control Modeling Nonlinear model predictive control Optimisation Process control Refinery Simulation Tailings reprocessing |
| url | http://hdl.handle.net/2263/104105 https://doi.org/10.25403/UPresearchdata.30023134 |