Bibliographic Details
| Title: |
Optimising an underground mine cooling water operation |
| Authors: |
Jordan, Anastasiou |
| Contributors: |
Louw, Tobi, Shipman, William, Stellenbosch University. Faculty of Engineering. Dept. of Chemical Engineering. |
| Publisher Information: |
Stellenbosch University |
| Publication Year: |
2025 |
| Collection: |
Stellenbosch University: SUNScholar Research Repository |
| Subject Terms: |
Cooling systems, Mine ventilation, Refrigeration and refrigerating machinery, Engineering systems -- Computer simulation, UCTD |
| Description: |
Anastasiou, J. 2025. Optimising an underground mine cooling water operation. Unpublished masters thesis. Stellenbosch: Stellenbosch Univeristy [online]. Available: https://scholar.sun.ac.za/items/5ab1f703-165f-429d-a952-33d87a3c65ef ; Thesis (MEng)--Stellenbosch University, 2025. ; ENGLISH ABSTRACT: Deep-level mines such as the Mponeng gold mine in Carletonville require energy intensive cooling processes to keep the environment for mine workers and machinery workable at a somewhat 5 km underground, where temperatures reach up to 65 °C. This cooling circuit in particular consists of storage dams, cooling towers and five refrigeration plants which account for a large percentage of the plant’s energy usage. Mintek was contracted to provide control to the circuit, initially limited to only two valves, and due to the lack of accurate models and parameters describing the system, they could not implement fridge plant scheduling according to Eskom’s time-of-day tariffs. It is estimated that scheduling fridge plant operation based on the downstream underground needs as well as the price of electricity could save up to R16 million per year in energy costs. To realise the possible economic benefits, the circuit is first modelled from first principle mass and energy balances, and the model parameters estimated based on recorded plant data using regression in MATLAB. To validate that the model parameters provide the best possible predictive accuracy, an identifiability analysis is carried out, making use of the profile likelihood method. Parameters are assessed for structural and practical identifiability, indicating the quality of the data and the model. Disturbances to the process are modelled stochastically, with an auto-regressive function. To demonstrate the possible financial savings, a model predictive controller (MPC) and optimiser are implemented in simulation. This includes setting up individual MPCs on each of the units as a base layer of control, and then applying an optimiser as a top layer. The optimiser is ... |
| Document Type: |
thesis |
| File Description: |
127 pages : illustrations; application/pdf |
| Language: |
unknown |
| Relation: |
https://scholar.sun.ac.za/handle/10019.1/132044 |
| Availability: |
https://scholar.sun.ac.za/handle/10019.1/132044 |
| Rights: |
Stellenbosch University |
| Accession Number: |
edsbas.A5F08B3B |
| Database: |
BASE |