Doctoral Degrees (Chemical Engineering)

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Browsing Doctoral Degrees (Chemical Engineering) by browse.metadata.advisor "Auret, Lidia"

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    Improving energy and economic performances of a typical sugarcane factory through energy indicator development, set-point optimization, and optimal sensor placement
    (Stellenbosch : Stellenbosch University, 2021-03) Mkwananzi, Thobeka; Gorgens, Johann F.; Auret, Lidia; Louw, Tobias M.; Mandegari, Mohsen A.; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: The volatile sugar markets and the recent recognition of bagasse as a key feedstock to produce biofuels and bioproducts have prompted a desire in the sugarcane industry to correct energy inefficiencies thereby allowing for additional revenue from increased surplus bagasse availability. However, the desire for improved energy efficiency is often beset by the lack of adequate measurements, imprecise measurements, budget constraints, and random variations in external process disturbances and market prices. In this regard, this study seeks to evaluate optimal control solutions that can be used to enhance the plant-wide monitoring and control of existing process operations in a typical sugarcane mill that processes 250 tonnes of sugarcane per hour. Objective 1 sought to identify the controlled variables (CVs) whose steady-state set-point deviations are associated with excess energy demands through energy indicator definition, sensitivity, and statistical analysis. An established sugarcane mill model was used to simulate the steady-state deviations of the CVs and to quantify their effect on energy usage based on defined energy indicators. Objective 2 entailed the use of Monte Carlo analysis to investigate the effect of process disturbances and market price variations on the steady-state factory control and net- revenue. Six disturbances were considered for simulation using the sugarcane mill model while the net revenue was defined in terms of raw materials cost and product revenue. From the observed steady-state deviations, set-point optimizing control (objective 3) was investigated for use in maximizing the net revenue by finding the optimal set-points for the CVs when disturbances and market prices vary. Fourteen CVs identified from objective 1 to have a large influence on energy consumption were used for set-point optimization. From objective 1, massecuite recycling was identified to result in excess energy demands and with set-point optimization, recycling was reduced by 23%. Surplus bagasse was increased by 8.5% with an acceptable 0.43% reduction in sugar yield and a 2.4% increase in net revenue. Nine CVs were identified to have optimal steady-state set-points that are insensitive to disturbance variations, thus allowing for simplified implementation of set-point optimization by keeping these CVs at constant set-points while re-optimizing for the remaining 5 CVs. The availability of precise measurements is crucial for effective automated control. Hence, the self-optimizing control concept was used to find an optimal linear combination of 41 CVs and their optimal sensor placement for use as constant CVs while eliminating the need for frequent online re-optimization when disturbances occur (objective 4). Optimality is defined as maximizing the net revenue by minimizing the total cost of purchasing the measuring instruments and the average revenue loss due to implementing the constant set-point policy rather than continuous real-time optimization. The cost of purchasing the sensor is normalized based on its expected lifespan. The attained optimal sensor placement has an average revenue loss of US$61.93/hr while the base case sensor placement loss is US$157.72/hr. The reduction in average revenue loss is attributed to 19 CVs for which the optimal sensor placement allocated more precise sensors compared to the base case sensor placement. The cost of purchasing the more precise sensors for these 19 CVs is US$2.73/hr. Overall, this study was able to successfully formulate strategies for enhanced process monitoring and control in sugarcane mills while contributing to the available literature.
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    Improving the performance of causality analysis techniques for automated fault diagnosis in mineral processing plants
    (Stellenbosch : Stellenbosch University, 2019-04) Lindner, Brian Siegfried; Auret, Lidia; Bauer, Margret; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: Modern mineral processing companies are driven towards improving productivity by leveraging existing processes optimally. This can be achieved by improving diagnosis of faults that degrade process performance to provide insightful and actionable information to process engineers. In mineral processing plants, units and variables are connected to each other through material ow, energy ow, and information ow. Faults propagate through a process along these interconnections, and can be traced back along their propagation paths to their root causes. Techniques have been developed for extracting these causal connections from historical process data. These techniques have proven successful for fault diagnosis in chemical processes. However, they have not been widely accepted by industry due to lack of automation of the techniques, complicated implementation, and complicated interpretation. This dissertation investigated the limitations of the causality analysis procedures currently available to process engineers as fault diagnosis tools and developed improvements on them. Improvements were developed and tested using a combination of simulated case studies and real world case studies of operational faults occurring in a mineral processing plant. Objective I: was to investigate the factors that a ect performance of causality analysis techniques. The use of transfer entropy for fault diagnosis in a minerals processing concentrator plant was demonstrated. The desired performance criteria of causality analysis techniques were then de ned in terms of: general applicability; automatability; interpretability; accuracy; precision; and computational complexity. The impact of process conditions on the performance of Granger causality and transfer entropy were then investigated. An analysis of variance (ANOVA) was performed to investigate the impact of process dynamics, fault dynamics, and the parameters on the accuracy of transfer entropy. Objective II: was to design a systematic work ow for application of causality analysis for fault diagnosis. The ANOVA was used to develop a novel relationship between the optimal transfer entropy parameters and the process and fault dynamics. This relationship was then placed within a systematic work ow developed for the application of transfer entropy for oscillation diagnosis, addressing the need for clear procedures and guidelines for data selection and parameter selection. The work ow was applied to an oscillation diagnosis case study from a minerals concentrator plant, and shown to provide a systematic approach to accurately determining the fault propagation path. Objective III: was to design a tool to aid the decision of which causality analysis method to select. A comparative analysis of Granger causality and transfer entropy for fault diagnosis based on the performance criteria de ned was performed. The comparison showed that transfer entropy was more precise, generalisable, and visually interpretable. Granger causality was more automatable, less computationally expensive, and easier to interpret. Guidelines were developed from these comparisons to aid users in deciding when to use Granger causality or transfer entropy Objective IV: was to present tools for interpretation of causal maps for root cause analysis. Methods for construction of causal maps from the results of the causality analysis calculation were presented, and methods for interpretation of causal maps. The usefulness of these techniques for diagnosis of real world case studies was demonstrated.
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    Monitoring, modelling and simulation of spiral concentrators
    (Stellenbosch : Stellenbosch University, 2018-12) Nienaber, Ernst Carel; Auret, Lidia; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: Spiral concentrators are robust gravity separation devices often compactly implemented in industry with large amounts of spirals per plant – organized in banks. Current automated monitoring strategies at spiral concentrator plants involve quantifying overall feed and product stream states. However, spiral unit monitoring is performed by manual operator inspection and control is mainly achieved by operators manually changing splitter settings of spirals across a plant. In large spiral plants, containing thousands of individual spiral concentrators, changing splitters can become tedious or is sometimes neglected. Automated monitoring and control of spirals can aid spiral plant operators in achieving optimal spiral plant performance. Computer vision orientated mineral interface detection have been proposed, in past studies, as a method to monitor spiral concentrators. This is due to the formation of different mineral bands within spiral troughs during heavy mineral separation. Particles differentiate based on density and size differences usually creating three, visually discernible, mineral bands (flowing down the spiral trough). These streams are known as the concentrate, middling and tailings streams. The concentrate band is often visually darker than the streams containing gangue and the mineral interfaces can serve as a useful cue for setting splitters. However, interface tracking on industrial slurries have not yet been demonstrated and due to the large number of spirals within spiral plants it is necessary to determine what sparse sensor implementation will look like (this is due to the lack of appropriate sensor placement algorithms for metallurgical plants). This text follows a framework that spans from sensor development to sensor implementation strategy within spiral concentration plants – exploring possible stumbling blocks along the way. A spiral interface sensor is proposed, as a spiral monitoring tool, and demonstrated with experimental work during which spiral modelling was also performed. Two image processing algorithms, CVI (edge detection based) and CVII (logistic regression based), were prepared to detect spiral interfaces. Experimental modelling of a Multotec SC21 spiral concentrator was performed by formulating and comparing response surface methodology (RSM) with a proposed extended Holland-Batt model. Two sensor placement strategies, SPI (state estimation based) and SPII (metallurgical performance based), were prepared to help determine important monitoring positions based on steady state spiral plant simulations. Optimal monitoring locations minimize sensor network financial cost while maximizing some proxy for monitoring benefit. Spiral concentrator and spiral plant modelling (including optimal sensor placement) is based on the case study of the Glencore Rowland spiral plant which treats slurry containing UG2 ores to upgrade chromite content. Algorithm CVII proved to be the superior interface detection approach and can identify chromite concentrate interfaces in slurry representative of industrial conditions. Spiral splitter control should be further investigated; however, spiral unit monitoring will still provide operators with useful information on process changes (should control be infeasible or unprofitable). RSM models were more precise than the extended Holland-Batt model; however, the latter showed superior extrapolation and plant simulation ability (emphasizing the need that modelling should be done with plant simulation in mind). SPI and SPII were used to rank different sensor configurations. Optimal sensor configurations determined by SPI were ultimately controlled by sensor financial cost. SPII is accepted as a superior sensor placement algorithm since sensor cost and metallurgical performance benefit were weighted in a way similar to a return on investment problem (suggesting a new perspective for this inherent multi-objective problem).