Department of Electrical and Electronic Engineering

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https://scholar.sun.ac.za/handle/10019.1/97
Electrical and Electronic Engineering is an exciting and dynamic field. Electrical engineers are responsible for the generation, transfer and conversion of electrical power, while electronic engineers are concerned with the transfer of information using radio waves, the design of electronic circuits, the design of computer systems and the development of control systems such as aircraft autopilots. These sought-after engineers can look forward to a rewarding and respected career.

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Browsing Department of Electrical and Electronic Engineering by browse.metadata.advisor "Auret, Lidia"

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    Advanced control with semi-empirical and data based modelling for falling film evaporators
    (Stellenbosch : Stellenbosch University, 2013-03) Haasbroek, Adriaan Lodewicus; Steyn, W. H.; Auret, Lidia; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: This work focussed on a local multiple chamber falling film evaporator (FFE). The FFE is currently under operator control and experiencing large amounts of lost production time due to excessive fouling. Furthermore, the product milk dry mass fraction (WP) is constantly off specification, negatively influencing product quality, while the first effect temperature (TE1) runs higher than the recommended 70°C (this is a main cause of fouling). A two month period of historical data were received with the aim to develop a controller that could outperform the operators by keeping both control variables, WP and TE1, at desired set points while also increasing throughput and maintaining product quality. Access to the local plant was not possible and as such available process data were cleaned and used to identify two data based models, transfer function and autoregressive with exogenous inputs (ARX) models, as well as a semi-empirical-model. The ARX model proved inadequate to predict TE1 trends, with an average TE1 correlation to historical data of 0.36, compared to 0.59 and 0.74 for the transfer function and semi-empirical-models respectively. Product dry mass correlations were similar between the models with the average correlations of 0.47, 0.53 and 0.51 for the semi-empirical, transfer function and ARX models respectively. Although the semi-empirical showed the lowest WP correlation, it was offset by the TE1 prediction advantage. Therefore, the semi-empirical model was selected for controller development and comparisons. The success of the semi-empirical model was in accordance with previous research [1] [2] [3], yet other studies have concluded that ARX modelling was more suited to FFE modelling [4]. Three controllers were developed, namely: a proportional and integral (PI) controller as base case, a linear quadratic regulator (LQR) as an optimal state space alternative and finally, to make full use of process knowledge, a predictive fuzzy logic controller (PFC). The PI controller was able to offer zero offset set point tracking, but could not adequately reject a feed dry mass (WF) disturbance (as proposed and reported by Winchester [5]). The LQR was combined with a Kalman estimator and used pre-delay states. In order to offer increased disturbance rejection, the feedback gains of the disturbance states were tuned individually. The altered LQR and PFC solutions proved to adequately reject all modelled disturbances and outperform a cascade controller designed by Bakker [6]. The maximum deviation in WP was a fractional increase of 0.007 for LQR and 0.005 for FPC, compared to 0.012 for PI and 0.0075 for the cascade controller [6] (WF disturbance fractional increase of 0.01). All the designed controllers managed to reduce the standard deviation of operator controlled WP and TE1 by at least 700% and 450%, respectively. The same level of reduction was seen for maximum control variable deviations (370%), the integral of the absolute error (300%) and the mean squared error (900%). All these performance metrics point to the controllers performing better than the operator based control. In order to prevent manipulated variable saturation and optimise the feed flow rate (F1), a fuzzy feed optimiser (FFO) was developed. The FFO focussed on maximising the available evaporative capacity of the FFE by optimising the motive steam pressure (PS), which supplied heat to the effects. By using the FFO for each controller the average feed flow rate was increased by 4.8% (±500kg/h) compared to the operator control. In addition to flow rate gain, the controllers kept TE1 below 70°C and WP on specification. As such, the overall product quality also increased as well as decreasing the down time due to less fouling.