Advanced control with semi-empirical and data based modelling for falling film evaporators

[摘要] ENGLISH ABSTRACT: This work focussed on a local multiple chamber falling film evaporator (FFE). The FFE is currentlyunder operator control and experiencing large amounts of lost production time due to excessivefouling. 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 therecommended 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 couldoutperform the operators by keeping both control variables, WP and TE1, at desired set points whilealso increasing throughput and maintaining product quality.Access to the local plant was not possible and as such available process data were cleaned and usedto 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 TE1trends, with an average TE1 correlation to historical data of 0.36, compared to 0.59 and 0.74 for thetransfer function and semi-empirical-models respectively. Product dry mass correlations were similarbetween 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 WPcorrelation, it was offset by the TE1 prediction advantage. Therefore, the semi-empirical model wasselected for controller development and comparisons. The success of the semi-empirical model wasin accordance with previous research [1] [2] [3], yet other studies have concluded that ARXmodelling was more suited to FFE modelling [4].Three controllers were developed, namely: a proportional and integral (PI) controller as base case, alinear quadratic regulator (LQR) as an optimal state space alternative and finally, to make full use ofprocess knowledge, a predictive fuzzy logic controller (PFC). The PI controller was able to offer zerooffset set point tracking, but could not adequately reject a feed dry mass (WF) disturbance (asproposed and reported by Winchester [5]). The LQR was combined with a Kalman estimator andused pre-delay states. In order to offer increased disturbance rejection, the feedback gains of thedisturbance states were tuned individually. The altered LQR and PFC solutions proved to adequatelyreject all modelled disturbances and outperform a cascade controller designed by Bakker [6]. Themaximum deviation in WP was a fractional increase of 0.007 for LQR and 0.005 for FPC, compared to0.012 for PI and 0.0075 for the cascade controller [6] (WF disturbance fractional increase of 0.01). Allthe designed controllers managed to reduce the standard deviation of operator controlled WP andTE1 by at least 700% and 450%, respectively. The same level of reduction was seen for maximumcontrol variable deviations (370%), the integral of the absolute error (300%) and the mean squarederror (900%). All these performance metrics point to the controllers performing better than theoperator based control.In order to prevent manipulated variable saturation and optimise the feed flow rate (F1), a fuzzy feedoptimiser (FFO) was developed. The FFO focussed on maximising the available evaporative capacityof the FFE by optimising the motive steam pressure (PS), which supplied heat to the effects. By usingthe FFO for each controller the average feed flow rate was increased by 4.8% (±500kg/h) comparedto the operator control. In addition to flow rate gain, the controllers kept TE1 below 70°C and WP onspecification. As such, the overall product quality also increased as well as decreasing the down timedue to less fouling.

[发布机构] Stellenbosch University