A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Robust face recognition using posterior union model based neural networks

Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.

A new model based approach for the prediction and optimisation of thermal homogeneity in single screw extrusion

In polymer extrusion, delivery of a melt which is homogenous in composition and temperature is important for good product quality. However, the process is inherently prone to temperature fluctuations which are difficult to monitor and control via single point based conventional thermo- couples. In this work, the die melt temperature profile was monitored by a thermocouple mesh and the data obtained was used to generate a model to predict the die melt temperature profile. A novel nonlinear model was then proposed which was demonstrated to be in good agreement with training and unseen data. Furthermore, the proposed model was used to select optimum process settings to achieve the desired average melt temperature across the die while improving the temperature homogeneity. The simulation results indicate a reduction in melt temperature variations of up to 60%.

The analysis of large-scale turbulence characteristics in the Indonesian seas derived from a regional model based on the Princeton Ocean Model

Turbulence characteristics in the Indonesian seas on the horizontal scale of order of 100 km were calculated with a regional model of the Indonesian seas circulation in the area based on the Princeton Ocean Model (POM). As is well known, the POM incorporates the Mellor–Yamada turbulence closure scheme. The calculated characteristics are: twice the turbulence kinetic energy per unit mass, <i>q</i>²; the turbulence master scale, &ell;; mixing coefficients of momentum, <i>K</i>_M; and temperature and salinity, <i>K</i>_H; etc. The analyzed turbulence has been generated essentially by the shear of large-scale ocean currents and by the large-scale wind turbulence. We focused on the analysis of turbulence around important topographic features, such as the Lifamatola Sill, the North Sangihe Ridge, the Dewakang Sill, and the North and South Halmahera Sea Sills. In general, the structure of turbulence characteristics in these regions turned out to be similar. For this reason, we have carried out a detailed analysis of the Lifamatola Sill region because dynamically this region is very important and some estimates of mixing coefficients in this area are available. <br><br> Briefly, the main results are as follows. The distribution of <i>q</i>² is quite adequately reproduced by the model. To the north of the Lifamatola Sill (in the Maluku Sea) and to the south of the Sill (in the Seram Sea), large values of <i>q</i>² occur in the deep layer extending several hundred meters above the bottom. The observed increase of <i>q</i>² near the very bottom is probably due to the increase of velocity shear and the corresponding shear production of <i>q</i>² very close to the bottom. The turbulence master scale, &ell;, was found to be constant in the main depth of the ocean, while &ell; rapidly decreases close to the bottom, as one would expect. However, in deep profiles away from the sill, the effect of topography results in the &ell; structure being unreasonably complicated as one moves towards the bottom. Values of 15 to 20 × 10^&minus;4 m² s^-1 were obtained for <i>K</i>_M and <i>K</i>_H in deep water in the vicinity of the Lifamatola Sill. These estimates agree well with basin-scale averaged values of 13.3 × 10^&minus;4 m² s^-1 found diagnostically for <i>K</i>_H in the deep Banda and Seram Seas (Gordon et al., 2003) and a value of 9.0 × 10^&minus;4 m² s^-1 found diagnostically for <i>K</i>_H for the deep Banda Sea system (van Aken et al., 1988). The somewhat higher simulated values can be explained by the presence of steep topography around the sill.