Optimal control and operation of drum granulation processes

Process optimisation and optimal control of batch and continuous drum granulation processes are studied in this paper. The main focus of the current research has been: (i) construction of optimisation and control relevant, population balance models through the incorporation of moisture content, drum rotation rate and bed depth into the coalescence kernels; (ii) investigation of optimal operational conditions using constrained optimisation techniques; (iii) development of optimal control algorithms based on discretized population balance equations; and (iv) comprehensive simulation studies on optimal control of both batch and continuous granulation processes. The objective of steady state optimisation is to minimise the recycle rate with minimum cost for continuous processes. It has been identified that the drum rotation-rate, bed depth (material charge), and moisture content of solids are practical decision (design) parameters for system optimisation. The objective for the optimal control of batch granulation processes is to maximize the mass of product-sized particles with minimum time and binder consumption. The objective for the optimal control of the continuous process is to drive the process from one steady state to another in a minimum time with minimum binder consumption, which is also known as the state-driving problem. It has been known for some time that the binder spray-rate is the most effective control (manipulative) variable. Although other possible manipulative variables, such as feed flow-rate and additional powder flow-rate have been investigated in the complete research project, only the single input problem with the binder spray rate as the manipulative variable is addressed in the paper to demonstrate the methodology. It can be shown from simulation results that the proposed models are suitable for control and optimisation studies, and the optimisation algorithms connected with either steady state or dynamic models are successful for the determination of optimal operational conditions and dynamic trajectories with good convergence properties. (c) 2005 Elsevier Ltd. All rights reserved.

Optimisation of strain rate imaging for application to stress echocardiography

We sought to improve the feasibility of strain rate imaging (SRI) during dobutamine stress echocardiography (DSE) in 56 subjects at low risk of coronary disease. The impact of several SRI changes during acquisition were studied, including: (1) changing from fundamental to harmonic imaging; (2) parallel beam-forming; (3) alteration of spatial resolution and (4) narrow sector acquisition. We assessed SR signal quality, a quantitative measure of signal noise and measurements of SRI. Of 1462 segments evaluated, 6% were uninterpretable at rest and 8% at peak stress. Signal quality was optimised by increasing temporal (p = 0.01) and spatial resolution (p<0.0001 vs. baseline imaging) at rest and peak. Increasing spatial resolution also minimised signal noise (p<0.0001). Inter-observer variability of time to peak SR and peak SR were less with high temporal and spatial resolution. SRI quality can be improved with harmonic imaging and higher temporal resolution but optimisation of spatial resolution is critical. (C) 2004 World Federation for Ultrasound in Medicine Biology.

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.