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Daresbury, United Kingdom

Tahir F.,Perceptive Engineering Ltd. | Jaimoukha I.M.,Imperial College London
IEEE Transactions on Automatic Control | Year: 2015

We propose a novel algorithm to compute low-complexity polytopic robust control invariant (RCI) sets, along with the corresponding state-feedback gain, for linear discrete-time systems subject to norm-bounded uncertainty, additive disturbances and state/input constraints. Using a slack variable approach, we propose new results to transform the original nonlinear problem into a convex/LMI problem whilst introducing only minor conservatism in the formulation. Through numerical examples, we illustrate that the proposed algorithm can yield improved maximal/minimal volume RCI set approximations in comparison with the schemes given in the literature. © 1963-2012 IEEE. Source

O'Brien M.,Perceptive Engineering Ltd. | Mack J.,Perceptive Engineering Ltd. | Lennox B.,University of Manchester | Lovett D.,Perceptive Engineering Ltd. | Wall A.,United Utilities
Control Engineering Practice | Year: 2011

This paper details a case study application of model predictive control for a wastewater treatment process in Lancaster, North England. The control system was implemented in real time, together with a plant monitoring system for the purposes of process supervision. Following implementation, the model predictive control system provided significant benefits compared with the previously applied control system. These benefits included a reduction of over 25% in power usage and a similar increase in plant efficiency. The system therefore represents a useful tool in helping the water industry to reach its goal of significantly reducing its carbon footprint. © 2010 Elsevier Ltd. Source

Austin P.C.,Victoria University of Wellington | Austin P.C.,Perceptive Engineering Ltd. | Mack J.,Perceptive Engineering Ltd. | Mcewan M.,Perceptive Engineering Ltd. | Afshar P.,University of Manchester
Appita Annual Conference | Year: 2010

Over the last two or three years, the increasing costs of energy, greater awareness of environmental concerns and worsening market conditions have focussed even greater attention within paper mills than before, on considering ways to reduce the energy used in paper making. Arising from a multivariable understanding of paper machine operation, Advanced Process Control (APC) technology enables paper machine behaviour to be controlled in a more coherent way, using all the variables available for control. Furthermore, with the machine under better regulation and with more variables used in control, there is the opportunity to optimise machine operation, usually providing very striking multi-objective performance improvement benefits of a number of kinds. Traditional three tem control technology does not offer this capability. The paper presents results from several different APC paper machine projects we have undertaken around the world. These projects have been aimed at improving machine stability, optimising chemicals usage and reducing energy use. On a brown paperboard machine in Australia, specific steam usage has been reduced by greater than 10%, averaged across the grades; the controller has also provided a significant capacity to increase production. On a Canadian newsprint machine the APC system has reduced steam usage by 10%, and it provides better control of colour and much improved wet end stability. The paper also outlines early results from two other APC projects being undertaken in England, each aimed at examining a different approach to reducing the energy used in paper making. The first of these two projects is focussed on optimising sheet drainage, aiming to present the dryer with a sheet having higher solids content. The second project aims to reduce specific steam usage by maximising machine production. The paper concludes with an outline of a project just begun in which better understanding is being sought of the role of differential pressure and condensate recovery rate in determining dryer effectiveness and energy efficiency. First principles models of a suitable simplification of the dryer are being developed with the expectation that this approach will add understanding to the empirical linear models normally developed in designing an APC dryer control system. Source

Goldrick S.,Northumbria University | Lennox B.,University of Manchester | Lovett D.,Perceptive Engineering Ltd. | Smith K.,Perceptive Engineering Ltd. | Montague G.,Northumbria University
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2013

This paper presents a simulation of an industrial scale filamentous fermentation; the simulation focuses on modeling a 120,000 litre Penicillium chrysogenum batch process. The simulation attempts to address many of the challenges that that are faced by industrial scale filamentous fermentations; these include the control of dissolved oxygen concentration above its critical value and also controlling substrate feed to an optimum trajectory. Previous unstructured models, that didn't consider the changing morphology of Penicillin fermentations, failed to adequately model the historical Penicillin production batch data presented here. This simulation extends previous structured models by including extra process variables such as gas inlet pressure and viscosity, which are shown to have a significant effect on the control strategy of these large-scale fermentations. The accuracy of the model is verified by successfully predicting both the Penicillin and dissolved oxygen concentration using the input data from two industrial 120,000 litre Penicillium chrysogenum batch fermentations. The overall aim of the simulation is to provide an improved test bed for fed-batch Penicillin fermentations that can be used for process monitoring, control and optimization studies. © IFAC. Source

Goldrick S.,Northumbria University | Mercer E.,Perceptive Engineering Ltd. | Montague G.,Northumbria University | Lovett D.,Perceptive Engineering Ltd. | Lennox B.,University of Manchester
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2014

This work investigates the application of a "Process Analytical Technology" (PAT) analyser to control the substrate concentration over traditional sequential batch control for an industrial scale fed-batch penicillin fermentation. A simulation that utilises the historical data from four batches, where a sequential batch control strategy was implemented, was used as the benchmark reference for this comparison. The simulation accurately predicts the main outputs variables of biomass and penicillin, given the inputs from the historical data set. The simulation includes a PAT analyser, used to build a calibration model with the available off-line substrate concentration from one of the batches. The prediction from this calibration model was used as the controlled variable within a proportional integral (PI) controller to manipulate the substrate feed rate for the three remaining batches. Performance of each control strategy was analysed by comparing the final penicillin yield of each batch. An increase of 35, 20 and 9% was observed for the three batches controlled using the PI controller compared with the sequential batch control strategy. © IFAC. Source

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