Dynamic Optimisation of Beer Fermentation under Parametric Uncertainty

Fermentation is one of the most important stages in the entire brewing process. In fermentation, the sugars are converted by the brewing yeast into alcohol, carbon dioxide, and a variety of by-products which affect the flavour of the beer. Fermentation temperature profile plays an essential role in the progression of fermentation and heavily influences the flavour. In this paper, the fermentation temperature profile is optimised. As every process model contains experimentally determined parameters, uncertainty on these parameters is unavoidable. This paper presents approaches to consider the effect of uncertain parameters in optimisation. Three methods for uncertainty propagation (linearisation, sigma points, and polynomial chaos expansion) are used to determine the influence of parametric uncertainty on the process model. Using these methods, an optimisation formulation considering parametric uncertainty is presented. It is shown that for the non-linear beer fermentation model, the linearisation approach performed worst amongst the three methods, while second-order polynomial chaos worked the best. Using the techniques described below, a fermentation process can be optimised for ensuring high alcohol content or low fermentation time while ensuring the quality constraints. As we explicitly consider uncertainty in the process, the solution, even though conservative, will be more robust to parametric uncertainties in the model.

An Extremal Problem with Applications to Renewable Energy Production

Dynamic optimisation provides complex challenges for optimal solution, but greatly in- creases applicability when considering time dependent situations. In this work, a constrained dynamic optimisation problem is analysed and subsequently applied to the resolution of a real-world engineering problem concerning Solar Power Tower plants. We study the ex- istence of solutions and deduce an appropriate optimality characterisation in this applied framework. Two iterative algorithms are presented, convergence properties are discussed and a numerical illustration is given utilising realistic data. Finally, conclusions are drawn on the considered model and some ideas for future work are discussed.

The application of computational fluid dynamics to the modelling and design of high-speed boats

Computational fluid dynamics solvers were applied to the field of high-speed boat design. The lattice Boltzmann method was used to assess the water-phase of the flow around a number of high-speed hullform geometries, and was validated against empirical industry and literature data. A heave dynamics capability was developed to assess the heave equilibrium position of a high speed boat, showing close agreement with industry data. A mesh movement and evolutionary optimisation software was applied to the aero-dynamic optimisation of a high-speed catamaran using a Reynolds-averaged Navier-Stokes solver for modelling of the air phase of the flow.

Influence of corners and road conditions on cycling individual time trial performance and ‘optimal’ pacing strategy: A simulation study

A mathematical model of a bike-rider’s longitudinal and lateral dynamics was used to study the influence of road conditions (tyre-road friction coefficient) on cycling individual time trial (ITT) performance and pacing strategy. A dynamic optimisation approach was used on different simulated 40-km-ITT courses, where environmental variables (i.e. slope and wind), the presence of corners and the tyre-road friction coefficient were varied. The objective of the optimisation was the performance time. Maximal velocity was constrained by road geometry and the tyre-road friction coefficient. The maximal deliverable power output was constrained accordingly to the critical power model. The simulation results suggest that when technical sections constitute 25% of the entire course, road conditions can meaningfully affect the final performance time and peak power required, but not the pacing strategy. In fact, the time lost in slow technical sections cannot be regained during fast straight sections, even if technical sections are used to restore anaerobic energy stores. However, more experimental research is needed to test the applicability of these predictions.