Dynamic Optimisation of Beer Fermentation under Parametric Uncertainty

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