Multivariate batch to batch optimisation of fermentation processes to improve productivity

Increasing the productivity of batch processes presents a complex challenge, with difficulties resulting from, amongst other things, the time-varying and non-linear characteristics that such processes exhibit. This paper presents an innovative optimisation technique which utilises a data-driven Gaussian process regression model, built on time-varying and non-linear historical process data, to iteratively increase batch productivity from one cycle to the next. Specifically, productivity is increased by making appropriate adjustments to batch cycle time and the trajectory of a manipulated variable. The capabilities of the proposed method are demonstrated using two benchmark fermentation simulations, Penicillin production (Pensim) and Saccharomyces Cerevisiae, where it is shown to achieve increases in productivity of between 60% and 97% compared with what was achieved using ‘golden batch’ conditions.