Fermentation is an essential step in beer brewing: when yeast is added to hopped wort, sugars released from the grain during germination are fermented into ethanol and higher alcohols. To study, simulate and optimise the beer fermentation process, accurate models of the chemical system are required for dynamic simulation of key component concentrations. Since the entire beer production process is a highly complex series of chemical reactions with the presence of over 600 species, many of the specific interactions are not quantitatively understood, a comprehensive dynamic model is impractical.
Ampicillin is a broad spectrum antibiotic and World Health Organization Essential Medicine whose crystallization is an essential unit operation in its production. A published model for the solubility of ampicillin as a function of pH as well as growth and nucleation kinetics allows for dynamic simulation and optimization of its batch crystallization. While experimental approaches to investigating different dynamic pH profiles have been considered in the literature, dynamic mathematical optimization of pH modulations to meet specific production objectives for ampicillin batch crystallization has yet to be implemented; therein lies the novelty of this study. This work performs dynamic simulation and optimization of the batch crystallization of ampicillin to establish optimal pH trajectories for different production objectives. Simulation of already published batch seeded ampicillin crystallization experiments is performed prior to definition and solution of a dynamic optimization problem for maximization of mean crystal sizes and minimization of size distribution width. The effects of seed loading, time domain discretization and mean crystal size and size distribution width objective function weights are considered and discussed. Pareto fronts showing tradeoffs between different objectives and constraints are then investigated.
On the application of a nature-inspired stochastic evolutionary algorithm to constrained multiobjective beer fermentation optimisation
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AbstractFermentation is an essential step in beer brewing, often acting as the system bottleneck due to the time consuming nature of the process stage (duration >120hrs), where a trade-off exists between attainable ethanol concentration and required batch time. To explore this trade-off we employ a multi-objective Plant Propagation algorithm (the Strawberry algorithm), for identifying temperature manipulations for improved fermentation performance. The methodology employed successfully produces families of favourable temperature profiles which exist along the Pareto front. A subset of these output profiles can simultaneously reduce batch time and increase product ethanol concentration while satisfying constraints on by-products produced in the fermenters, representing significant improvements in comparison with current industrial practice. A potential batch time reduction of over 12 hours has been highlighted, coupled with a moderate improvement in ethanol content.
Beer fermentation efficiency improvements have the strongest potential to boost profitability, as its long batch time renders this particular unit operation the throughput bottleneck of this complex, multistage biochemical process which mankind has employed for several millennia. Accurate fermentation models are critical for reliable dynamic simulation and process optimization: empirical trial-and-error approaches are not viable, and incrementally altering proven recipes implies prohibitively expensive campaigns, in terms of equipment use, off-spec production and personnel time for sampling and analysis. This paper considers parameter estimation for a published beer fermentation model, demonstrating that estimating the complete unknown parameter set is an ill-posed problem, which can lead to inconsistent solutions. Systematic sensitivity analysis is pursued, elucidating the relative significance of parametric discrepancy on the validity of key species concentration trajectories. Parameters have been identified and ranked by decreasing importance, and a high-fidelity estimation is performed for a published dataset.
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