A vast number of contemporary production processes rely on living cells or
biomolecules preforming chemical transformations as a vital step in the manufacture
of valuable products. Providing sustainable supplies of food and drink
relies on such biochemical processes which are essentially unchanged for millennia,
and will remain quintessential for future communities and societies. Likewise
many pharmaceutical products and produced from similar systems, and have
formed an essential component of modern civilisation. To ensure high productivity
without wasting resources (agricultural feedstock, equipment, time), it is
critical to determine optimal dynamic operating profiles toward prescribing implementable
control methodologies. Mathematical models have been developed
for many important food and drink manufacturing processes operated using suboptimal
recipes: these journal publications are quite rigorous and extensive (often
describing not only composition, but also how flavour can be tuned as desired),
but they frequently require consistent kinetic parameter estimation on the basis of
industrial data, which is itself a dynamic optimisation problem (multi-parametric
error minimisation). Dynamic optimisation of biochemical processes is of extreme
technoeconomic interest and importance in industrial control practice, particularly
for biochemical process systems which display steady-state and/or operating
regime multiplicity, and require sizeable vectors of time-dependent concentrations
and temperature-dependent kinetic parameters. Alcohol fermentation is undergoing
continuous development for several millennia via concurrent advances in
chemistry and chemical engineering (which greatly affected the art of bringing
yeast, barley and hops together); at the same time, the biological evolution of
yeast strands by natural selection as well as empirical recipes and procedures
have impacted brewing even more. Ensuring high product quality is not a trivial
task, particularly when strong market demand dictates process intensification.
Producing food and drink in shorter times (more efficiently) with optimised processes
(more cost-effectively) requires in-depth knowledge of reactive systems.
The problems of consistent kinetic parameter estimation and systematic determination
of optimal operating profiles to improve industrial practice are explored in
this thesis for several different biochemical systems. For the first time attainable
performance in beer fermentation has been exhaustively mapped under a comprehensive
family of realistic time dependent temperature manipulations, providing
invaluable insight to industrial brewing collaborators. This is expanded upon
with the computation of optimal dynamic fermentor temperature profiles subject
to a range of realistic threshold constraints on flavour degrading compounds
in the product. Herein the influence of each individual by-product level on the
achievable process performance can be explicitly quantified and visualised. Furthermore,
the inherent trade-off in brewing process targets (batch time vs product
quality) has been explored for the first time in this work, mapping the Pareto front
via multi-objective dynamic optimisation. These results can be used by decision
makers to better inform process decisions with significant economic implications.
Following an extensive experimental campaign the first lumped parameter model
and associated parameter values for the enzymatic hydrolysis of keratin waste is
also proposed in this work. The model is used to formulate a dynamic optimisation
problem, demonstrating that treatment of this waste can be accelerated
with novel feed strategies. This work highlights the immense value in systematic
and rigorous model based simulation and optimisation campaigns for biochemical
process systems
