Eng. D ThesisIn the present study, the application of hybrid modelling techniques is applied to industrial
applications. Many of the studies currently known to the literature for the fields under
examination are either purely model-based, theory-based or lab/pilot scale empirical
studies. In this work, we present a hybrid approach whereby empirical data is used to
form statistical models for relationships where no clear fundamental relationship can be
described mathematically. Equally, first-principles models are employed where no suitable
data can be gathered empirically. Finally, the process understanding, heuristics and
recollections of plant operators, engineers and maintenance personnel can be integrated
formally into the decision-making process of process design/optimisation.
The first half of this work is concerned with process development of a proprietary modular Gas-to-Liquids process, briefly comprised of a packed bed plate-fin ’mini-channel’
Fischer-Tropsch reactor. Currently, little can be predicted about the flow or temperature
performance of a complex reactor geometry in the design phase. Data-driven models
provide a simplistic approximation with no added understanding. At commercially relevant
scales, the parameters of interest are both costly and hazardous to iterate through empirical
trial and improvement. By integrating offline analysis, online data and a novel temperature
sensing scheme, we increase the spaciotemporal resolution of data while adding process
understanding.
The second theme of this work is related to flue gas filtration in large-scale Biomass
and Energy-from-Waste Power Generation plants. Flue gas filtration is overlooked as an
opportunity for process improvement. We argue that a filtration system designed on the
basis of lowest CAPEX, and operated at the lowest maintenance cost will not provide the
lowest total cost of ownership. By integrating industrial historic data, maintenance records,
commercial data and multivariate modelling methods, we produce a set of recommendations
for improved operation. Commercially available solutions are benchmarked in predictive
hybrid models on a ROI basisEngineering and Physical Sciences Research Council for part funding this work. Innovate UK for part funding this wor