Industrial applications of hybrid modelling techniques

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

Polymorph identification for flexible molecules : linear regression analysis of experimental and calculated solution- and solid-state NMR data

The Δδ regression approach of Blade et al. [ J. Phys. Chem. A 2020, 124(43), 8959–8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured 1H and 13C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδexperimental) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδcalculated) by regression analysis and a t-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule’s charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδexperimental and Δδcalculated are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures

Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data

The Δδ regression approach of Blade et al. [J. Phys. Chem. A 2020, 124(43), 8959–8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured 1H and 13C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδexperimental) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδcalculated) by regression analysis and a t-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule’s charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδexperimental and Δδcalculated are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures