Fingerprint of a submerged-arc furnace: Optimising energy consumption through data mining, dynamic modelling and computational fluid dynamics

This study imparts a scientific perception of a phosphorous-producing submerged arc furnace never seen before; a proverbial fingerprint that can improve problem identification, disturbance diagnostics, process prediction, dynamic modelling and model predictive control of this type of furnace. It successfully incorporates accurate, multi-field thermodynamic-, kinetic- and industrial data with computational flow dynamic calculations; thus further unifying the sciences of kinetics and equilibrium thermodynamics. The true power of this study is the extensive and methodical validation that ensures industrially endorsed results. To facilitate all this the author spent six uninterrupted months at an industrial plant (Thermphos International), twice walked inside a cold submerged-arc furnace, gathered and analysed more than thirty-four mineralogical samples, managed an extensive and insightful sampling campaign on the slag streams, performed feed material porosity tests and had thirteen additional temperature probes installed inside the furnace lining. The author also scrutinised over years of industrial data, inspected many industrial drawing and partook in countless valuable conversations with industrial and technical experts to guarantee, not only a valuable scientific contribution, but one that is deep-rooted in authentic engineering principles