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Full-Text Articles in Engineering
Carbonate Precipitation Behavior In Co2 Mea Electrolyzers With Cu Electrocatalyst, John C. Hendershot
Carbonate Precipitation Behavior In Co2 Mea Electrolyzers With Cu Electrocatalyst, John C. Hendershot
LSU Master's Theses
Annually, over 35 billion metric tons of carbon dioxide (CO2) are emitted into Earth’s atmosphere. The accumulated CO2 emissions in the atmosphere over the last century drive climate change. CO2 electroreduction (CO2ER) is an emerging technology that aims to recycle carbon emissions from the industrial sector by using CO2, H2O, and electricity as a feedstock to produce ethylene (C2H4) at current densities as high as 1 A cm-2 and Faradaic efficiencies (FE) of 60%. However, CO2 electrolyzers currently demonstrate poor durability at industrially relevant …
Improved Selectivity And Stability In Methane Dry Reforming By Atomic Layer Deposition Onto Ni-Ceo2-Zro2/Al2o3 Catalysts, Jonathan Lucas
Improved Selectivity And Stability In Methane Dry Reforming By Atomic Layer Deposition Onto Ni-Ceo2-Zro2/Al2o3 Catalysts, Jonathan Lucas
LSU Master's Theses
The use of Ni in the dry reforming of methane (DRM) has been widely studied as a replacement for noble metal catalysts (Pt, Pd, Rh) due to Ni’s low cost compared to noble metals, its abundance, and the fact that its DRM activity is near that of noble metal catalysts. However, Ni has been shown to deactivate quickly under DRM conditions. Rare earth oxides such as CeO2, or as CeO2-ZrO2 (CZO) are supports that improve both the activity and stability of Ni DRM systems due to their redox activity. However, this same activity is thought …
A Data-Driven Multivariate Process Monitoring Platform For Knowledge Discovery And Model Building In Industrial Applications, Estelle E. Seghers
A Data-Driven Multivariate Process Monitoring Platform For Knowledge Discovery And Model Building In Industrial Applications, Estelle E. Seghers
LSU Master's Theses
In industrial chemical manufacturing processes, the amount of raw data generated can add complexity in the analysis and understanding of the process dynamics. Being able to properly interpret this data can help improve plant operation, especially regarding safety and profitability. This research has culminated in FastMan-JMP, a platform proposed for monitoring of industrial processes and optimization of the offline data-driven model-building process as part of the process monitoring workflow. FastMan-JMP is a tool developed in Python to apply various data mining and machine learning techniques quickly and easily to better understand valuable patterns and hidden trends in process data. One …