Advanced polymer membrane development in pervaporation dehydration and lateral flow diagnostics

The work in this thesis concerns the advanced development of polymeric membranes of two types; pervaporation and lateral-flow. The former produced from a solution casting method and the latter from a phase separation. All membranes were produced from casting lacquers. Early research centred on the development of viable membranes. This led to a supported polymer blend pervaporation membrane. Selective layer: plasticized 4:1 mass ratio sodium-alginate: poly(vinyl-alcohol) polymer blend. Using this membrane, pervaporation separation of ethanol/water mixtures was carefully monitored as a function of film thickness and time. Contrary to literature expectations, these films showed increased selectivity and decreased flux as film thickness was reduced. It is argued that morphology and structure of the polymer blend changes with thickness and that these changes define membrane efficiency. Mixed matrix membrane development was done using spherical, discreet, size-monodisperse mesoporous silica particles of 1.8 - 2μm diameter, with pore diameters of ~1.8 nm were incorporated into a poly(vinyl alcohol) [PVA] matrix. Inclusion of silica benefitted pervaporation performance for the dehydration of ethanol, improving flux and selectivity throughout in all but the highest silica content samples. Early lateral-flow membrane research produced a membrane from a basic lacquer composition required for phase inversion; polymer, solvent and non-solvent. Results showed that bringing lacquers to cloud point benefits both the pore structure and skin layers of the membranes. Advancement of this work showed that incorporation of ethanol as a mesosolvent into the lacquer effectively enhances membrane pore structure resulting in an improvement in lateral flow rates of the final membranes. This project details the formation mechanics of pervaporation and lateral-flow membranes and how these can be controlled. The principle methods of control can be applied to the formation of any other flat sheet polymer membranes, opening many avenues of future membrane research and industrial application.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.