Analysis of energy management in GMIT

Energy management is the process of monitoring, controlling and conserving energy in a building or organisation. The main reasons for this are for cost purposes and benefit to the environment. Through various techniques and solutions for lighting, heating, office equipment, the building fabric etc along with a change in people’s attitudes there can be a substantial saving in the amount spent on energy. A good example o f energy waste in GMIT is the lighting situation in the library. All the lights are switched on all day on even in places where that is adequate daylighting, which is a big waste o f energy. Also the lights for book shelves are left on. Surely all these books won’t be searched for all at the one time. It would make much more sense to have local switches that the users can control when they are searching for a particular book. Heating controls for the older parts o f the college are badly needed. A room like 834 needs a TRV to prevent it from overheating as temperatures often reach the high twenties due to the heat from the radiators, computers, solar gains and heat from users o f the room. Also in the old part o f the college it is missing vital insulation, along with not being air tight due to the era when it was built. Pumped bonded bead insulation and sealant around services and gaps can greatly improve the thermal performance o f the building and help achieve a higher BER cert. GMIT should also look at the possibility o f installing a CHP plant to meet the base heating loads. It would meet the requirement o f running 4500 hours a year and would receive some financial support from the Accelerated Capital Allowance. I f people’s attitudes are changed through energy awareness campaigns and a few changes made for more energy efficient equipment, substantial savings can be made in the energy expenditure.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.