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.

The concept of risk management in the renewable sector.

This study explores the perception of risk and the level of risk management implementation in the renewable sector. Risk management is emerging as a key issue due to the loss of confidence amongst banks, causing the attainment of financing to be difficult over the next few years. To attract financing, there is a fundamental requirement to manage risk in a way that minimizes the probability of a negative financial impact on the project. Miller and Lessard (2001) argue that successful projects are not selected but shaped with risk resolution in mind. Rather than evaluating projects at the outset based on projections of the full set of benefits, costs and risks over their lifetime, successful developers start with project ideas that have the potential of becoming viable. Therefore, this study bridges the gap that exists within the renewable sector in relation to risk management literature. This study succeeds through a detailed comparative case study analysis where two developers and two financiers were questioned through qualitative semi-structured interviews on the concept of risk management and its level implementation within the industry. It is believed that the growth in financed renewable energy projects depends on the adequate design and implementation of risk management to mitigate inherent project risks. However, this study revealed that are certain types of developers in existence within the renewable sector, which underestimate the magnitude of risk and view the development of projects as a ‘money racket’. Therefore, it can be concluded that perception of risk will also differ, causing risk and uncertainty to vary from project to project, resulting in investment reluctance to be associated with certain projects. The study originality lies in how it demonstrates to developers the concept of risk management, outlining the simplicity and benefits of implementing it in project development. Finally, this study contributes to the knowledge by enhancing the awareness and understanding of the presence and nature of risk in a RE project environment.