Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand

The last 30 years have seen Fuzzy Logic (FL) emerging as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are overlapping with differences in opinion as to when which method should be used. Lacking are practically relevant case studies comparing these two methods. This work compares stochastic and FL methods for the assessment of spare capacity on the example of pharmaceutical high purity water (HPW) utility systems. The goal of this study was to find the most appropriate method modelling uncertainty in industrial scale HPW systems. The results provide evidence which suggests that stochastic methods are superior to the methods of FL in simulating uncertainty in chemical plant utilities including HPW systems in typical cases whereby extreme events, for example peaks in demand, or day-to-day variation rather than average values are of interest. The average production output or other statistical measures may, for instance, be of interest in the assessment of workshops. Furthermore the results indicate that the stochastic model should be used only if found necessary by a deterministic simulation. Consequently, this thesis concludes that either deterministic or stochastic methods should be used to simulate uncertainty in chemical plant utility systems and by extension some process system because extreme events or the modelling of day-to-day variation are important in capacity extension projects. Other reasons supporting the suggestion that stochastic HPW models are preferred to FL HPW models include: 1. The computer code for stochastic models is typically less complex than a FL models, thus reducing code maintenance and validation issues. 2. In many respects FL models are similar to deterministic models. Thus the need for a FL model over a deterministic model is questionable in the case of industrial scale HPW systems as presented here (as well as other similar systems) since the latter requires simpler models. 3. A FL model may be difficult to "sell" to an end-user as its results represent "approximate reasoning" a definition of which is, however, lacking. 4. Stochastic models may be applied with some relatively minor modifications on other systems, whereas FL models may not. For instance, the stochastic HPW system could be used to model municipal drinking water systems, whereas the FL HPW model should or could not be used on such systems. This is because the FL and stochastic model philosophies of a HPW system are fundamentally different. The stochastic model sees schedule and volume uncertainties as random phenomena described by statistical distributions based on either estimated or historical data. The FL model, on the other hand, simulates schedule uncertainties based on estimated operator behaviour e.g. tiredness of the operators and their working schedule. But in a municipal drinking water distribution system the notion of "operator" breaks down. 5. Stochastic methods can account for uncertainties that are difficult to model with FL. The FL HPW system model does not account for dispensed volume uncertainty, as there appears to be no reasonable method to account for it with FL whereas the stochastic model includes volume uncertainty.

Reforming the legal framework for clinical trials with neonates in Ireland: a children’s rights approach

This thesis assesses the current regulatory framework regarding clinical trials with neonates in Ireland from a children’s rights perspective, as derived from the UN Convention on the Rights of the Child 1989 (UN CRC) and its supporting instruments. The focus on neonates in the thesis is due to the particular need for clinical research with this group of children, their dependency on others for their protection and the lack of attention which has been given to them in the regulatory framework. The importance of children’s rights in this area is linked to the role of human rights in the regulation of clinical research in general. A rights-based approach is of great practical relevance in reforming law, policy and practice. For example, the CRC contains a set of commonly agreed legal benchmarks which can be used to assess the current framework and shape recommendations for reform. In this way, it provides a set of binding norms under international law, which must be complied with by states and state actors in all law, policy and practice affecting children. However, the contribution which a children’s rights approach could make to the regulation of research with children has not, to date, been explored in detail. This thesis aims to address this gap by developing a set of children’s rights-based benchmarks, which are used to assess the Irish regulatory framework for clinical trials with neonates and to develop recommendations for reform. The purpose of the analysis and recommendations is to assess Ireland’s compliance with international children’s rights law in the area and to analyse the potential of children’s rights to effectively address inadequacies in the Irish framework. The recommendations ultimately aim to develop a framework which will enhance the protection of neonates’ rights in this important area of children’s lives.