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.

Research and design of high-speed advanced analogue front-ends for fibre-optic transmission systems

In the last decade, we have witnessed the emergence of large, warehouse-scale data centres which have enabled new internet-based software applications such as cloud computing, search engines, social media, e-government etc. Such data centres consist of large collections of servers interconnected using short-reach (reach up to a few hundred meters) optical interconnect. Today, transceivers for these applications achieve up to 100Gb/s by multiplexing 10x 10Gb/s or 4x 25Gb/s channels. In the near future however, data centre operators have expressed a need for optical links which can support 400Gb/s up to 1Tb/s. The crucial challenge is to achieve this in the same footprint (same transceiver module) and with similar power consumption as today’s technology. Straightforward scaling of the currently used space or wavelength division multiplexing may be difficult to achieve: indeed a 1Tb/s transceiver would require integration of 40 VCSELs (vertical cavity surface emitting laser diode, widely used for short‐reach optical interconnect), 40 photodiodes and the electronics operating at 25Gb/s in the same module as today’s 100Gb/s transceiver. Pushing the bit rate on such links beyond today’s commercially available 100Gb/s/fibre will require new generations of VCSELs and their driver and receiver electronics. This work looks into a number of state‐of-the-art technologies and investigates their performance restraints and recommends different set of designs, specifically targeting multilevel modulation formats. Several methods to extend the bandwidth using deep submicron (65nm and 28nm) CMOS technology are explored in this work, while also maintaining a focus upon reducing power consumption and chip area. The techniques used were pre-emphasis in rising and falling edges of the signal and bandwidth extensions by inductive peaking and different local feedback techniques. These techniques have been applied to a transmitter and receiver developed for advanced modulation formats such as PAM-4 (4 level pulse amplitude modulation). Such modulation format can increase the throughput per individual channel, which helps to overcome the challenges mentioned above to realize 400Gb/s to 1Tb/s transceivers.