No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Analysis of flexible manufacturing cells (FMCs) shows their requirement for flexible, correct, reliable, safe and distributed control. A comparison of the state of the art in software engineering for parallel systems, and an examination of safety related systems, reveal a need for formal and rigorous techniques at all stages in the software life cycle. However, parallel software, safety related software and formal techniques are complex. It is better to avoid faults rather than eliminate or tolerate them, and although less flexible, avoidance is often simpler to implement. There is a need for a tool which overcomes many of these complexities, and this thesis discusses and defines such a tool in the form of a methodology. The novelty of the work is in the combination of the core goals to manage these issues, and how the strategies guide the user to a solution which will not deadlock and which is comprehensible. Place-transition Petri nets are an ideal representation for designing and modelling the interaction of concurrent (and distributed) processes. Occam is a high level real time parallel language designed to execute on one or a network of transputers. Transputers are processing, memory and communication building blocks, and, together with occam, are shown to be suitable for controlling and communicating the control as the DCS in FMCs. The methodology developed in this thesis adopts the mathematically based tools of Petri nets, occam and transputers, and, by exploiting their structural similarities, incorporates them in a steps and tasks to improve the development of correct, reliable and hence safe occam code. The four steps: identify concurrent and sequential operations, produce Petri net graphs for all controllers, combine controller Petri net graphs and translate Petri net graphs into occam; are structured around three core goals: Petri net/occam equivalence, comprehensibility and pro-activity; which are manifest in four strategies: output-work-backwards, concurrent and sequential actions, structuralise and modularise, and deadlock avoidance. The methodology assists in all stages of the software development life cycle, and is applicable to small DCSs such as an FMC. The methodology begins by assisting in the creation of DCS requirements from the manufacturing requirements of the FMC, and guides the user to the production of dependable occam code. Petri nets allow the requirements to be specified as they are created, and the methodology's imposed restrictions enable the final Petri net design to be translated directly into occam. Thus the mathematics behind the formal tools is hidden from the user, which should be attractive to industry. The methodology is successfully applied to the example FMC, and occam code to simulate the FMC is produced. Due to the novelty of the research, many suggestions for further work are given.

This research presented in this thesis examines the application of feed forward neural networks to the performance control of a gas transmission compressor. It is estimated that a global saving in compressor fuel gas of 1% could save the production of 6 million tonnes of CO2 per year. Current compressor control philosophy pivots around prevention of surge or anti-surge control. Prevention of damage to high capital cost equipment is a key control driver but other factors such as environmental emissions restrictions require most efficient use of fuel. This requires reliable and accurate performance control. A steady state compressor model was developed. Actual compressor performance characteristics were used in the model and correlations were applied to determine the adiabatic head characteristics for changed process conditions. The techniques of neural network function approximation and pattern recognition were investigated. The use of neural networks can avoid the potential difficulties in specifying regression model coefficients. Neural networks can be readily re-trained, once a database is populated, to reflect changing characteristics of a compressor. Research into the use of neural networks to model compressor performance characteristics is described. A program of numerical testing was devised to assess the performance of neural networks. Testing was designed to evaluate training set size, signal noise, extrapolated data, random data and use of normalised compressor coefficient data on compressor speed estimates. Data sets were generated using the steady state compressor model. The results of the numerical testing are discussed. Established control paradigms are reviewed and the use of neural networks in control l'Iystems were identified. These were generally to be found in the areas of adaptive or model predictive control. Algorithms required to implement a novel compressor performance control scheme are described. A review of plant control hierarchies has identified how the Mdwme might be implemented. The performance control algorithm evaluates current !,!'Ocells load and suggests a new compressor speed or updates the neural network model. {'ornpressor speed can be predicted to approximately ± 2.5% using a neural network h,lt1l'd model predictive performance controller. Comparisons with previous work suggest l'1l1t 'IlUal global savings of 34 million tonnes of CO2 emissions per year. A generic, rotating machinery performance control expert system is proposed.

The modern process plant is characterised by the use of computers for process control. Increasing reliability and diminishing capital investment costs have encouraged the use of process computers as the principal control mechanism. Greater reliability has also led to an increase and change in the tasks assigned to the computer. The relatively rapid advances in process control have understandably resulted in a carry-over of traditional practices which are often inadequate in the present day environment. A notable example of such a practice is the alarm system which is neglected in the literature and is frequently the least satisfactory aspect of a control system.