FRAMS : A software prototype for incorporating Flexibility, Reliability, Availability, Maintenance and Safety in complex systems design and operations
T. THOMAIDIS – E. PISTIKOPOULOS Centre for Process Systems Engineering Imperial College,
– FRAMS Methodology is based on stochastic modeling for the continuous and discrete state uncertainty, a set of analytical tools for the quantification of operability aspects and the identification/ranking of critical parts of equipment and critical events,
while supported by a software package utilized for design and operability optimization of process systems.
– The incorporation of operability measures at the early design level of complex systems has economic benefits, while resulting in inherently more flexible, reliable and safe design configurations.
However, the integration of operability objectives (such as flexibility, controllability, reliability, maintainability and safety) in the design stage of complex systems requires a common mathematical framework [1,2]:
• Continuous uncertain parameters either internal or external to the system-usually described by continuous probability distribution functions.
• Discrete uncertainty such as equipment availability, unexpected events / faults, reliability models and discrete probability functions are used.
• A system model, i.e. a set of equality and inequality constraints and/or physical operational model equations, design equations/specifications, product specifications, etc; typically a differential algebraic model is employed
Integration of flexibility, reliability and maintenance in process synthesis and design
Centre for Process Systems Engineering, Department of Cliemical Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BY, U.K.
This paper proposes an MINLP model that represents the stochastic process of system failures and repairs as a continuous-time Markov chain, based on which it optimizes the selection of redundancy and the frequency of inspection and maintenance tasks for maximum profit. The model explicitly accounts for every possible state of the system. Effective decomposition and scenario reduction methods are also proposed. A small example with two processing stage is solved to demonstrate the impact of incorporating maintenance considerations. A decomposition method and a scenario reduction method are applied to this example and are shown to drastically reduce the computational effort. A larger example with four stages, which is not directly solvable, is also successfully solved using the proposed algorithm. Lastly, we show that the proposed model and algorithms are capable of solving a practical problem based on the air separation process example that motivated our work, which features multiple stages, potential units and failure modes