The throughput, reliability, availability, maintainability (TRAM) methodology for predicting chemical plant production

Fault tree analysis is a method for evaluating reliability and availability in terms of equipment system "states", but this method does not lend itself easily to the evaluation of equipment interactions through time. This makes fault trees difficult to use for the analysis of systems whose reliability and availability depend on complex interactions between its subsystems. This difficulty is overcome by combining fault trees with discrete event simulation methods. The new TRAM methodology combines models and techniques for the analysis of throughput, availability, reliability, and maintainability into a single approach. This paper describes the TRAM methodology and illustrates it with an application to a chemical processing plant. TRAM combines fault tree analysis at a low level of the system description and discrete event simulation at a higher level to create a new method for analyzing the availability and throughput capacity of material processing plants. Failure and repair data is modeled stochastically by a very flexible type of finite mixture distribution that allows the analyst to separate the effects of different repair strategies, such as the reliance on procurement of off-site (vs. on-site) spare parts. An important application of the TRAM method is to facilitate the design of a plant that tolerates outages of its subsystems in the most efficient way possible. Mitigation strategies including in-process storage, alternate work-flows, availability of spare parts, and design for over-production: all of these can be assessed using the TRAM approach, and it thereby facilitates the design of more robust manufacturing systems. The TRAM methodology enables sophisticated "what-if" analyses of alternative designs, e.g. equipment sets, capacities (tanks sizes), shift schedules, spare parts, etc. to optimize plant design and operation. It is a stochastic, time dependent process that provides probabilities of success (or failure) and confidence bounds on availability and throughput. Finally, the TRAM methodology can help plant managers and owners to focus on the plant production metrics by which they are compensated, and not solely on abstract metrics such as availability. Accordingly, TRAM is potentially a more influential tool in the industry than conventional RAM methods. The TRAM method is based on the discrete event formalism developed by Zeigler et al. [1], and explained further in [2]. In TRAM the plant model is completely separated from the simulation