From requirements development to verification ~ designing reliability into a large scale chemical manufacturing system

Abstract

Ensuring that reliability, availability, and maintainability (RAM) are inclusive in a system's performance involves complete, up-front enhancement in the overall design process. A design team must address requirements for RAM performance at the very beginning of the project life cycle in order to get the desired RAM results. Companies that large scale systems, especially those that operate in environments that are conducive to failure (high thermal, corrosion, and stress environments) must be designed to withstand those environments. Typically, a project will minimize cost and schedule to bring a new product or service to market, vowing to issues during operation of the system. However, it is a data-supported fact that it costs on the order of 10 times more to fix a problem in a fielded facility than it would have cost to design out that failure mode early in the project life cycle. Companies use measures such as cost of capital and return on capital investment to benchmark performance of new systems - and given the high cost of green field projects, it only makes sense to do it right the first time. This paper details the process for ensuring that reliability, availability, and maintainability are designed into a large system using tried and true techniques, including reliability simulation and modeling, analysis, and verification techniques. This process is intended to be implemented in its entirety or tailored towards a system's needs, but if it is adhered to, will generate the performance results that all project managers are seeking. If a project manager seeks to come in ahead of schedule and under budget without considering the effects on long term performance, then surely reliability will suffer. However, if a project manager approaches the development of his or her system as if he will operate it for a long period of time, the process herein will serve to provide a system design that will generate the productivity that is desired. One final point to mention is that application of the proper, optimized set of resources is key, as well as the availability of historical data that will provide baselines for analysis