برنامه های صنعتی مدل RAM: توسعه و پیاده سازی یک مدل شبیه سازی RAM برای گیاه لکسان در صنعت پلاستیک GE

A RAM (reliability, availability and maintenance) model has been built for the GE Industrial, Plastics Lexan® plant in Bergen op Zoom, The Netherlands. It was based on a Reliability Block Diagram with a Monte Carlo simulation engine. The model has been validated against actual plant data from two different sources, and against local expert opinions, resulting in a satisfactory simulation model. The model was used to assess two key decisions that were (to be) made by GE Industrial, Plastics concerning operation and shutdown policies of the plant. The model results showed that the operation and maintenance could be further improved, and that in doing so the annual production loss could be reduced further.

The operational availability of equipment in process plants plays a key role in the actual production capacity of plants and their financial returns. The reliability of the plant and its equipment, therefore, determines whether the output of the plant is as planned and whether the plant is profitable or not. Second, reliability is of interest to the process industry from a maintenance engineering point of view because it helps in determining what and how much maintenance should be performed. The field of reliability engineering in the process industry has received a lot of attention in recent years, and many tools and techniques are available, such as availability modeling, RCM (reliability centered maintenance), RBI (risk based inspection), etc. Another incentive for the process industry to take a closer look at reliability engineering are the ever more stricter environment, health, safety (EHS) regulations. It is anticipated that an increase in the reliability of the plant will lead to fewer people working in the plant and lower probability for personal or environmental accidents. This decrease of risks is an important factor for the license to operate. In addition, the rules and regulations set by government are becoming stricter. Many international operating directives states that every operating company should produce a safety report (e.g. Seveso II directive [1]). Even more so, the Dutch implementation of this directive requires every plant to have quantified scenarios of how accidents might occur and of their possible consequences (CPR 20 [2]). A structured approach to reliability engineering, for example in the form of reliability models, can support the processing industry in developing these scenarios.

Extensive research has been done on the theoretical background of availability simulation modeling and the possible benefits for the chemical industry. To date not many companies actually benefit from the opportunities that reliability simulation modeling provides. This study has, therefore, been performed within an industrial organization. It has resulted in a practical application of the tool and it was focused on the opportunities for implementation. The research has attempted to overcome the gap between theoretical knowledge and related tools, and the actual situation in an industrial company. First, we have seen that availability modeling is not widely spread throughout industry yet, because most companies are still optimizing their maintenance policies and are thus focusing on the implementation of structural improvement processes in that area. However, it is regarded as a valuable tool for availability optimization in design and operating stages and will be the next step after the structural improvement programs. At this stage, however, there are still more simple benefits to be gained. Second, simulation modeling is now mainly performed by consultants because it requires specific modeling knowledge and is a labor intensive activity and therefore it takes large effort to implement this in a manufacturing organization. The research has shown, however, that the effort has a very short payback period. Third, due to more strict laws and regulations that require more and more quantified scenarios for safety and risks, there will be a trend that will increase the use of simulation and risk quantification. A rapid increase of simulation modeling can be expected.