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We present a mechanism for modeling recipes with the same control structure used in logic controllers designed to the ANSI/ISA-88 standard as it applies to Batch Control Language. This mechanism enables the engineering of recipes through the modeling environment and into the plant floor operational recipes. The use case we present is that of manufacturing the active ingredient in Aspirin – acetyl salicylic acid. We use a simple production recipe and generate a simulation model from that recipe, collecting data on that process. We have developed campaign and multi-campaign models using this technique as well, and have automatically generated production recipes for control systems such as Provox and DeltaV.
This paper describes an analysis performed to assess the fidelity, scalability, and performance of the Sage® fab advisor's semiconductor fab simulation engine executing two years of fab operations across a range of lot sizes. We describe the demand and fab operations models used, as well as the tools and methodology used in conducting the analysis. Our results were validated against a well-known model running on a well-known toolset, showing performance to be very competitive with that model. Further, we show that our engine's performance, running this model, scales almost linearly from 25 wafer lot sizes down to single wafer lot sizes. that is, simulation time increases roughly linearly with respect to the number of lots being processed.
This paper describes a simulation system that monitors operations on a production floor, periodically creating a model of those operations, and running a simulation that predicts the next several shifts' worth of events, providing operators with new predictive analysis capabilities.
As a set of procedures is carried out in the model and the real world, deviations are introduced by the variations between the expected activities and the actual occurrences. More deviations arise from explicit adaptations undertaken by operations staff in response to already-observed anomalies. with each cycle, those deviations are integrated into the model, heuristics are applied to estimate the likely future course of events, and after a simulation run, a new set of predictions is generated from that model, and it compares the new predictions with the last run's predictions. Differences between the pre-loop prediction and the post-loop prediction serve to indicate whether the situation is improving or degrading.
This assessment is intended to provide a general survey of the strengths and weaknesses of approaching a simulation problem using a workbench-style tool versus approaching that same problem with source code, a simulation server and class libraries (which, along with some quick-start wizards and design tools, characterizes Sage®).
This paper describes the philosophy, architectures and key features of a new .net-based simulation environment called Sage®. Sage® is comprised of a simulation executive, a set of class libraries and some tools built on top of Microsoft's .Net platform. It was built to take advantage of the object-oriented flavor and extensive integration plumbing ingrained in the .Net framework. It supports 'active entity', 'block-based', 'workflow-oriented' and several other types of simulation architectures in both the discrete-time and continuous domains. It enables developers to approach their simulation frameworks or applications in a wide range of languages including such inexpensive and available languages as C# and VB.Net.