A Solution to Existing Performance Monitoring Inherited Limitation Using Smart Performance Monitoring Combining Diagnostic Capabilities and Value Creation Determination

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Abstract

This paper related to the development of an effective operating plan to ensure sustained optimum operation in the operating or process oriented facilities. In this paper, an illustration of an operating plan of the steam system in a Gas Oil Separation Plant (GOSP) is described in detail. The tool adopts the advanced analytic modelling algorithm and optimization to provide the users (engineers and operators) with the recommended operating plan for the mode of operations in question for the next day, next week or next month. It involves two steps; namely the predictive and prescriptive phases. The first step allows the prediction of the performance be made based on the given historical performance data, while the second step, utilizing the predicted performance data, employs the optimization algorithm to prescribe the predicted optimum performance. With this tool, by being proactive, the tool has guided the users to sustain optimum performance and specifically, during the deployment of the tool in the steam system, the fuel gas savings of around 1 MMSCFD were captured. Furthermore, by leveraging on this tool, the users are better equipped with a sound strategy for their next day of operation as they are now able to at least anticipate what to expect in the immediate future operation and to ensure their readiness of any eventualities during the operation to make the operation safer and reliable all the times. Is it worth to mention that this idea has been implemented at fuel gas system and it will require engineering and resources to extend the implementation to cover all energy parameters.

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APA

Alabdulmohsen, M., & Alddin, K. A. (2020). A Solution to Existing Performance Monitoring Inherited Limitation Using Smart Performance Monitoring Combining Diagnostic Capabilities and Value Creation Determination. In Advances in Intelligent Systems and Computing (Vol. 1134, pp. 673–677). Springer. https://doi.org/10.1007/978-3-030-43020-7_92

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