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Mechanistic Optimization of Commercial Gas Dehydration and Natural Gas Liquids Recovery Units

机译:商用煤气脱水和天然气液体回收单位的机械优化

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Objective: This study aims at high-fidelity modeling and mechanistic optimization of gas dehydration and NGL (Natural Gas Liquids) systems of a commercial natural gas plant based in Abu Dhabi, UAE. Scope of the work includes development of models, validation of models with plant data, optimization analysis and real-time validation at the plant site. Method: In this work, we developed a dynamic model for the gas dehydration system and a steady state model for the natural gas liquids recovery unit. An advanced process simulator that follows equation-oriented approach is employed as the modelling and optimization platform. We first show the comprehensive plant data reconciliation followed by the model validation using the operating data of the years 2016 and 2018, to ensure that the model predictions match the real plant operation. We then present how the mechanistic optimization entity result in the best operating conditions for the natural gas liquids recovery system. We also show the optimization analysis that aims at maximizing the adsorption cycle time for the dehydration unit while minimizing the total heating duty required for the regeneration of the molecular sieve beds. Results: Optimization analysis reveals a significant increase in the annual net revenue of natural gas liquid recovery unit as a result of modifying various process operating conditions that lead to higher liquid hydrocarbon production and lower operating costs related to steam and refrigeration. Similarly, optimization analysis of the dehydration system indicates that adsorption-step time can be increased to a higher value, which results in significant reduction of regeneration costs. As a next step, we aim to carry out the validation tests on the plant site to verify and implement the model recommendations in the real plant to verify the model recommendations. We also plan to derive the set of operating guidelines that allow the operators to drive the plant towards optimal operation. Novelty & Significance: 1. To the best knowledge of authors, this study is the first effort to build a holistic model comprising of the dynamic dehydration model and steady state NGL model on a common platform. 2. Rigorous validation of the model is performed using real plant data of two calendar years. 3. Scope of this study also includes real time validation of the model recommendations through tests performed at plant site.
机译:目的:本研究旨在基于阿联酋阿布扎比的商业天然气厂的气体脱水和NGL(天然气液体)系统的高保真建模和机械优化。工作范围包括模型的开发,验证具有植物数据的模型,优化分析和工厂网站的实时验证。方法:在这项工作中,我们开发了一种用于天然气液体回收单元的气体脱水系统和稳态模型的动态模型。遵循方程导向方法的高级过程模拟器被用作建模和优化平台。我们首先展示了综合工厂数据和解,然后使用2016年和2018年的运营数据进行模型验证,以确保模型预测符合实际工厂操作。然后我们提出了机械优化实体如何导致天然气液体回收系统的最佳操作条件。我们还展示了优化分析,其旨在最大化脱水机组的吸附循环时间,同时最小化分子筛床再生所需的总加热占空比。结果:优化分析显示天然气液体回收单元年度净收入的显着增加,导致修改各种过程操作条件,导致液体烃生产较高,较低与蒸汽和制冷相关的运营成本。类似地,脱水系统的优化分析表明吸附步骤时间可以增加到更高的值,这导致再生成本显着降低。作为下一步,我们的目标是在工厂网站上进行验证测试,以验证和实施真实工厂中的模型建议,以验证模型建议。我们还计划推导出一组操作指南,使运营商能够将工厂推向最佳操作。新颖性和意义:1。对于作者的最佳知识,这项研究是建立一个整体模型的第一努力,包括在共同平台上的动态脱水模型和稳态NGL模型。 2.使用两个日历年的真实工厂数据进行模型的严格验证。 3.本研究的范围还包括通过在工厂网站上进行的测试来实时验证模型建议。

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