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Fluid catalytic cracking unit control using model predictive control and adaptive neuro fuzzy inference system: Comparative study

机译:基于模型预测控制和自适应神经模糊推理系统的流化催化裂化装置控制:对比研究

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The disrupted energy markets, growing environmental regulations and economic downtrend have driven the oil and gas industry to search for more efficient strategies to control its facilities. Fluid Catalytic Cracking (FCC) has kept its key role between processes of oil refineries due to its contribution in refinery's overall performance and impact on economic profitability. Multiple control techniques have been studied to control the unit, as it presents a very strong challenge for control theory because of its complexity. This work compares Model Predictive Control (MPC); one of the most successful control technologies in industry to Adaptive Neuro Fuzzy Inference Systems (ANFIS) controller, as a candidate of the artificial intelligence control techniques.
机译:能源市场的混乱,不断增长的环境法规和经济下降趋势,驱使石油和天然气行业寻求更有效的策略来控制其设施。流体催化裂化(FCC)由于其对炼油厂整体性能的贡献和对经济盈利能力的影响,因此在炼油厂过程之间保持了关键作用。为了控制单元,已经研究了多种控制技术,因为它的复杂性对控制理论提出了非常严峻的挑战。这项工作比较了模型预测控制(MPC);自适应神经模糊推理系统(ANFIS)控制器是工业上最成功的控制技术之一,是人工智能控制技术的候选者。

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