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A Multi-stage Economic NMPC for the Tennessee Eastman Challenge Process

机译:田纳西州伊斯特曼挑战过程的多阶段经济纳米铅

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This paper addresses the design and implementation of a robust nonlinear model predictive control (NMPC) scheme for a benchmark plant-wide control problem. The focus of our research is on the performance of direct optimizing control for a complex large-scale process which is subject to plant-model mismatch and external disturbances. As a benchmark case for control and monitoring applications, the Tennessee Eastman Challenge (TEC) process has been widely employed in many publications. We present a first NMPC implementation for this where only economics criteria are used for the control of the process. The results obtained demonstrate the viability of plant-wide economics optimizing NMPC. We also address the issue of robustness against model uncertainties and employ multi-stage NMPC to tackle these. Different possible multi-stage NMPC implementations are discussed and the trade-offs between economic performance and robustness are highlighted.
机译:本文解决了用于基准植物范围控制问题的强大非线性模型预测控制(NMPC)方案的设计和实现。我们的研究焦点是对植物模型失配和外部干扰的复杂大规模过程进行直接优化控制。作为控制和监测申请的基准案例,田纳西州伊斯特曼挑战(TEC)进程已被广泛用于许多出版物。我们为此提供了一个第一个NMPC实现,其中仅用于控制过程的经济标准。获得的结果证明了植物 - 宽经济学优化NMPC的活力。我们还解决了对模型不确定性的稳健性问题,并使用多阶段NMPC来解决这些问题。讨论了不同可能的多级NMPC实现,并突出了经济绩效与鲁棒性之间的权衡。

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