Numerous nonlinear distributed parameter systems (DPSs) operate within an extensive range due to process uncertainties. Their spatial distribution characteristic, combined with nonlinearity and uncertainty, poses challenges in optimal operation under two-step real-time optimization (RTO) and economic model predictive control (EMPC). Both methods necessitate substantial computational power for prompt online reoptimization. Recent local distributed parameter self-optimizing control (DPSOC) achieves optimality without repetitive optimization. However, its effectiveness is confined to a narrow range around a nominal operation. Here, globalized DPSOC is introduced to surmount the limitation of the local DPSOC. A global loss functional concerning controlled variables (CVs) is formulated using linear operators and Fubini's theorem. Minimizing the loss with a numerical optimization procedure yields CVs exhibiting global optimality. Maintaining these CVs at constants ensures such a process has a minimal average loss in a large operating space. The effectiveness of the proposed method is substantiated through a transport reaction simulation.

中文翻译：

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分布式参数自优化控制的全球化


由于过程的不确定性，许多非线性分布参数系统（DPS）在很大的范围内运行。它们的空间分布特征，加上非线性和不确定性，对两步实时优化（RTO）和经济模型预测控制（EMPC）下的优化运行提出了挑战。这两种方法都需要大量的计算能力来快速在线重新优化。最近的局部分布式参数自优化控制（DPSOC）无需重复优化即可实现最优性。然而，其有效性仅限于名义操作周围的狭窄范围内。这里引入全局DPSOC来克服局部DPSOC的限制。使用线性算子和 Fubini 定理制定有关受控变量 (CV) 的全局损失函数。通过数值优化过程最小化损失可以产生表现出全局最优性的 CV。将这些 CV 保持恒定可确保该过程在较大的操作空间中具有最小的平均损失。通过输运反应模拟证实了所提出方法的有效性。