Distributed state estimation in large-scale processes decomposed into observable subsystems using community detection
Distributed state estimation in large-scale processes decomposed into observable subsystems using community detection
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DOI：
10.1016/j.compchemeng.2021.107544
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
L. S. Masooleh;Jeffrey E. Arbogast;W. Seider;U. Oktem;M. Soroush
中科院分区：
文献类型：
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作者：
L. S. Masooleh;Jeffrey E. Arbogast;W. Seider;U. Oktem;M. Soroush
为了有效地控制和监视过程，有时需要关于过程的状态变量的足够频繁的信息。然而，它在实践中并不经常可用，这可以使用状态估计器来解决。这项工作涉及大规模过程中的分布式状态估计。将过程分解为多个可观测子系统的问题转化为一个优化问题，并采用一种有效的鲸鱼优化算法进行求解。四个非线性状态估计方法（扩展卡尔曼，无迹卡尔曼，球形无迹卡尔曼，和容积卡尔曼滤波），然后实施和比较使用分布式和集中式架构的过程中，由两个反应堆和一个分离器，和田纳西伊士曼过程。提出了一种提高分布式体系结构计算效率的并行化策略。仿真结果表明，并行实现的分布式滤波方法是计算更有效的，而产生类似的准确的状态估计比他们的集中式同行。
Adequate frequent information on state variables of a process is sometimes needed for effective control and monitoring of the process. However, it is not often available in practice, which can be addressed using a state estimator. This work deals with distributed state estimation in large-scale processes. The decomposition of a process into observable subsystems is formulated as an optimization problem, which is solved using an efficient whale optimization algorithm. Four nonlinear state estimation methods (extended Kalman, unscented Kalman, spherical unscented Kalman, and cubature Kalman filtering) are then implemented and compared using distributed and centralized architectures on a process consisting of two reactors and a separator, and the Tennessee Eastman process. A parallelization strategy that improves the computational efficiency of the distributed architecture is proposed. Simulation results show that the parallel implementation of the distributed filtering methods is computationally more efficient than their centralized counterparts while yielding similarly accurate state estimates.