Optimal Sensor Location in Chemical Plants Using the Estimation of Distribution Algorithms,Industrial & Engineering Chemistry Research

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Optimal Sensor Location in Chemical Plants Using the Estimation of Distribution Algorithms
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2018-08-30 , DOI: 10.1021/acs.iecr.8b01680
Mercedes Carnero ₁ , José L. Hernández ₁ , Mabel Sánchez ₂

Affiliation

The optimal selection of sensor structures improves the knowledge of the current plant state, which is a central issue for the decision-making process. Instrumentation design is a challenging optimization problem that involves a huge amount of binary variables that represent the possible sensor locations. In this work, the limitations of the current design strategies are discussed, and they support the application of evolutionary solution methods. Among them, the estimation of distribution algorithms (EDAs) arises as a convenient alternative to solving the problem. These are stochastic optimization strategies devised to capture complex interactions among problem variables by learning the probabilistic model of candidate solutions and its sampling to generate the next population. From the broad spectrum of EDAs that use multivariate models, two representative procedures are selected that significantly differ in the methods used for learning and sampling those models. Furthermore, a comparative performance study is conducted to evaluate the benefits of increasing the complexity of the distribution model with respect to a memetic procedure based on univariate models.

中文翻译：

基于分布算法估计的化工厂中传感器的最佳位置

传感器结构的最佳选择可提高对当前工厂状态的了解，这是决策过程的核心问题。仪器设计是一个具有挑战性的优化问题，涉及大量代表可能的传感器位置的二进制变量。在这项工作中，讨论了当前设计策略的局限性，它们支持演化解决方案方法的应用。其中，分布算法（EDA）的估计是解决问题的一种方便的替代方法。这些是随机优化策略，旨在通过学习候选解决方案的概率模型及其采样以生成下一个总体来捕获问题变量之间的复杂相互作用。从使用多元模型的广泛EDA中，选择了两个有代表性的程序，它们在学习和采样这些模型的方法上有很大不同。此外，进行了一项比较性能研究，以评估相对于基于单变量模型的模因过程而言，增加分布模型的复杂性的好处。

更新日期：2018-08-31

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