The structure of the sensor network installed in the plant strongly influences the performance of state estimation techniques. One of them, the Unscented Kalman Filter (UKF), provides significant improvement over other filtering methods. It approximates the true mean and covariance of random variables that undergo nonlinear transformations correctly up to the third order with low computational effort. In this work, a Sensor Network Design strategy for monitoring nonlinear dynamic chemical processes using UKF is presented. In contrast to previous works, the tradeoff between cost and estimates precision is addressed in a systematic and efficient way. A novel procedure is proposed to calculate a sensible upper bound for the estimation error. This avoids fixing bounds based on engineer judgment about the new process. Regarding efficiency, the obtained sensor network is generally cheaper and provides a global precision which is between the maximum possible for a given budget and the precision obtained by the sensor network that satisfices the maximum system observability for the same budget. This formulation is important when the budget is limited and it is desired to minimize the cost, without losing the quality of the estimates. The proposed methodology can be easily extended to other nonlinear state estimation techniques. The optimal solution is obtained using a level transversal search algorithm with cutting and stopping criteria. A copolymerization process taken from the literature is used to demonstrate the performance of the proposed instrumentation design technique.

安装在工厂中的传感器网络的结构强烈影响状态估计技术的性能。其中之一，无味卡尔曼滤波器（UKF），与其他滤波方法相比，有了很大的改进。它近似地估算了经过非线性转换，且以较低的计算量正确地进行了三阶变换的随机变量的真实均值和协方差。在这项工作中，提出了一种使用UKF监测非线性动态化学过程的传感器网络设计策略。与以前的工作相比，成本和估算精度之间的权衡是以系统有效的方式解决的。提出了一种新颖的程序来计算估计误差的合理上限。这样可以避免基于工程师对新过程的判断来确定界限。关于效率，所获得的传感器网络通常更便宜，并且提供的全局精度介于给定预算的最大可能值与满足相同预算的满足最大系统可观察性的传感器网络所获得的精度之间。当预算有限且希望在不损失估算质量的情况下将成本降至最低时，此公式很重要。所提出的方法可以容易地扩展到其他非线性状态估计技术。使用具有切割和停止标准的水平横向搜索算法可获得最佳解决方案。从文献中获得的共聚过程用于证明所提出的仪器设计技术的性能。