An algorithm for optimal sensor network design for multi-scale, time-varying differential algebraic equation systems with non-separable dynamics is presented. As the process is time-varying, an integral normalized posterior error covariance of a multi-scale filter is minimized to obtain the optimal sensor locations. For reducing the computational cost, an adaptive sampling rate approach is considered for the slowly-varying variables. The algorithm is applied to a smart refractory brick with embedded sensors as part of an entrained-flow gasifier. Thermistors and interdigital capacitors are considered as candidate measurement technologies for estimating temperature and slag penetration profile along the gasifier wall. When the optimal set of sensors obtained from the algorithm is used for estimating temperature and slag penetration profiles in a multi-scale Kalman filter framework, satisfactory estimates are obtained despite high measurement noise and model mismatch.

提出了一种具有不可分动力学的多尺度时变微分代数方程组最优传感器网络设计算法。由于该过程是随时间变化的，因此将多尺度滤波器的积分归一化后验误差协方差最小化，以获得最佳传感器位置。为了降低计算成本，考虑对缓慢变化的变量采用自适应采样率方法。该算法被应用于带有嵌入式传感器的智能耐火砖，作为夹带流气化炉的一部分。热敏电阻和叉指电容器被认为是候选测量技术，用于估算气化炉壁上的温度和炉渣渗透曲线。