Abstract This study presents the estimation of key unknown states for a pilot-scale gasifier using Kalman Filter (KF). The transient behaviour of a pilot-scale gasification unit is represented using a dynamic reduced order model. This model consists of 479 state variables including molar fractions for the species, temperature, and slag thickness across the gasifier. The quality of the state estimation provided by KF has been evaluated under multiple arrangements of the number and the location of the sensors available for the top section of the gasifier. Also, plant-model mismatch, additive uncertainty in the prior estimation, and load-following scenarios have been considered. The results show that KF is capable of estimating the unknown states for a large variety of changes in the gasifier’s inputs, even though online temperature sensors are only available in limited locations across the gasifier.

中文翻译：

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使用卡尔曼滤波器的气流气化系统的状态估计和传感器位置

摘要 本研究介绍了使用卡尔曼滤波器 (KF) 估算中试规模气化炉的关键未知状态。中试规模气化装置的瞬态行为使用动态降阶模型表示。该模型由 479 个状态变量组成，包括物质的摩尔分数、温度和整个气化器的炉渣厚度。KF 提供的状态估计的质量已经在可用于气化器顶部的传感器数量和位置的多种安排下进行了评估。此外，还考虑了工厂模型不匹配、先前估计中的附加不确定性和负载跟随场景。结果表明，KF 能够估计气化器输入的各种变化的未知状态，