Estimation-based model predictive control of an electrically-heated steam methane reforming process

IF 3 Q2 ENGINEERING, CHEMICAL Digital Chemical Engineering Pub Date : 2024-04-24 DOI:10.1016/j.dche.2024.100153
Xiaodong Cui , Berkay Çıtmacı , Dominic Peters , Fahim Abdullah , Yifei Wang , Esther Hsu , Parth Chheda , Carlos G. Morales-Guio , Panagiotis D. Christofides
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Abstract

The surge in demand for hydrogen (H2) across diverse sectors, including clean energy transportation and chemical synthesis, underscores the need for a thorough investigation into H2 production dynamics and the development of effective controllers for industrial applications. This paper focuses on an electrically heated steam methane reforming (SMR) process for H2 production, offering advantages such as enhanced environmental sustainability, compactness, efficiency, and controllability compared to conventional reforming methods. Electric heating of the entire system allows for adjustments in current to control reactor temperature, thereby impacting hydrogen production rates. However, accurately modeling hydrogen production dynamics presents a formidable challenge, as complex models with high precision are computationally unsuitable for real-time control integration. Considering these factors, an accurate and efficient first-principles-based lumped-parameter model is developed to provide a dependable estimation of hydrogen production in an electrically-heated steam methane reformer. This model is validated experimentally and then utilized in a model predictive controller (MPC). To obtain the necessary state estimate information for the MPC, an extended Luenberger observer (ELO) method is employed to estimate state variables from limited, infrequent and delayed measurements of gas-phase reactor outlet stream and frequent measurements of the reactor temperature. Simulation comparisons with a proportional-integral (PI) controller reveal a much faster response in achieving the desired H2 production rate under the estimation-based MPC. Additionally, the simulations demonstrate the robustness of the controller to process variability such as a decrease in catalyst activation energy, commonly encountered in the SMR process, highlighting its effectiveness in maintaining stable operation under varying process conditions.

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基于估计的电加热蒸汽甲烷转化过程模型预测控制
包括清洁能源运输和化学合成在内的各行各业对氢气(H2)的需求激增,这凸显了对氢气生产动态进行深入研究并为工业应用开发有效控制器的必要性。与传统重整方法相比,电加热蒸汽甲烷重整(SMR)工艺具有更强的环境可持续性、紧凑性、高效性和可控性等优势。通过对整个系统进行电加热,可以调节电流来控制反应器温度,从而影响氢气生产率。然而,对制氢动态进行精确建模是一项艰巨的挑战,因为高精度的复杂模型在计算上不适合实时控制集成。考虑到这些因素,我们开发了一种基于第一原理的精确、高效的整块参数模型,用于可靠地估算电加热蒸汽甲烷转化炉的制氢量。该模型经过实验验证,然后用于模型预测控制器 (MPC)。为了获得 MPC 所需的状态估计信息,采用了扩展卢恩伯格观测器 (ELO) 方法,通过对反应器出口气流的有限、不频繁和延迟测量,以及对反应器温度的频繁测量来估计状态变量。与比例-积分 (PI) 控制器的仿真比较显示,基于估计的 MPC 在实现所需的 H2 生产率方面反应更快。此外,模拟还证明了控制器对 SMR 过程中常见的催化剂活化能下降等过程变化的稳健性,突出了其在不同过程条件下保持稳定运行的有效性。
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