| Summary: | 碩士 === 逢甲大學 === 化學工程學所 === 97 === This thesis considers the dynamic simulation, optimal design and control of the propane steam reformer reactor which used to produce hydrogen. A finite element method is used to build the hydrogen generation model for the propane steam reformer reactor. In order to make the high conversion rate for hydrogen production, a real coded genetic algorithm is applied for the optimal design of the propane steam reformer reactor. Besides, for exit hydrogen concentration regulation of steam reformer reactor, a direct adaptive model-free learning control scheme is proposed. An output-bounded single neuron controller is utilized for the learning control of the process by merely observing the process output error, even though the process dynamics is characterized by partial differential equations. Extensive simulation results reveal that the proposed model-based control strategy is more promising to existing methodologies, such as IMC-PI and PI controls. From the significant results presented in this thesis, it is believed that the proposed optimal design and control strategies can substantially enhance the effectiveness and performance of propane steam reforming processes.
|
|---|
