Adaptive Neuro-Fuzzy Inference Systems (ANFIS) - Based Model Predictive Control (MPC) for Carbon Dioxide Reforming of Methane (CDRM) in a Plug Flow Tubular Reactor for Hydrogen Production

Abstract

The current sources of our energy supply are plagued with many problems, and the impact on the climate is of grave concern. To preserve and sustain our environment, a non-polluting and renewable energy source is required. Hydrogen (H2), when extracted from one of its many sources during carbon dioxide (CO2) capture, is considered a non-polluting, efficient and environmentally sustainable energy source. In this research work, the control of a pilot-scale reformer for the production of hydrogen was studied. Hydrogen was produced through the carbon dioxide reforming of methane (CDRM). This process was used to convert methane (CH4) and carbon dioxide into hydrogen. A high methane conversion was maintained by controlling the temperature in the reformer at the thermodynamically desired level. The control strategy applied to this process was the model predictive control (MPC) based on an adaptive neuro-fuzzy inference system (ANFIS) model. MPC has, among other advantages, the ability to predict the response of the system over a given prediction horizon. Experimental results showed that the ANFIS model was able to accurately replicate the response of the process to changes in temperature. Based on the ANFIS model, an MPC strategy was formulated for the process.