Integration of Green Ammonia into Smart Grids: Neural Network-Based Modeling and Control for Direct Ammonia Synthesis and Fuel Cells

Abstract

The current green ammonia production method involves generating green hydrogen via an electrolyzer and combining it with nitrogen through the Haber Bosch (HB) process to produce ammonia. A newer method, Direct Ammonia Synthesis (DAS), is gaining attention as it can produce green ammonia directly using an Electrochemical Ammonia Synthesizer (EAS) without the electrolyzer and HB system, significantly reducing costs and energy consumption. The produced ammonia can be directly converted to electricity using Direct Ammonia Fuel Cells (DAFC). Additionally, ammonia addresses hydrogen-related issues such as high flammability, poor volumetric density, and high storage costs. First, the thesis focuses on the DAS approach. It explores the integration of EAS and DAFC into the grid as a means to provide stable power through the utilization of various power smoothing filters. The EAS converts excess wind/solar power into green ammonia, which is then used by DAFC to produce electricity during power deficits. Second, a novel neural network (NN) model for EAS is developed to simplify the traditionally complex and sensor-intensive modeling of electrochemical systems. This NN model accurately predicts ammonia production based on solar power, nitrogen, and water inputs. Third, an NN model for DAFC is created to output electrical power from ammonia. Both EAS and DAFC NN models can be integrated into the existing microgrid system models in MATLAB-Simulink and Python. Finally, the thesis introduces a Neural Network-based Model Predictive Control (NNMPC) approach for regulating EAS output and meeting the ammonia demand, which demonstrates superior accuracy and efficiency compared to the traditional fuzzy logic control method. Unlike a traditional MPC, which uses a mathematical plant model for predictive optimization, an NN model demonstrates superior accuracy in encapsulating plant dynamics. The NNMPC addresses mathematical intricacies in MPC models, especially as plant complexities increase. Simulation results confirm the effectiveness of the NN models and NNMPC in practical applications. The research conducted in this thesis has resulted in journal and conference research publications as well as a collaborative project with a Waterloo-based company.