PROCESS SIMULATION AND ANN OPTIMIZATION OF CONVENTIONAL AND EMERGING AMMONIA SYNTHESIS TECHNOLOGIES

Abstract

Ammonia production is a crucial process in various industries, including agriculture, pharmaceuticals, and energy. However, traditional ammonia production methods are often energy-intensive and environmentally unsustainable. This study presents a comparative analysis of different ammonia production methods using Artificial Neural Network (ANN) modelling, simulation, and optimization in Python. The ANN models are developed to predict the performance of three ammonia production methods: Haber-Bosch process, solid-state ammonia synthesis, and electrochemical ammonia synthesis. The models are trained using experimental data and optimized using various algorithms to minimize errors and improve accuracy. The simulation results show that the ANN models accurately predict the performance of each ammonia production method. The comparative analysis reveals that electrochemical ammonia synthesis has the potential to be more energy-efficient and environmentally friendly than traditional methods. This study demonstrates the effectiveness of ANN modelling, simulation, and optimization in evaluating and improving ammonia production methods. The findings of this research can inform the development of more sustainable and efficient ammonia production technologies.