Application of physics informed neural networks (pinn) in quality control of the haber-bosch process product

Abstract

The article considers the possibilities of using Physics-Informed Neural Networks (PINNs) for quality control of ammonia produced by the Haber-Bosch process. The ammonia synthesis process is a highly nonlinear and important industrial process where product stability and high quality are critical. Traditional quality control methods face limitations, such as the lack of direct online measurements of key parameters and the need for large amounts of data to build models. PINNs offer a hybrid approach that combines physical laws of the process with deep learning capabilities, allowing for accurate real-time prediction of ammonia concentration and purity based on a limited set of sensors. The paper analyses current research on the use of neural networks in industrial processes and

justifies the architecture of a PINN model for monitoring product quality in ammonia synthesis. The advantages of PINNs over traditional methods are shown, such as reducing the need for data, providing physically consistent results, and integrating into existing control systems to improve production efficiency. It is proved that physically based neural networks offer a new level of intelligent control for chemical processes. In the case of ammonia synthesis, this approach allows solving long-standing quality control problems in a new way by combining science and data. The implementation of PINN in ammonia production will potentially provide higher product quality, operational flexibility and process sustainability, contributing to the progress towards Industry 4.0 [20] in the chemical industry. This is a step towards smarter and more efficient plants, where every critical process is under the reliable supervision of combined human and machine intelligence. The technology is expected to increase process yield and stability, reduce energy consumption (through optimal operating conditions) and improve safety (through early detection of deviations and potential malfunctions, which can have a significant economic impact on a global industry scale and reduce natural gas consumption and CO₂ emissions.