Simulation and quantitative analysis of Raman spectra in chemical processes with autoencoders

Abstract

Raman spectroscopy represents an advanced process analytical technology to monitor and control chemical and biochemical processes. This study presents an autoencoder-based methodology that simulates Raman spectra from process variables and predicts the concentrations of different chemicals. The methodology accurately predicts concentrations from the spectra, even considering the temperature influences, and can work as an anomaly detector in process monitoring. The proposed methodology has significant implications for the optimization of industrial processes, improving process efficiency, reducing waste, and minimizing costs. It can also be extended to other industrial processes and imaging spectroscopy techniques, making it a valuable tool for process monitoring. This study highlights the effectiveness of autoencoders in simulating spectra and quantitative analysis, contributing significantly to the field of process monitoring. It has the potential to revolutionize industrial process monitoring and optimization, leading to substantial improvements in productivity and sustainability.