Development of AI-Driven Spectroscopic Analysis for Industrial Catalyst Monitoring

Thesis Abstract

Industrial catalysts play a pivotal role in optimizing chemical processes, yet their effective monitoring remains a significant challenge due to complex reaction dynamics and the limitations of traditional analytical techniques. This study aims to develop a novel AI-driven spectroscopic analysis framework to enhance real-time monitoring and predictive maintenance of industrial catalysts, thereby improving efficiency, reducing operational costs, and minimizing environmental impacts. The specific objectives include (1) evaluating the applicability of various spectroscopic techniques such as Fourier-transform infrared (FTIR), Raman, and Near-Infrared (NIR) spectroscopy for catalyst analysis; (2) designing and implementing machine learning models—including regression algorithms, support vector machines (SVM), and deep learning architectures such as convolutional neural networks (CNN)—to interpret spectral data; (3) establishing data preprocessing and feature extraction protocols to optimize model performance; and (4) validating the developed models through field trials within an operational catalytic processing plant. Employing a mixed-method research design, the study collected spectral data from a population of approximately 150 catalyst samples sourced from a chemical manufacturing plant over six months. Data acquisition involved spectroscopic measurements using FTIR, Raman, and NIR instruments calibrated specifically for catalyst monitoring, with calibration curves generated for key parameters such as surface composition and activity levels. Complementary data, including operational parameters and catalyst lifespan, were obtained through plant reports and maintenance logs. The models were developed and trained using a data set comprising 1,200 spectral instances, split into training (70%) and testing (30%) subsets. Machine learning algorithms—including multiple linear regression, SVM with radial basis function kernels, and deep CNNs—were implemented using Python-based libraries (scikit-learn, TensorFlow). Model validation involved cross-validation techniques, root mean square error (RMSE), coefficient of determination (R²), and confusion matrices where applicable, to assess accuracy and robustness. Analytical procedures also included Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) for feature reduction and correlation analysis, supported by the theoretical framework of the Knowledge-based Systems theory and the Diffusion of Innovations theory, which underpin the integration of AI into industrial processes. Expected findings indicate that the AI-driven models will outperform traditional spectroscopic interpretation methods in accuracy, speed, and predictive capability, providing a reliable basis for real-time catalyst monitoring. It is anticipated that deep learning models, particularly CNNs, will demonstrate superior performance in identifying complex spectral patterns associated with catalyst degradation and activity shifts, with R² values exceeding 0.92 and RMSE reductions of 35% compared to conventional regression methods. The study is expected to reveal significant correlations between spectroscopic features and catalyst health indicators, facilitating predictive maintenance strategies that proactively address catalyst deactivation issues. These contributions extend existing knowledge by integrating advanced AI techniques with spectroscopic analysis, offering a scalable, cost-effective solution for industrial catalyst management. The main conclusion emphasizes that AI-enhanced spectral analysis significantly advances catalyst monitoring, offering manufacturers a precise, timely tool to optimize catalyst lifespan and operational efficiency. The study recommends adopting AI-integrated spectroscopic systems in industrial settings, alongside ongoing data-driven process optimization. Future research should explore the application of explainable AI models for enhanced interpretability and the potential integration with other sensor data for comprehensive process control. This research underscores the transformative potential of AI-backed spectroscopy in industrial catalysis, promising substantial improvements in process sustainability and economic performance.