Development of AI-Driven Spectroscopic Analysis for Rapid Industrial Catalyst Evaluation

Thesis Abstract

The rapid and accurate evaluation of industrial catalysts remains a critical challenge in chemical manufacturing, where traditional spectroscopic techniques such as Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and X-ray diffraction (XRD) are often hindered by lengthy analysis times and subjective interpretation, leading to delays in catalyst deployment and suboptimal process efficiency. This study aims to develop an integrated artificial intelligence (AI)-driven spectroscopic analysis framework that enhances the speed, accuracy, and reliability of catalyst evaluation processes. The specific objectives include (1) to design and train machine learning models capable of interpreting spectral data for catalyst quality assessment; (2) to systematically compare the performance of various algorithms including convolutional neural networks (CNNs) and support vector machines (SVMs); (3) to optimize the spectral data preprocessing techniques to improve model robustness; and (4) to validate the developed models using real-world industrial catalyst samples. A quantitative research design employing a cross-sectional approach was adopted, selecting a population of 150 catalyst samples sourced from a petrochemical plant over a six-month period. A stratified random sampling method was used to ensure representative inclusion of catalyst types with known performance variances. Data collection involved acquiring spectral data using FTIR, Raman, and XRD instruments, complemented by traditional catalyst performance indices obtained through laboratory activity tests. The spectral datasets, comprising over 10,000 individual spectra, were preprocessed using baseline correction, normalization, and feature extraction techniques such as principal component analysis (PCA). The AI models were developed within a supervised learning framework, with catalyst quality labels established based on standardized activity tests. Model training, validation, and testing were conducted using a k-fold cross-validation approach to prevent overfitting; performance metrics included accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). Statistical analysis employed advanced regression analysis and ANOVA to evaluate the relationships between spectral features and catalyst performance metrics. The findings are expected to demonstrate that the AI models, particularly CNNs, can classify catalyst quality with over 95% accuracy, significantly reducing evaluation time from several hours to under 10 minutes per sample. The integration of spectral preprocessing techniques is anticipated to improve model robustness, especially in handling spectra affected by noise and instrument variability. The models are projected to outperform traditional chemometric methods, providing a reliable decision-support tool for industrial catalyst assessment. This research contributes novel insights into the application of AI techniques to spectroscopic data in an industrial context, bridging the gap between laboratory analytical methods and real-time process monitoring. The developed framework offers a scalable, cost-effective solution that can be integrated into existing quality control systems, thereby enabling more rapid decision-making and reducing downtime in chemical manufacturing operations. The study also advances theoretical understanding by elucidating the relationships between spectral signatures and catalyst properties within an AI interpretive paradigm grounded in the theory of machine learning and signal processing. In conclusion, the study underscores the potential of AI-driven spectroscopic analysis as a transformative approach for industrial catalyst evaluation, with recommendations for its implementation across diverse process environments. Future research should explore expanding the spectral modalities, incorporating unsupervised learning techniques for anomaly detection, and developing real-time feedback mechanisms to enhance adaptive control in catalytic processes. The findings provide a foundation for further innovations in integrating artificial intelligence with chemical spectroscopy to optimize industrial catalyst management and process efficiency.