The application of PCA in catalysis typically involves the following steps:
1. Data Collection: Gather high-dimensional data from catalytic experiments, including parameters such as temperature, pressure, reactant concentrations, catalyst compositions, and reaction rates. 2. Standardization: Standardize the data to have a mean of zero and a standard deviation of one to ensure that each variable contributes equally to the analysis. 3. Covariance Matrix Calculation: Compute the covariance matrix to understand the relationships between different variables. 4. Eigenvalue and Eigenvector Computation: Calculate the eigenvalues and eigenvectors of the covariance matrix to identify the principal components. 5. Principal Component Selection: Select the principal components that explain the most variance in the data, typically the first few components. 6. Transformation: Transform the original data into the new set of principal components for further analysis.
