Abstract: A system and method for performing data-based optimization of performance indicators of process and manufacturing plants. The system consists of modules for collecting and merging data from industrial processing units, pre-processing the data to remove outliers and missingness. Further, the system generates customized outputs from data and identifies important variables that affect a given process performance indicator. The system also builds predictive models for key performance indicators comprising the important features and determines operating points for optimizing the key performance indicators with minimum user intervention. In particular, the system receives inputs from users on the key performance indicators to be optimized and notifies the users of outputs from various steps in the analysis that help the users to effectively manage the analysis and take appropriate operational decisions.

Abstract: A system and method for performing data-based optimization of performance indicators of process and manufacturing plants. The system consists of modules for collecting and merging data from industrial processing units, pre-processing the data to remove outliers and missingness. Further, the system generates customized outputs from data and identifies important variables that affect a given process performance indicator. The system also builds predictive models for key performance indicators comprising the important features and determines operating points for optimizing the key performance indicators with minimum user intervention. In particular, the system receives inputs from users on the key performance indicators to be optimized and notifies the users of outputs from various steps in the analysis that help the users to effectively manage the analysis and take appropriate operational decisions.

Abstract: This disclosure relates generally to data preprocessing, and more particularly to implementing data pre-processing through outlier analysis and multivariate imputation process. In one embodiment, the method includes performing iterations for processing integrated data associated with a manufacturing process. Each iteration comprises removing outliers from the integrated data using a multi-level outlier model to obtain a filtered data. The filtered data is categorized into multiple categories to identify missing data based on a frequency of occurrence of various parameters. Missing data is selectively imputed based on the multiple categories to obtain imputed data which is clustered into various data clusters based on a predefined criteria. After every iteration, it is determined whether the imputed data associated with a current iteration is clustered into the same data clusters as associated with a previous iteration.