Industrial processes involve hundreds of correlated variables interacting simultaneously. Traditional univariate monitoring misses the compl...
Industrial processes involve hundreds of correlated variables interacting simultaneously. Traditional univariate monitoring misses the compl...
Overview
Industrial processes involve hundreds of correlated variables interacting simultaneously. Traditional univariate monitoring misses the complex relationships that drive quality, efficiency, and yield. AhilyaSoft's Multivariate Analysis platform applies principal component analysis (PCA), partial least squares (PLS), and advanced machine learning techniques to model, monitor, and optimize your process: identifying the real underlying sources of variation and enabling proactive intervention before problems manifest in final product quality.
Methodology
Collect and clean process data from historian, synchronize batch and continuous data streams, and identify the variable set that influences your target quality/performance metrics.
Build PCA/PLS/ML models that capture the multivariate relationships between process variables and quality/performance outcomes: identifying the true drivers of variation.
Deploy models for real-time multivariate process monitoring: detecting subtle shifts from optimal operation before they impact final product quality or efficiency.
When deviations are detected, contribution plots and variable importance analysis pinpoint which process variables are driving the deviation for targeted corrective action.
What You Gain
Measurable results within weeks, not quarters.
Identify hidden correlations and root causes invisible to univariate analysis
Early detection of process shifts before they impact quality or yield
Reduce off-spec production through proactive multivariate quality monitoring
Operator assist: real-time guidance on which variables to adjust and by how much
Golden batch/run analysis for identifying and replicating best operating practices
Deployed across industries where milliseconds and margins matter.
Applications
Refinery: multivariate monitoring across CDU, FCC, and hydroprocessing for yield optimization
Sugar: Brix, POL, and purity prediction from upstream process variables
Cement: raw mix quality prediction and kiln performance optimization
Pharmaceutical: batch process monitoring and golden batch analysis
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