Operator Variation Analysis for Steady-State Process Performance
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Solution Overview
Problem
Industrial operations face significant productivity and profitability losses due to variations in operator performance, with estimated annual losses of $80 billion across the process industry, as different operators make varying decisions and take different actions during steady-state and abnormal situations, leading to inefficiencies and inefficacies.
Innovation Solution
A system and method for providing operator variation analysis that processes input data from multiple sources to identify steady-state process data, clusters data for each distinct product or regime of operation, and identifies the best operator based on economic performance metrics, allowing for the measurement and characterization of gaps between operators, and proposing solutions to address these gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operator variation analysis is implemented to identify and address performance gaps, then productivity and profitability improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The system segments operator performance analysis by dividing the industrial operation into distinct regimes of operation and distinct products. Each regime is analyzed independently to identify operator variations specific to that context, making the complex analysis manageable and targeted rather than attempting to analyze all operations uniformly.
Solution Approach 2:
The patent introduces data clustering techniques as an intermediary mechanism between raw process data and operator performance evaluation. The clustering algorithm groups similar operational patterns together, serving as a mediator that transforms complex raw data into meaningful clusters that can be analyzed for operator variations without requiring direct complex analysis of all raw data.
2Measurement precision
If comprehensive data clustering and analysis are performed to identify best operators, then measurement precision of operator performance improves, but computational requirements and analysis time increase
Solution Approach 1:
The system applies segmentation by separating the analysis into distinct regimes of operation and distinct products. This division allows the clustering and analysis to be performed on smaller, more manageable subsets of data rather than attempting to analyze all operational data simultaneously, reducing overall processing time while maintaining precision within each segment.
Solution Approach 2:
The patent performs preliminary data processing steps including identifying steady-state process data and selecting relevant data types before applying clustering techniques. This preliminary organization and filtering of data prepares the dataset in advance, making the subsequent clustering and analysis more efficient and reducing the time required for the main analysis tasks.
3Loss of energy
If operator variations are measured and addressed through systematic analysis, then economic operation improves, but implementation cost and resource requirements increase
Solution Approach 1:
The system enables self-service by allowing the data to speak for itself through automated clustering and analysis. The patent uses unsupervised learning techniques that automatically identify patterns and operator variations without requiring extensive manual configuration or expert intervention, reducing implementation complexity while still capturing economic impacts of operator variations.
Solution Approach 2:
The patent creates a universal analysis framework that can be applied across different industrial operations, distinct regimes, and product types. The same clustering and analysis methodology works universally across various contexts, reducing implementation complexity by avoiding the need for custom solutions for each specific operational context while still capturing economy-specific variations.
Data Source
AI summary
Systems and methods for providing operator variation analysis for an industrial operation are disclosed herein. In one aspect of this disclosure, a method for providing operator variation analysis includes processing input data received from one or more data sources to identify steady state process data relating to the industrial operation and selecting one or more types of data in the steady state process data to cluster for operator variation analysis. The one or more types of data are clustered using one or more data clustering techniques, and the clustered one or more types of data are analyzed to identify a best operator of a plurality of operators responsible for managing the industrial operation. Information is analyzed to determine if one or more gaps exist in the economic operation of the industrial operation due to operator variability between the best operator and other operators.