ASOP Metrics for Industrial Process Situational Awareness
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Solution Overview
Problem
Modern industrial facilities face challenges in obtaining comprehensive situational awareness due to the vast amount of operational data generated, which often remains unanalyzed and leads to sub-optimized processes and potential safety incidents.
Innovation Solution
The development of systems and methods to collect and analyze data for determining Automation, Safety, and Operations Performance (ASOP) metrics, including Alarm System Performance Metric, Operator Loading Metric, Controllability Performance Metric, Proximity to Safety System Metric, Demand on Safety Systems Metric, and Control System Integrity Metric, which are normalized and displayed to provide a holistic view of industrial process performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive operational data is collected from industrial facilities, then situational awareness is improved, but data complexity and analysis difficulty increase
Solution Approach 1:
The patent segments the complex operational data into distinct performance categories (safety performance, reliability performance, efficiency performance). Each category is measured using specific metrics that break down the overwhelming data into manageable, analyzable components. This segmentation allows operators to systematically assess different aspects of facility performance without being overwhelmed by the total data volume.
Solution Approach 2:
The patent introduces performance metrics as intermediary elements between raw operational data and situational awareness. These metrics serve as mediators that transform complex, unstructured operational data into standardized, comparable performance indicators. The metrics act as an intermediate layer that simplifies data interpretation while preserving the essential information needed for comprehensive situational awareness.
2Productivity
If real-time performance metrics are calculated and displayed, then operational optimization is improved, but computational resource requirements increase
Solution Approach 1:
The patent applies local quality by calculating performance metrics at specific operational levels and locations within the facility rather than processing all data centrally. Each process unit or control system calculates its own performance metrics using local data, reducing the need for centralized computational resources. This distributed approach enables real-time optimization while minimizing overall computational energy consumption.
3Reliability
If multiple performance metrics are monitored simultaneously, then comprehensive assessment is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple performance metrics into three integrated performance categories (safety, reliability, efficiency). Rather than monitoring numerous separate metrics independently, the system combines related metrics into unified categories that provide comprehensive assessment while reducing system complexity. This merging approach maintains assessment completeness by capturing all critical performance aspects within a structured framework.
Data Source
AI summary
In accordance with various embodiments described herein are systems and methods for collecting and analyzing parameter and properties data from an operation and using the data to determining Automation Safety and Operations Performance (ASOP) metrics indicative of various performance aspects of a given operation. Some embodiments are systems and methods for determining one or more ASOP metrics or metrics selected from a group consisting of elements including but not limited to Alarm System Performance Metric, an Operator Loading Metric, a Controllability Performance Metric, a Proximity to Safety System Metric, a Demand on Safety Systems Metric, and a Control System Integrity Metric.