Plant Risk Calculation System for Operational Optimization
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Solution Overview
Problem
Industrial plants face challenges in optimizing operations due to the inability to effectively combine dynamic and static data to predict and mitigate equipment failures and maintain efficiency, leading to resource downtime and reduced reliability.
Innovation Solution
A system comprising a risk calculation engine and decision support system that processes dynamic and static data to calculate risks and derive operational decisions, including mitigation actions, to optimize plant operations and increase efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic and static data are combined to predict future plant conditions, then plant reliability is improved, but device complexity increases
Solution Approach 1:
The system segments data processing by separating dynamic data inputs (real-time plant conditions) from static data inputs (historical and design data), processing them through dedicated modules before integration. This segmentation allows complex predictive analytics to be broken into manageable components, improving reliability while controlling complexity.
Solution Approach 2:
A decision support system acts as an intermediary between raw data and plant operations, synthesizing dynamic and static data to generate predictive insights. This intermediary layer manages the complexity of data integration while delivering simplified, actionable recommendations to operators, thereby improving plant reliability without overwhelming system complexity.
2Productivity
If continuous monitoring and updating of risk projections is implemented, then equipment utilization is improved, but loss of energy increases
Solution Approach 1:
The system implements periodic risk assessment cycles rather than truly continuous monitoring, updating risk projections at strategically determined intervals based on equipment criticality and data availability. This periodic approach maintains high equipment utilization while reducing the energy consumption associated with constant real-time processing.
Solution Approach 2:
The system dynamically adjusts monitoring parameters and update frequencies based on equipment risk levels, operational context, and resource availability. High-risk equipment receives more frequent monitoring while lower-risk equipment uses reduced monitoring schedules, optimizing equipment utilization while minimizing energy loss from excessive monitoring.
Data Source
AI summary
Embodiments of the present disclosure include systems and a method. In one embodiment, a system is provided. The system includes a risk calculation system configured to calculate a risk based on a static input and a dynamic input, and a decision support system configured to use the risk to derive a decision. The system also includes a plant control system configured to update operations of a plant based on the decision, wherein the decision predicts future plant conditions.