Advisory System for Industrial Plant Loss Computation
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Solution Overview
Problem
Industrial plants, such as power generation plants, face performance losses due to aging, degradation, and unplanned maintenance events, leading to inefficiencies and increased operational costs, which existing systems fail to adequately address.
Innovation Solution
A system comprising an advisory system with a loss computation engine, cost model, and control strategy system that uses sensor data and predictive models to quantify losses, derive cost estimates, and implement control corrections to optimize plant operations, including automatic outage and maintenance scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional control systems are used without loss computation and predictive modeling, then device complexity is reduced, but plant performance and efficiency deteriorate due to unaddressed losses from aging and degradation
Solution Approach 1:
The control system is segmented into distinct functional modules: sensor data acquisition module, predictive model module, loss computation engine, cost model module, and control strategy module. Each module performs a specific function, allowing the complex system to be managed through modular components that can be independently developed, tested, and maintained while collectively improving plant performance.
Solution Approach 2:
The system performs preliminary actions by continuously computing predicted losses and cost estimates before making control decisions. The loss computation engine predicts future losses based on current sensor data and predictive models, allowing the control strategy to proactively adjust operations to minimize anticipated losses rather than reacting to actual degradation after it occurs.
2Loss of energy
If real-time sensor data and predictive models are integrated to compute losses and costs, then operational cost optimization is improved, but measurement precision requirements increase
Solution Approach 1:
The predictive model acts as an intermediary between raw sensor measurements and loss computation. Instead of directly using potentially noisy sensor data to calculate losses, the system first processes sensor readings through predictive models that estimate component degradation and loss factors. This intermediary processing step filters and contextualizes measurement data, reducing the impact of measurement imprecision while still enabling accurate loss estimation.
Solution Approach 2:
The system implements feedback mechanisms where loss computation results and cost estimates are fed back to adjust control strategies, which in turn affect operational parameters and sensor readings. This closed-loop feedback allows the system to continuously refine measurements and computations, improving the accuracy of loss assessment over time while adapting to changing plant conditions.
3Ease of operation
If comprehensive loss computation and cost modeling are implemented, then economic insight and control optimization are improved, but device complexity and computational requirements increase
Solution Approach 1:
The control system is designed with multi-functional modules that serve multiple purposes. The predictive model not only forecasts component degradation but also provides data for loss computation, cost estimation, and control strategy optimization. The loss computation engine simultaneously evaluates multiple loss factors (aging, degradation, operational conditions) and integrates them into a comprehensive loss metric. This multi-functionality reduces overall system complexity by eliminating redundant computational components while enhancing control optimization capabilities.
4Reliability
If continuous monitoring and adaptive control strategies are applied, then plant effectiveness is improved, but loss of time for data processing and computation increases
Solution Approach 1:
The system applies partial monitoring and computation actions based on priority and current plant conditions. Rather than continuously processing all sensor data at full computational intensity, the system selectively focuses computational resources on critical parameters and components with highest impact on plant effectiveness. The control strategy adjusts the level of monitoring and computation based on operational urgency, performing comprehensive analysis only when necessary while maintaining baseline monitoring continuously.
Data Source
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AI summary
Systems and methods provided herein. In one embodiment, a system includes an advisory system 76 including a loss computation engine 112 configured to derive a total system loss for an industrial plant based on a first sensor positioned in a first industrial plant component and on a first physical model of the first industrial plant component. The advisory system 76 further includes a cost model 116 configured to use a cost function to derive a cost based on the total system loss, and a control strategy system 118 configured to derive an advisory report, a control correction factor, or a combination thereof, based on the cost, wherein a control system 120 is configured to apply the control correction factor to control a process in the industrial plant.