Probabilistic Agent Control for Industrial Process Uncertainty
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Solution Overview
Problem
Complex industrial equipment and dynamic processes pose challenges in identifying and remedying equipment and production problems due to sensor failures, calibration drift, and varying process parameters, leading to uncertainty in control actions that can risk process disruptions or failures.
Innovation Solution
An artificially intelligent model-based controller (AIMC) system that utilizes probabilistic modeling, including Bayesian Networks, to classify deviations, prioritize diagnoses, and initiate control actions, ensuring sufficient confidence in process state for automated control with acceptable risk levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional advanced control systems use defined models or processes to correct process variations, then control actions can be automated, but it becomes difficult to accurately determine causality of variations or disturbances leading to incorrect diagnosis and process risk
Solution Approach 1:
The patent introduces probabilistic agents as intermediary components between sensor data and control actions. These agents use Bayesian networks to probabilistically infer causality of disturbances, providing a bridge that maintains automated control while improving diagnostic accuracy through probabilistic reasoning rather than deterministic rules
Solution Approach 2:
The system changes from fixed deterministic control parameters to dynamic probabilistic parameters. By using probabilistic modeling where control decisions are based on probability distributions and confidence levels rather than fixed thresholds, the system can adapt to uncertain conditions while maintaining automation
2Measurement precision
If exhaustive logical or knowledge based rules are used to identify causes of disturbances, then diagnostic coverage is improved, but computational requirements interfere with performance of time sensitive process monitoring and control
Solution Approach 1:
The patent segments the diagnostic system into multiple specialized probabilistic agents, each responsible for specific types of disturbances or process units. This segmentation allows parallel processing of different diagnostic tasks, reducing overall computational time while maintaining comprehensive diagnostic coverage through the collective expertise of specialized agents
Solution Approach 2:
The system applies partial diagnostic action by focusing computational resources on the most probable causes of disturbances. Using Bayesian inference, the system identifies and investigates the most likely culprits first rather than exhaustively analyzing all possible causes, achieving sufficient diagnostic accuracy with reduced computational effort
3Speed
If pattern recognition is used to classify variations or disturbances, then processing speed is improved, but the system lacks true understanding of physical data leading to blind and risky control actions
Solution Approach 1:
The patent implements feedback loops where probabilistic agents continuously update their beliefs about disturbance causes based on new sensor data and previous inferences. This feedback mechanism allows the system to maintain fast processing through pattern recognition while progressively building confidence in diagnoses through iterative probabilistic reasoning that incorporates physical understanding
Data Source
AI summary
A system and method for controlling a process such as an oil production process is (2) disclosed. The system comprises multiple intelligent agents for processing data (3) received from a plurality sensors deployed in a job site of an oil well, and applies a (4) probabilistic model for evaluating risk and recommending appropriate control action to the process.