AI Plant Control for Stable Separation Process Loops
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Solution Overview
Problem
Current industrial plant control methods for separation and treatment processes without chemical reactions lack the use of artificial intelligence and machine learning, which hampers productivity and revenue maximization, and do not effectively stabilize control loops to prevent unwanted shutdowns.
Innovation Solution
A method that employs artificial intelligence and machine learning to define objective functions for profit maximization, uses genetic algorithms and neural networks to model process behavior, and performs non-linear dynamic simulations to optimize control loops and prevent plant shutdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional control methods are used for industrial plants, then the control system is simple and easy to implement, but productivity and revenue are not maximized
Solution Approach 1:
The patent replaces conventional mechanical control systems with artificial intelligence and machine learning-based control systems. Specifically, neural networks are used to model process behavior and genetic algorithms optimize control parameters, substituting traditional control methodologies with intelligent systems that can adaptively maximize productivity and revenue while handling the complexity of multivariate industrial processes.
2Reliability
If conventional control methods are used for industrial plants, then the control system is simple to implement, but control loops are not effectively stabilized and unwanted shutdowns occur
Solution Approach 1:
The patent implements advanced feedback mechanisms using neural networks that continuously monitor and model process behavior. The system uses real-time data from control loops to dynamically adjust control actions, providing intelligent feedback that stabilizes loops and prevents unwanted shutdowns. The genetic algorithms also provide feedback by iteratively optimizing control parameters based on system performance.
3Productivity
If cost minimization objective function is used, then operational costs are reduced, but plant production and revenue are not maximized
Solution Approach 1:
The patent fundamentally changes the objective function parameter from cost minimization to profit maximization. The new objective function incorporates both revenue generation and cost considerations, allowing the control system to optimize for productive output while accounting for operational expenses. This parameter change enables the system to identify operating conditions that maximize overall profitability rather than merely minimizing costs.
Data Source
AI summary
The present invention refers to a method for controlling a plant of separation and treatment industrial processes without chemical reaction using artificial intelligence and machine learning, aiming at improving revenues and profits obtained, as well as the performance of the system, and the technique can be applied in steps of conceptual design for a unit in operation, comprising the steps of: defining objectives and gains of the plant; delimiting the plant; evaluation in steady state of the plant; evaluation in dynamic state of the plant; and performing non-linear dynamic simulation of the plant.