Material Separation Control via Dynamic State Estimation
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Solution Overview
Problem
Industrial material separation processes, such as flotation, are difficult to control due to their multivariable and non-linear nature, often operating far from optimal conditions and requiring expensive X-ray refractometry measurements, which limits control precision.
Innovation Solution
A method and system that measure process output variables to estimate the state of the separation process, optimize an objective function to maximize desired material recovery or minimize additives and energy use, using a dynamic or static model to adjust input variables and set points for efficient separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If X-ray refractometry measurements are used to control the separation process, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces expensive X-ray refractometry equipment with alternative measurement methods that copy or simulate the functionality of the original measurement system. This allows achieving comparable measurement precision through less complex and more cost-effective means, directly resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent employs simpler, potentially disposable or easily replaceable measurement solutions instead of expensive, complex X-ray refractometry equipment. This approach reduces device complexity and cost while maintaining adequate measurement precision for process control
2Device complexity
If the number of measurement points is reduced to minimize cost, then device complexity is reduced, but control precision deteriorates
Solution Approach 1:
The patent introduces a dynamic model as an intermediary between the reduced measurement points and the process control. This model compensates for the reduced number of measurements by predicting process behavior and filling information gaps, thereby maintaining control precision despite using fewer measurement points and simpler equipment
Solution Approach 2:
The patent changes the parameters of the measurement system by using alternative, simpler measurement methods with different characteristics. Combined with the dynamic model, this allows achieving adequate control precision with fewer measurement points, resolving the contradiction between device complexity and control precision
3Ease of operation
If conventional control methods are used to control concentrate grade and tailings, then ease of operation is maintained, but productivity is reduced due to operation far from optimal conditions
Solution Approach 1:
The patent implements a feedback control system that continuously monitors process variables and adjusts operating parameters to maintain optimal conditions. The dynamic model provides predictions that feed into the control loop, enabling the system to automatically operate at or near optimal points while maintaining ease of operation through automated adjustment
Solution Approach 2:
The patent transitions from static control setpoints to dynamic, time-varying optimal trajectories. The dynamic model enables the control system to adapt to changing process conditions and continuously optimize performance, thereby improving productivity while maintaining ease of operation through automated dynamic adjustment
4Productivity
If dynamic optimization control is implemented to operate at optimal conditions, then productivity is improved, but device complexity increases due to advanced control systems
Solution Approach 1:
The patent uses a dynamic model as an intermediary that bridges the gap between simple measurements and complex optimization goals. This model enables productivity improvement through optimal control while keeping the actual control system relatively simple by offloading the computational complexity to the model rather than requiring complex hardware or software implementation
Solution Approach 2:
The patent replaces complex mechanical or hardware-based control systems with a software-based dynamic model and computational optimization approach. This substitution achieves optimal control and improved productivity while reducing physical device complexity by using information processing rather than mechanical complexity
Data Source
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AI summary
The invention concerns a method, device and computer program product for controlling a material separation process as well as to a material separating system. The material separating system (10) comprises units (16, 26) separating desired material from undesired material, units (24, 34) measuring process output variables in the material separation process indicative of the degree of separation between desired and undesired material, a unit (38) estimating the state of the process by applying the measured output variables and external constraints for a prediction time interval on a model of the material separation process, a unit (40) optimising an objective function through maximising the recovery of the desired material in the separation process, which optimising provides at least one set point value for each input variable of the model, and at least one regulating unit (22, 32) regulating the separation process by using the set point value.