Multi-Component Defoamer Feed Control for Adaptive Foam Intervention
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Solution Overview
Problem
Conventional systems for controlling foam in industrial processes, such as the pulp and paper industry, require the addition of entire defoamer formulations even when only specific attributes need adjustment, leading to inefficiencies and excess chemical usage.
Innovation Solution
A computer-implemented method for proactive intervention in industrial processes involves generating signals from online sensors, predicting changes in target values for defoamer components using machine learning models, and automatically controlling actuators to adjust the defoamer composition dynamically, thereby optimizing process performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems add entire defoamer formulations to address all three attributes (entrained air reduction, surface foam control, and persistence), then all attributes are covered, but chemical usage becomes excessive and inefficient
Solution Approach 1:
The defoamer formulation is segmented into three separate components (silicone oil, surfactant, and carrier) that can be independently controlled and dosed. This segmentation allows the system to add only the specific component needed for the detected foam condition, rather than adding the entire formulation, thereby reducing chemical usage while maintaining effectiveness.
Solution Approach 2:
The system applies partial action by dosing only the required portion of the defoamer components based on real-time sensor feedback. When only one attribute needs adjustment (e.g., surface foam control), only the relevant component is added at the appropriate dose, avoiding the excess chemical usage of conventional full-formulation addition.
2Ease of operation
If a single defoamer formulation is used for all conditions, then the system is simple to operate, but it cannot adapt to changing whitewater properties due to upsets, raw material changes, or seasonal effects
Solution Approach 1:
The system transitions from static single-formulation dosing to dynamic multi-component control. Real-time sensors continuously monitor foam conditions and whitewater properties, and the control system dynamically adjusts the type and dose of defoamer components based on current process conditions, enabling adaptation to upsets, raw material changes, and seasonal effects while maintaining ease of operation through automation.
Solution Approach 2:
The system incorporates feedback loops where sensors monitor foam height, entrained air, and other process parameters, and this information is fed back to the control system which adjusts component dosing accordingly. This closed-loop control enables the system to adapt to changing conditions automatically while maintaining simple operation for the user.
3Adaptability or versatility
If manual monitoring and adjustment of defoamer components is performed, then flexibility in response to process changes is achieved, but labor requirements and response time increase
Solution Approach 1:
The system performs self-service by automatically monitoring foam conditions and adjusting defoamer component dosing without human intervention. Sensors continuously detect process changes, and the control system independently makes dosing adjustments, eliminating the need for manual monitoring while maintaining rapid response to process changes and reducing labor requirements.
Solution Approach 2:
The system replaces manual mechanical adjustment with automated electronic control. Sensors and actuators form an automated control loop that substitutes human operators, enabling continuous real-time monitoring and adjustment with faster response times and reduced labor while maintaining the flexibility to adapt to process changes.
4Stability of the object's composition
If all three defoamer components are added simultaneously in fixed ratios, then the formulation remains stable, but the system cannot optimize for specific foam conditions or reduce chemical usage
Solution Approach 1:
The defoamer is segmented into three separately controllable components with individual dosing pumps and control valves. This segmentation allows the system to maintain formulation stability by adding components in appropriate ratios when needed, while simultaneously enabling optimization for specific foam conditions and reduction of chemical usage by adding only the required components at the right doses.
Solution Approach 2:
The system dynamically changes dosing parameters (component selection, dose rate, timing) based on real-time sensor feedback and process conditions. This enables optimization of the defoamer composition for specific foam conditions while maintaining formulation stability through controlled addition, thereby improving process efficiency and reducing chemical usage.
Data Source
AI summary
Systems and methods are disclosed herein for proactive intervention in an industrial process involving a defoamer composition. Learning models are iteratively trained using historical input data sets comprising directly measured variables from the industrial process and correlations between combinations of the input data and respective process states. For a current input data set associated with the industrial process, and responsive to detected process attributes, an intervention event is determined with respect to at least a first defoamer component in a current composition. Models are utilized to predict, responsive to the intervention event, changes in target values within a specified range of target values for other components in the current composition and/or for directly measured variables as corresponding with a specified process state. Automatic control is performed for respective actuators to produce a new defoamer composition, and as needed to other actuators for the directly measured variables.