Fermentation Foam Prediction Using Image-Based ML Control
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Solution Overview
Problem
Fermentation processes face challenges in monitoring and controlling foaming, which can lead to reduced productivity and contamination risks due to the introduction of antifoam agents, and existing methods lack efficient automated solutions for predicting and mitigating foaming issues.
Innovation Solution
The implementation of machine learning algorithms for detecting and predicting foaming levels using image data from bioreactors, allowing for automated adjustments in process conditions such as airflow, pressure, and agitation speed to prevent or mitigate foaming, thereby reducing the need for antifoam agents and minimizing contamination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If antifoam agents are introduced to control foaming, then foaming is suppressed, but contamination risk increases and productivity decreases
Solution Approach 1:
The machine learning model predicts foaming events before they occur by analyzing real-time sensor data from the fermentation process. This early prediction allows the system to take preventive action by adjusting operational parameters (agitation speed, aeration rate, pH) before foaming becomes severe, thereby avoiding the need to introduce antifoam agents and eliminating contamination risk
Solution Approach 2:
The patent replaces the chemical method of foaming control (antifoam agents) with a mechanical/control-based approach. The machine learning system continuously monitors process parameters and automatically adjusts operational conditions to prevent foaming, substituting chemical intervention with intelligent process control
2Object-affected harmful factors
If antifoam agents are used to mitigate foaming, then foaming is reduced, but the amount of substance used increases and cost rises
Solution Approach 1:
The system performs preliminary prediction of foaming events using machine learning algorithms that analyze real-time sensor data. By predicting foaming before it occurs, the system can prevent foaming through operational parameter adjustments, thereby eliminating or significantly reducing the need for antifoam agent consumption
3Object-affected harmful factors
If manual monitoring and control of foaming is performed, then flexibility is maintained, but productivity decreases due to labor requirements
Solution Approach 1:
The system implements self-service automation where the machine learning model continuously monitors process parameters, predicts foaming events, and automatically adjusts operational parameters without human intervention. This automated closed-loop control eliminates manual monitoring requirements while maintaining effective foaming control, thereby maximizing fermentation productivity
Solution Approach 2:
The system employs real-time feedback control by continuously monitoring sensor data from the fermentation process, comparing actual conditions with predicted conditions, and automatically adjusting operational parameters to prevent foaming. This automated feedback loop replaces manual monitoring and enables continuous optimal operation
4Object-affected harmful factors
If complex monitoring systems are implemented to predict foaming, then foaming control improves, but device complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it predicts foaming events, identifies optimal operational parameters, and guides process adjustments. By using a single multi-functional AI system rather than multiple specialized devices, the patent achieves accurate foaming prediction while minimizing system complexity
Solution Approach 2:
The patent replaces complex mechanical and chemical foaming control systems with an intelligent software-based machine learning model. This substitution reduces physical device complexity while maintaining or improving foaming prediction accuracy through data-driven insights
Data Source
AI summary
The present disclosure provides methods and systems for foam control. A method of foam control for a fermentation system comprises: obtaining image data from an imaging device located at the fermentation system; and processing said sensor data using a trained machine learning algorithm to generate an output that indicates presence of foam or level of foaming.