Bioreactor Dual Cycle Control for Cultured Meat Growth
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Solution Overview
Problem
Current bioreactor technologies face challenges in achieving controlled and scalable cultivation of cell cultures for industrial biomass production, particularly in optimizing operational parameters for reliable and efficient growth stimulation, especially for cultured meat production, where traditional methods are inefficient and lack reproducibility.
Innovation Solution
A dual cycle-controlled optimization process in bioreactors that includes a cultivation optimization cycle to identify a biologically optimized treatment window and a treatment optimization cycle to adjust operational parameters, utilizing sensory devices and machine-learning or artificial-intelligence modules to automatically adjust parameters for optimal growth and treatment performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional bioreactor cultivation methods are used, then the process is simpler to operate, but the growth efficiency and reproducibility are insufficient
Solution Approach 1:
The control process is segmented into two independent but coupled optimization cycles: a cultivation optimization cycle that identifies biologically optimized treatment windows, and a treatment optimization cycle that adjusts operational parameters. This segmentation allows each cycle to focus on specific optimization tasks, improving growth efficiency while maintaining manageable complexity through modular control architecture.
Solution Approach 2:
The system dynamically adapts operational parameters based on real-time sensory data and machine learning analysis. The dual cycle-controlled optimization continuously adjusts cultivation and treatment parameters, transforming the static traditional bioreactor into a dynamic system that responds to changing biological conditions, thereby improving reproducibility and growth efficiency.
2Measurement precision
If manual parameter adjustment is used, then the system is easier to operate, but the measurement precision and control accuracy are insufficient
Solution Approach 1:
Sensory devices continuously measure operational parameters and biological responses, feeding this data back to the control system. Machine learning algorithms analyze this feedback to automatically adjust parameters with high precision, eliminating manual adjustment errors while the automated system manages complexity through intelligent decision-making algorithms.
Solution Approach 2:
The system performs self-optimization through machine learning models that automatically analyze sensory data and adjust operational parameters without human intervention. The dual cycle-controlled optimization process autonomously identifies treatment windows and adjusts parameters, allowing the system to serve itself while achieving high measurement precision and control accuracy.
3Reliability
If single-cycle optimization is used, then the control process is simpler, but the treatment performance and growth stimulation reliability are insufficient
Solution Approach 1:
Two optimization cycles are merged into a coupled control system where the cultivation optimization cycle identifies biologically optimized treatment windows and the treatment optimization cycle adjusts operational parameters. These cycles exchange information and work together, combining their capabilities to achieve reliable growth stimulation while managing complexity through coordinated interaction rather than isolated operation.
Solution Approach 2:
The cultivation optimization cycle performs preliminary action by identifying biologically optimized treatment windows before the treatment optimization cycle adjusts operational parameters. This sequential approach ensures that parameter adjustments are based on pre-determined biological optimization criteria, improving reliability while organizing complexity into a structured two-stage process.
Data Source
AI summary
The invention relates to an industrial bioreactor with a dual cycle-controlled optimization process providing an optimized cultivation process for cell cultures, cell components or metabolic products of the cells in a nutrient medium. The bioreactor comprises a reactor vessel providing controlled bioreactor conditions for the cultivation process, a control unit connected to sensory devices measuring sensory parameter values comprising at least measures related to the composition of the nutrient medium and/or concentration of the nutrient medium and/or oxygen and/or temperature and/or pH-value and/or sterility, and transmitting them to the control unit. The control unit controls and automatically optimizes the operational parameter of the cultivation process and operational parameters of a treatment process applied to the cells during the cultivation process by adjusting operational parameters of the bioreactor affecting the measured sensory parameter values.