Microbial Growth Modeling With Genetic Sensing for Production Control
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Solution Overview
Problem
Existing microbial monitoring systems in production environments face limitations in speed, accuracy, and integration with predictive analytics, leading to inefficient cleaning schedules, product waste, and increased costs due to inaccurate microbial detection and control.
Innovation Solution
A computing system utilizing rapid genetic sequencing and machine learning models to identify and forecast microbial growth, integrating with production system data for real-time control and scheduling to optimize operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional microbial monitoring methods are used, then system complexity is reduced, but measurement precision and speed deteriorate
Solution Approach 1:
The patent introduces genetic sequencing as an intermediary between sample collection and microbial identification. Instead of direct cultural methods, the system sequences genetic material (DNA/RNA) from environmental samples, using molecular biology techniques to bridge the gap between physical sampling and accurate microbial detection, thereby achieving high precision without proportionally increasing system complexity
Solution Approach 2:
The patent replaces traditional mechanical/cultural methods (petri dishes, incubation, visual inspection) with computational and molecular methods (genetic sequencing, bioinformatics analysis, machine learning). This substitution transitions from labor-intensive mechanical processes to automated analytical systems, improving measurement precision while the automation actually reduces operational complexity
2Reliability
If frequent cleaning is performed to prevent microbial growth, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The patent implements preliminary microbial detection through genetic sequencing of environmental samples before contamination becomes problematic. By detecting microbial presence early in the production process, the system can take preventive actions (targeted cleaning, process adjustments) before microbial loads reach critical levels, thereby maintaining reliability while minimizing production interruptions
Solution Approach 2:
The patent establishes a feedback loop where continuous microbial monitoring through genetic sequencing provides real-time data on microbial populations. This feedback enables dynamic adjustment of cleaning schedules and operational parameters, allowing the system to maintain high reliability by responding to actual microbial conditions rather than following fixed cleaning schedules, thus preserving productivity
3Measurement precision
If rapid genetic sequencing is implemented, then measurement precision is improved, but use of energy and cost increase
Solution Approach 1:
The patent extracts only the essential genetic information needed for microbial identification rather than performing complete genomic sequencing. By targeting specific genetic markers or using metagenomic approaches that focus on relevant taxa, the system achieves high identification accuracy while significantly reducing sequencing depth requirements, thereby lowering energy consumption and costs
Solution Approach 2:
The patent employs parameter changes in the sequencing process, such as adjusting read depth, targeting specific gene regions (16S rRNA, ITS), or using amplicon-based approaches instead of whole-genome sequencing. These parameter modifications maintain sufficient measurement precision for microbial identification while dramatically reducing the computational and energy resources required, making the system more cost-effective
Data Source
AI summary
Disclosed are systems and methods for microbial sensing and predictive growth modeling. A system can include one or more processors, coupled with memory, to receive genetic information of a microbe in a production system, the genetic information sequenced from a sample taken from the production system. The one or more processors can execute at least one model trained by machine learning using the genetic information to identify the microbe or determine a characteristic of the microbe. The one or more processors can update operation of the production system using the identity of the microbe or the characteristic of the microbe.