Microbiome Monitoring System for Industrial Process Control
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Solution Overview
Problem
Traditional monitoring and control methods in industrial settings have not effectively utilized microbial and genetic information to enhance or predict industrial operations, despite its potential for improving processes in agriculture, manufacturing, energy exploration, and other fields.
Innovation Solution
The use of microbiome information, including historic, real-time, and predictive data, is integrated with bioinformatics processing stages such as QIIME, barcode decoding, OTU picking, and phylogenetic tree construction to analyze and direct industrial operations, leveraging GPS data and other environmental parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional physical sensing methods are used for industrial monitoring, then measurement and control can be implemented, but microbial and genetic information cannot be utilized
Solution Approach 1:
The patent combines traditional physical sensing methods with microbiome analysis techniques into a unified monitoring system. The system integrates sensors for physical parameters (temperature, pressure, flow) with DNA sequencing capabilities and bioinformatics processing, allowing simultaneous collection and analysis of both physical and biological data from industrial processes
Solution Approach 2:
The monitoring system is designed to handle multiple types of data (physical measurements, chemical compositions, microbial communities, genetic sequences) through a single integrated platform. The system can analyze diverse industrial processes across different sectors (energy, agriculture, manufacturing, water treatment) using the same core technology stack, making it universally applicable
2Productivity
If microbiome information is integrated into industrial monitoring, then predictive insights and process optimization are enabled, but data processing complexity increases
Solution Approach 1:
The system performs preliminary bioinformatics processing of microbiome data in real-time, including quality filtering, sequence alignment, and taxonomic classification, before the data reaches the decision-making layer. This preliminary processing converts raw sequencing data into meaningful biological indicators that can be directly used for process optimization
Solution Approach 2:
The patent introduces an intermediary bioinformatics processing layer that translates complex microbiome data into simplified biological indicators and predictive metrics. This intermediary layer uses machine learning models and statistical analysis to convert raw sequence data into actionable insights about process health, contamination risks, and optimization opportunities
3Reliability
If real-time microbiome analysis is implemented, then predictive control is achieved, but analysis time and computational resources increase
Solution Approach 1:
The system implements partial real-time analysis by continuously monitoring key microbiome indicators and full taxonomic composition at different frequencies. Critical parameters that affect process safety and quality are analyzed in real-time, while less critical data is processed at lower frequencies, optimizing the balance between responsiveness and computational efficiency
Solution Approach 2:
The analysis system applies different levels of processing intensity to different data streams based on their importance. High-priority indicators such as pathogen detection and process contamination alerts receive immediate full-depth analysis, while routine microbiome composition data undergoes streamlined processing, allocating computational resources according to local needs
Data Source
AI summary
There are provided methods, systems and processes for the utilization of microbial and related genetic information for use in industrial settings, such as the exploration, determination, and recovery of natural resources, minerals, and energy sources, the monitoring and analysis of processes, activities, and materials transmission.