Sectional Grain Drying Control for Uniform Moisture in Storage
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Solution Overview
Problem
Existing grain drying systems in large-scale containers face issues with uneven drying and inconsistent moisture and temperature conditions due to the physical packing of grains and conventional heating methods, leading to potential spoilage, stress cracking, and inefficiencies, with no existing systems addressing these issues on a section-by-section basis or using advanced analytical models with machine learning and AI for real-time adjustments.
Innovation Solution
A post-harvest crop management platform that uses sensors and machine learning to analyze moisture content and temperature across different sections of a container, models fluid flow patterns, and actuates a multi-stack assembly to deliver fluids section-by-section, applying ambient air, heat, or gases to achieve desired crop characteristics, integrating with autonomous field operations and other precision agriculture systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ambient air is pushed from the bottom plenum to dry the crop, then the drying process can be initiated, but uneven drying and inconsistent moisture/temperature conditions occur across different sections
Solution Approach 1:
The container is divided into multiple sections with independent fluid delivery control. Each section has its own fluid delivery device that can be independently actuated based on local moisture and temperature conditions, allowing precise control of drying uniformity across different regions of the crop.
Solution Approach 2:
The system applies different fluid delivery characteristics to different sections based on their specific conditions. Sensors monitor local moisture and temperature, and the control system adjusts fluid delivery parameters (flow rate, temperature, timing) for each section to achieve uniform drying throughout the entire crop mass.
2Temperature
If heat is applied from the bottom plenum area, then drying can proceed, but heat damage and stress cracking occur at the bottom while top and center remain under-dried
Solution Approach 1:
The heating function is segmented into multiple independent fluid delivery devices distributed throughout the container. Each device can apply heat locally based on real-time sensor feedback from its specific zone, preventing overheating in any single area while ensuring adequate heating throughout the crop mass.
Solution Approach 2:
Instead of applying heat from the bottom up (conventional approach), the system can apply heat from multiple locations including top, sides, and center sections. This inverted approach to heat application direction prevents bottom heat damage while still achieving effective drying of the entire crop.
3Device complexity
If air delivery is concentrated at the bottom plenum, then the system structure is simple, but air penetration ability decreases with distance from the source
Solution Approach 1:
The air delivery system is segmented into multiple distributed fluid delivery devices rather than a single bottom plenum source. This segmentation improves air penetration by placing delivery points throughout the crop mass, ensuring reliable airflow reaches all sections regardless of distance from any single source.
Solution Approach 2:
The system transitions from one-dimensional bottom-up air delivery to three-dimensional distributed delivery throughout the container volume. Fluid delivery devices are positioned at various heights and locations, creating airflow paths in multiple dimensions that significantly improve penetration and distribution uniformity.
4Ease of operation
If conventional bottom-up drying is used, then the system is easy to operate, but spoilage and crop damage occur due to under-drying in top and center sections
Solution Approach 1:
The system incorporates sensors that automatically monitor local moisture and temperature conditions in each section. The control system uses this feedback to autonomously adjust fluid delivery parameters, eliminating the need for manual intervention while ensuring consistent crop quality and preventing spoilage through real-time condition-based control.
Solution Approach 2:
The system implements closed-loop feedback control where sensors continuously monitor crop conditions and the control system adjusts fluid delivery based on this feedback. This ensures reliable prevention of under-drying and spoilage while maintaining ease of operation through automated control that responds to actual crop needs.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables precise, efficient, and autonomous crop drying and storage management, minimizing losses by ensuring consistent quality and value of stored crops until unloading, by addressing uneven drying and temperature issues on a section-by-section basis with real-time data analysis and predictive adjustments.
Implementation Method 1
sampling conditions of the multiple sections of the crop to identify parameters relative to a desired crop characteristic level
Implementation Method 2
generating a profile of the selected crop characteristic across the multiple sections of the crop
Implementation Method 3
delivering a fluid to the crop in a fluid flow pattern that achieves the desired crop characteristic level
Implementation Method 4
modeling a fluid flow pattern for delivery of a fluid to the crop by actuating the multi-stack assembly in a manner that achieves the desired crop characteristic level
Implementation Method 5
dries, aerates, conditions, transfers, and manages the quality of the crop by efficiently applying ambient air, supplemental heat, gas, or other fluid
Data Source
AI summary
A post-harvest crop management platform is provided for regulating conditions of an agricultural crop being dried and/or stored. The platform utilizes data collected from sensors positioned proximate to, or embedded within, an agricultural crop, and analyzes selected crop characteristics affecting the stored crop in multiple sections thereof. The platform identifies parameters relative to achieving a desired crop characteristic level in the agricultural crop, generates a profile of the selected crop characteristic across the multiple sections of the stored crop, and models an application of a fluid flow pattern to achieve the desired crop characteristic level in each section. The crop storage monitoring and management platform also actuates a multi-stack assembly, configured within the stored crop, to automatically apply the fluid flow pattern in one or more cycles that are adjustable to changing conditions within the stored crop in real time. The crop storage monitoring and management platform further integrates with, and connects to and communicates with, other systems within an autonomous field activity ecosystem.