Sectional Grain Drying Control for Uniform Bulk Storage Conditioning
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Solution Overview
Problem
Existing grain-drying systems in bulk containers suffer from uneven drying and inconsistent moisture and temperature conditions due to the physical packing of grains and conventional heating methods, leading to issues like spoilage and stress cracking, and lack advanced analytical models for real-time section-by-section control.
Innovation Solution
A post-harvest crop management platform that uses sensors and machine learning to analyze crop conditions within containers, model fluid flow patterns, and actuate multi-stack assemblies to deliver fluids section-by-section, ensuring uniform drying and conditioning through autonomous crop drying, conditioning, and storage management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ambient air is pushed from the bottom plenum through the entire crop, then the drying process can be performed, but uneven drying and inconsistent moisture conditions occur across different sections
Solution Approach 1:
The container is divided into multiple sections with independent fluid delivery. Each section has its own fluid source and delivery mechanism, allowing separate control of drying conditions for each section based on local crop density and moisture requirements
Solution Approach 2:
Different sections of the crop receive different fluid delivery rates and conditions tailored to their specific needs. Perimeter sections with lower density receive different treatment compared to central sections with higher density, achieving uniform drying across all areas
2Productivity
If heat is applied from the bottom plenum area, then drying can be achieved, but heat damage and stress cracking occur at the bottom near the heat source
Solution Approach 1:
Heat application is segmented into multiple independent sources distributed throughout the container rather than concentrated at the bottom plenum. Each section has its own heat source that can be independently controlled to prevent localized overheating
Solution Approach 2:
The heat source distribution transitions from a single bottom-level source to multiple distributed sources at various heights and locations within the container, changing the spatial dimension of heat application to eliminate hot spots
3Device complexity
If air delivery is concentrated at the bottom plenum, then the system structure is simple, but air penetration ability decreases for sections further from the source
Solution Approach 1:
The air delivery system is segmented into multiple independent delivery points distributed throughout the container. Each segment can deliver air directly to its local section, ensuring effective penetration regardless of distance from a central source
Solution Approach 2:
Multiple intermediate fluid sources are introduced throughout the container to act as mediators between the external air supply and the crop sections. These intermediaries ensure adequate air delivery to all areas without requiring complex long-distance ducting
4Productivity
If conventional bulk drying systems are used, then large-scale processing is achieved, but crop quality deteriorates due to spoilage and uneven conditions
Solution Approach 1:
The large-scale container is divided into multiple independently controllable sections, each with its own sensors and fluid delivery system. This allows uniform quality control across the entire bulk crop while maintaining large-scale processing capability
Solution Approach 2:
Sensors are placed in each section to monitor moisture content, temperature, and other crop conditions in real-time. This feedback is used to automatically adjust fluid delivery rates in each section to maintain consistent quality throughout the bulk crop
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 achieves uniform drying and conditioning across all sections of the container, minimizing losses by addressing uneven drying and temperature issues, and predicting future crop conditions to maintain quality until unloading.
Implementation Method 1
sampling conditions of the crop to identify parameters relative to a desired crop characteristic level
Implementation Method 2
modeling an application of a fluid flow pattern to the crop across each section of the container
Implementation Method 3
whether they be heated air, ambient air, gases, liquids, or any other treatment applied to crops
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.