Gas amount estimation apparatus, gas processing apparatus, transportation container, gas amount estimation method, and program
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Solution Overview
Problem
The challenge is to optimize the injection amount of Controlled Atmosphere (CA) gas during transportation of perishable products to maintain freshness, as insufficient CA gas affects product freshness and excessive gas usage leads to larger gas processing apparatuses and reduced product capacity.
Innovation Solution
A gas amount estimation apparatus that uses a control unit to set input data related to perishable products and estimate the supply or processing amount of CA gas over a predetermined time, employing machine learning or table data to optimize CA gas injection, considering factors like temperature, humidity, and transportation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If relatively large amounts of CA gas are injected to maintain freshness, then the freshness of perishable products is maintained, but the gas processing apparatus becomes large and the amount of perishable products that can be transported becomes small
Solution Approach 1:
The patent applies parameter changes by using machine learning to dynamically determine the optimal CA gas injection amount based on various parameters such as product type, transportation time, temperature, and humidity. This replaces the conventional approach of using fixed large amounts of gas, allowing the system to maintain freshness (reliability) while optimizing the gas amount to maximize product capacity.
2Volume of moving object
If insufficient CA gas is injected to reduce apparatus size, then the gas processing apparatus becomes smaller and more product can be transported, but the freshness of perishable products cannot be maintained
Solution Approach 1:
The patent implements feedback mechanisms by using machine learning models that learn from historical data about CA gas consumption patterns, product respiration rates, and environmental conditions during transportation. The system continuously adjusts the gas injection amount based on feedback from sensors monitoring temperature, humidity, and gas composition, ensuring freshness is maintained while optimizing product capacity.
3Quantity of substance
If machine learning is used to estimate CA gas supply amount, then the injection amount can be optimized, but the device complexity increases
Solution Approach 1:
The patent replaces complex mechanical gas control systems with software-based machine learning algorithms and data processing. Instead of using complex hardware mechanisms to control gas flow, the system uses computational models that process input parameters (product type, transportation time, environmental conditions) to determine the optimal gas amount, reducing mechanical complexity while improving optimization capability.
Data Source
AI summary
An object of the present disclosure is to optimize the injection amount of the CA gas to be injected into a transportation means such as a truck, to maintain the freshness of perishable products during transportation above a predetermined level.Accordingly, the present disclosure discloses a gas amount estimation apparatus including a control unit, wherein the control unit is configured to set, as input data, information relating to a type and an amount of a perishable product stored in a CA refrigerator, and estimate a supply amount or a processing amount of CA gas with respect to the CA refrigerator in a predetermined time and set the estimated supply amount or the estimated processing amount as output data. Thus, the freshness of the perishable products during transportation can be maintained above a predetermined level.