Quasi-equilibrium atmospheric modular thermodynamic system and method
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems for controlling internal atmospheric conditions in enclosed spaces are inefficient and costly, reacting slowly to external changes and lacking precision, leading to temperature and humidity fluctuations that can harm stored items and pose risks during disasters.
Innovation Solution
A controlled atmosphere system that maintains a quasi-equilibrium state by using a layered-cell structure with embedded energy-transfer tubing, sensors, and a computer processor to predict and adjust internal conditions based on external and future environmental factors, employing predictive analytics and machine learning to make incremental changes before conditions become unstable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reactive cooling and heating systems are used to control internal atmospheric conditions, then the system can respond to temperature changes, but the response is slow and imprecise, leading to condition fluctuations beyond predetermined ranges
Solution Approach 1:
The system performs preliminary actions by using predictive analytics and machine learning to forecast external environmental changes before they occur. The control system proactively adjusts atmospheric conditions in anticipation of upcoming changes, rather than reacting after changes have already affected the internal environment. This eliminates the time lag inherent in conventional reactive systems and maintains precision by acting before conditions drift from target ranges.
2Temperature
If insulation is added to walls and ceiling to thermally isolate stored items, then temperature control is improved, but random hot and cold areas form on surfaces, causing condensation that harms stored items
Solution Approach 1:
The system applies local quality by using multiple sensors positioned at different locations within the enclosed space to detect local temperature and humidity variations. The control system then applies localized adjustments through distributed heating/cooling elements and dehumidification systems targeted at specific areas experiencing condensation risks. This prevents the formation of harmful condensation in vulnerable zones while maintaining overall temperature stability, rather than applying uniform insulation that creates localized problems.
3Reliability
If conventional thermal isolation systems are used, then energy consumption is high due to reactive adjustments, but the system lacks the capability to predict and prevent condition swings
Solution Approach 1:
The system performs preliminary actions by forecasting external environmental changes using predictive analytics and machine learning algorithms. By anticipating upcoming temperature shifts, humidity changes, or pressure variations, the system proactively adjusts atmospheric conditions before external influences can cause instability. This prevents condition swings without requiring excessive reactive energy input, as the system is already positioned near target conditions when external changes occur.
Solution Approach 2:
The system implements continuous feedback through multiple sensors monitoring internal temperature, humidity, and pressure conditions. This feedback is fed into the machine learning model that refines predictions and adjusts control strategies in real-time. The closed-loop feedback mechanism ensures high reliability by detecting and correcting deviations from target conditions, while the predictive component reduces energy consumption by avoiding large corrective adjustments.
4Adaptability or versatility
If reactive control systems are implemented, then the system can maintain atmospheric conditions, but it responds only after changes have already occurred, lacking predictive capability
Solution Approach 1:
The system performs preliminary actions by using predictive analytics and machine learning to forecast external environmental changes before they occur. The control system proactively adjusts atmospheric conditions in anticipation of upcoming changes, rather than reacting after changes have already affected the internal environment. This eliminates the time lag inherent in conventional reactive systems and maintains precision by acting before conditions drift from target ranges.
Solution Approach 2:
The system achieves multi-functionality by integrating predictive analytics, machine learning algorithms, real-time sensing, and control actuation into a single unified platform. The machine learning model serves multiple purposes: forecasting external conditions, identifying patterns in sensor data, optimizing control strategies, and detecting anomalies. This universal approach provides predictive capability without proportionally increasing system complexity, as one core intelligence engine handles multiple control functions.
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 system effectively maintains internal atmospheric conditions within predetermined ranges, reducing energy consumption and costs while preventing damage to stored items and enhancing disaster response capabilities by anticipating and mitigating external influences.
Implementation Method 1
a heating/cooling unit connectable to the energy-transfer tubing embedded in the concrete of the first layer of the cell to control temperature of the first layer and thereby control the temperature of the atmosphere of the enclosed area inside the cell
Data Source
AI summary
Controlled internal atmosphere systems and methods are disclosed, including a system comprising a cell having a top, a bottom, and wall(s) extending between the top and bottom defining an enclosed area inside the cell; the top and wall comprising a first layer having embedded energy-transfer tubing, a sealant layer outside the first layer, an aerated substrate layer outside the sealant layer, and an impermeable layer outside the substrate layer; a heating/cooling unit connectable to the tubing to control the temperature of the first layer and thereby control the temperature of an atmosphere of the enclosed area inside the cell; sensor(s) within the enclosed area; and a computer configured to receive input from the sensor(s) indicative of the condition of the enclosed area atmosphere, to receive input regarding environmental conditions outside of the cell, and to control operation of the heating/cooling unit based on the received input and predicted effects.