Related Experiment Video
Updated: Aug 5, 2026
07:18
Experimental System of Solar Adsorption Refrigeration with Concentrated Collector
Published on: October 18, 2017
Bio-Inspired Explainable Evolutionary Rule Mining for Thermodynamic Performance Assessment of a Solar Greenhouse
Mehmet Das1, Ebru Akpinar1, Ferdi Dogan2
1Department of Mechanical Engineering, Faculty of Engineering, Firat University, Elazig 23119, Türkiye.
Biomimetics (Basel, Switzerland)
|July 27, 2026
Summary
This study optimized a greenhouse dryer with a solar collector using AI. It found that solar radiation, temperature, and humidity significantly impact drying efficiency, providing clear operating rules.
Area of Science:
- Agricultural Engineering
- Renewable Energy Systems
- Artificial Intelligence in Engineering
Background:
- Greenhouse dryers offer potential for efficient agricultural product dehydration.
- Integrating solar collectors enhances dryer performance but requires optimized operation.
- Explainable AI (XAI) can provide interpretable insights into complex system dynamics.
Purpose of the Study:
- To evaluate the thermodynamic and drying performance of a greenhouse dryer integrated with a parabolic trough solar collector (PTSC).
- To develop interpretable operating rules for the solar greenhouse dryer using a bio-inspired explainable artificial intelligence framework.
- To identify key environmental and operational factors influencing dryer efficiency.
Main Methods:
- Conducted outdoor apple-drying experiments to collect performance data.
- Calculated energy, drying, and exergy efficiencies.
- Employed the Chaotic Rule-based Strength Pareto Evolutionary Algorithm 2 (CRb-SPEA2), a bio-inspired XAI method, to classify efficiencies and extract decision rules.
Main Results:
- Energy efficiency ranged from 17.7% to 29.2%, drying efficiency from 1.0% to 9.7%, and exergy efficiency from 5.6% to 8.4%.
- CRb-SPEA2 achieved high classification recall (up to 1.000) for different efficiency levels.
- Extracted rules identified solar radiation, temperature, relative humidity, and product weight as critical factors affecting dryer performance.
Conclusions:
- The developed XAI framework successfully generated interpretable rules for optimizing solar greenhouse dryer operation.
- The findings provide practical guidance for enhancing the efficiency of solar drying systems.
- Solar radiation, temperature, humidity, and product weight are key determinants of dryer performance, enabling data-driven operational adjustments.
Related Concept Videos
01:27Thermal expansion and Thermal stress: Problem Solving
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in temperature (ΔT) is 55 °C.
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in temperature (ΔT) is 55 °C.
01:21First Law Of Thermodynamics: Problem-Solving
The first law of thermodynamics states that the change in internal energy of the system is equal to the net heat transfer into the system minus the net work done by the system. This equation is a generalized form of energy conservation and can be applied to any thermodynamic process.
The following strategies can be used to solve any problem involving the first law of thermodynamics.
The following strategies can be used to solve any problem involving the first law of thermodynamics.
02:49Second Law of Thermodynamics
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
00:53Second Law of Thermodynamics
The Second Law of Thermodynamics states that entropy, or the amount of disorder in a system, increases each time energy is transferred or transformed. Each energy transfer results in a certain amount of energy that is lost—usually in the form of heat—that increases the disorder of the surroundings. This can also be demonstrated in a classic food web. Herbivores harvest chemical energy from plants and release heat and carbon dioxide into the environment. Carnivores harvest the chemical energy...
01:19Mechanism of heat transfer
Understanding heat transfer mechanisms is essential for understanding how our bodies maintain balance in different environmental conditions. When the environment is thermoneutral, the body is in a state of balance, neither using nor releasing energy to maintain its core temperature. However, when the environment is not thermoneutral, the body employs four heat transfer mechanisms to maintain homeostasis: conduction, convection, evaporation, and radiation. These mechanisms facilitate heat...
01:17First Law of Thermodynamics
A change in the internal energy of a system depends on the the net heat transfer into the system and the net work done by the system. The first law of thermodynamics, which is a generalized form of energy conservation, relates these three quantities mathematically. It states that the change in the internal energy equals the difference between the heat transfer and work done by the system.
The applied heat increases the internal energy of a system. Hence, conventionally heat is considered...
The applied heat increases the internal energy of a system. Hence, conventionally heat is considered...