Improving Drying Efficiency Of Direct Type Solar Dryer Using Gen Ai

Abstract

Solar Drying Is A Sustainable, Eco-friendly, And Cost-effective Method Used For Preserving Agricultural And Food Products By Utilizing Renewable Solar Energy As An Alternative To Conventional Fuel-based Drying Methods. However, Traditional Direct Type Solar Dryers Are Generally Fixed In Position And Unable To Follow The Movement Of The Sun, Which Results In Reduced Heat Absorption, Uneven Moisture Removal, Longer Drying Time, And Fluctuations In Chamber Temperature That May Affect The Quality Of Dried Products. To Overcome These Limitations, This Project Proposes An Improved Direct Type Solar Dryer Integrated With Automatic Sun Tracking And Generative AI Based Performance Optimization. The System Uses LDR (Light Dependent Resistor) Sensors To Detect The Direction Of Maximum Sunlight Intensity And An Arduino Uno Controller To Process Sensor Data And Operate An SG90 Servo Motor For Rotating The Dryer Towards The Sun Throughout The Day. By Maintaining Maximum Solar Exposure, The Dryer Is Capable Of Achieving Higher Internal Temperature, Faster Moisture Evaporation, And Improved Drying Efficiency. In Addition, Generative AI Techniques Are Utilized For Conceptual Design Enhancement, Drying Parameter Prediction, And Optimization Of System Performance. Phase 1 Of The Project Mainly Focuses On Design Analysis, Component Selection, Control Logic Development, And Expected Performance Evaluation Before Prototype Fabrication. The Proposed System Is Expected To Reduce Drying Time, Improve Product Quality, Minimize Manual Intervention, And Increase Energy Utilization Efficiency When Compared With Conventional Fixed Solar Dryers. This Low-cost And Practical Solution Is Highly Suitable For Small-scale Agricultural And Food Processing Applications, Especially In Rural And Remote Areas.

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