Open Access

ARTICLE

Optimal Design of Drying Process of the Potatoes with Multi-Agent Reinforced Deep Learning

Mohammad Yaghoub Abdollahzadeh Jamalabadi^*

Department of Mechanical Engineering, Chabahar Maritime University, Chabahar, Iran

* Corresponding Author: Mohammad Yaghoub Abdollahzadeh Jamalabadi. Email:

(This article belongs to the Special Issue: Advances in Drying Technologies)

Frontiers in Heat and Mass Transfer 2024, 22(2), 511-536. https://doi.org/10.32604/fhmt.2024.051004

Received 25 February 2024; Accepted 12 April 2024; Issue published 20 May 2024

Abstract

Heat and mass transport through evaporation or drying processes occur in many applications such as food processing, pharmaceutical products, solar-driven vapor generation, textile design, and electronic cigarettes. In this paper, the transport of water from a fresh potato considered as a wet porous media with laminar convective dry air fluid flow governed by Darcy’s law in two-dimensional is highlighted. Governing equations of mass conservation, momentum conservation, multiphase fluid flow in porous media, heat transfer, and transport of participating fluids and gases through evaporation from liquid to gaseous phase are solved simultaneously. In this model, the variable is block locations, the object function is changing water saturation inside the porous medium and the constraint is the constant mass of porous material. It shows that there is an optimal configuration for the purpose of water removal from the specimen. The results are compared with experimental and analytical methods Benchmark. Then for the purpose of configuration optimization, multi-agent reinforcement learning (MARL) is used while multiple porous blocks are considered as agents that transfer their moisture content with the environment in a real-world scenario. MARL has been tested and validated with previous conventional effective optimization simulations and its superiority proved. Our study examines and proposes an effective method for validating and testing multiagent reinforcement learning models and methods using a multiagent simulation.

---

Keywords

---