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

作　　者：Mohammad Yaghoub Abdollahzadeh Jamalabadi 

机构地区：[1]Department of Mechanical Engineering,Chabahar Maritime University,Chabahar,Iran

出　　处：《Frontiers in Heat and Mass Transfer》2024年第2期511-536,共26页热量和质量传递前沿（英文）

摘　　要：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,momentumconservation,multiphase fluid flowin porousmedia,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 analyticalmethods 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.

关 键 词：MULTI-AGENT reinforced learning heat transfer mass transfer DRYING darcy flow MOISTURE optimization 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]