Analytical Modeling and Optimal Control of Cold Storage System with Large-Scale Implementation Using IoT

Warehouses and cold storage mechanisms are integral and important components for the storage of the agricultural products beyond their intended life-period. Configurability and self-adaptability can greatly enhance the functional efficiency of these systems and minimize wastage of resources. This paper explores the technical glitches faced by the cold storage system and proposes a novel design for a new and advanced automated cold storage system (ACSS). To analyze the performance of ACSS, a mathematical representation of the model is presented. Two performance metrics, namely, temperature and humidity, are considered which play a vital role in any cold storage system. This paper also proposes efficient methods to monitor and control these performance metrics of the cold storage system. The self-adaptable capabilities are provided by using proportional-integral-derivative (PID) controller whose parameter are tuned using particle swarm optimization (PSO) algorithm. The performance of the designed cold storages system is evaluated using MATLAB simulation. Also, to provide the intelligence capability, the ubiquitous Internet of Things (IoT) implementation of ACSS has been proposed in this paper along with the deployment strategy which further improves the efficacy of cold storage system due to its seamless reconfigurable and self-adaptable capabilities.