Application of Artificial Neural Network for Prediction of Key Indexes of Corn Industrial Drying by Considering the Ambient Conditions

Bin Li,Changyou Li,Chengjie Li,Junying Huang

doi:10.3390/app10165659

Bin Li, Changyou Li + Show 2 more

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https://doi.org/10.3390/app10165659

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Journal: Applied Sciences	Publication Date: Aug 14, 2020
Citations: 4	License type: CC BY 4.0

Affiliation: South China Agricultural University

Abstract

Uncontrollable ambient conditions are the main factors limiting the self-adaption control of an industrial drying system. To achieve the goal of accurate control of the drying process, the influence of the ambient conditions on the drying behavior should be taken into consideration when modeling the drying process. Present work introduced an industrial drying system with a loading capacity of 50 t, two artificial neural network prediction models with (IANN) and without (OANN) considering the ambient conditions were established using artificial neural network modeling approach. The ambient conditions on the moisture content (MC), exergy efficiency of the heat exchanger (ηex,h) and specific recovered radiant energy (Er) of the drying process were also investigated. The results showed that the ηex,h and Er increase with the increase of ambient temperature while the drying time decrease with the increase of the ambient temperature. The IANN model has a better prediction performance that that of OANN model. An optimal architecture of 9-2-12-3 artificial neuron network model was developed and the best prediction performance of the artificial neural network (ANN) model were found at a training epoch number of 30, and a momentum coefficient of 0.4, where the coefficient of determination of moisture content, exergy efficiency of heat exchanger, and the specific recovered radiant energy, respectively are 0.998, 0.992, and 0.980, indicating that the model has an excellent prediction performance and can be used in engineering practice.

Highlights

IntroductionDrying is the process of removing the moisture from natural products (e.g., agricultural products, wood, and fruits) or industrial materials (e.g., lignite, ceramics, and medical materials) down to a specific moisture content, while ensuring at the same time prime product quality, high throughput, and minimal operational costs [1,2]
Drying is the process of removing the moisture from natural products or industrial materials down to a specific moisture content, while ensuring at the same time prime product quality, high throughput, and minimal operational costs [1,2]
In the last few decades, researchers have done a lot of works on the mathematical modeling of grain drying process, which mainly focus on the modeling of single particle drying [4,5,6], thin layer drying [7,8], and deep bed drying [9,10,11], of which the deep bed drying model is generally used in industrial drying

Summary

Introduction

Drying is the process of removing the moisture from natural products (e.g., agricultural products, wood, and fruits) or industrial materials (e.g., lignite, ceramics, and medical materials) down to a specific moisture content, while ensuring at the same time prime product quality, high throughput, and minimal operational costs [1,2]. Industrial drying is an effective approach to achieve the efficient and economic production of the agricultural product, while the real-time detecting and controlling technology plays a very important role in the production. The real-time measurement of the drying process has hysteresis and is interfered by many factors [3]. It is very difficult to establish a mathematical model that is in line with the actual drying process in industrial production. The typical deep bed drying model is partial differential equation (PDE), which is

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Conclusion