ResNet14Attention network for identifying the titration end-point of potassium dichromate

Siwen Liang; Linfei Yin; Dashui Zhang; Dongwei Su; Hui-Ying Qu

doi:10.1016/j.heliyon.2023.e18992

ResNet14Attention network for identifying the titration end-point of potassium dichromate

Heliyon. 2023 Aug 6;9(8):e18992. doi: 10.1016/j.heliyon.2023.e18992. eCollection 2023 Aug.

Authors

Siwen Liang¹, Linfei Yin¹, Dashui Zhang², Dongwei Su¹, Hui-Ying Qu²

Affiliations

¹ Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning, Guangxi, 530004, China.
² School of Chemistry and Chemical Engineering, Nanning University, Nanning, Guangxi, 530004, China.

Abstract

With the rapid development of industry, the increasing discharge of sewage causes the detection of water quality to be of increasing importance. Potassium dichromate titration is one of the most important testing methods in water quality detection; the ability to accurately identify the titration end-point of potassium dichromate is currently a research challenge. To identify titration end-point quickly and accurately, this study proposes a ResNet14Attention network, which utilizes residual modules that focus on original image information and an attention mechanism that focuses highly on classification targets. The proposed ResNet14Attention network is compared with 12 convolutional neural networks such as ResNet series networks, VGG, and GoogLeNet. The results of comparison experiments reveal that only the proposed ResNet14Attention network has the highest training and testing accuracy of 100% among all convolutional neural networks in the comparison experiment; the proposed ResNet14Attention network has the highest training speed compared to all the networks that over 90% accuracy.

Keywords: Attention; Deep learning; Potassium dichromate method; ResNet; Titration end-point.