Application of artificial intelligence techniques in textile wastewater decolorisation fields: A systematic and citation network analysis review

Abstract

This study reviewed 155 journal articles to examine how artificial intelligence techniques are being applied in textile coloration and related fields. Distribution of the reviewed articles was assessed in terms of the type of journals, year of publication, methods, and research background. Based on the citation network analysis method, an objective approach, CitNetExplorer and VOSviewer are used to identify the clusters. It is found that artificial intelligence techniques are mainly three-layer artificial neural networks with different designs, which are mainly used in textile wastewater decolorisation fields, including wastewater treatment and colour fading, such as colour prediction and real-time monitoring of textile wastewater, dye removal efficiency, and dye degradation. Finally, the future research direction and limitations of this article are put forward.

Looking Back to Look Forward

“…They also build a case for more rigorous reporting of experimental information of studies which employ them, because the conventional level of detail is typically insufficient to make sense of the behaviour of reactive dyes within a cellular environment. Other reviews to appear in the last year tackle subjects that are of importance to diverse fields of coloration: research to treat coloration industry wastewater [5], developments in oxidative colorants for the permanent dyeing of hair [6], modification of pigment dispersion properties through encapsulation [7] and technologies that greatly reduce water usage in dyebaths [8].…”

A Review on Applications of Artificial Intelligence in Wastewater Treatment

“…AI models are becoming more and more popular in wastewaterrelated fields, especially in recent years (see Figure 1), and have been employed for the prediction and optimization of the WWT process [9,10]. In previous WWT-related research, AI models have shown very good prediction and optimization performances [11], and have been successfully applied to WWT process design [10,12], water quality monitoring [13,14], WWT process parameters optimization [15,16] and WWT process performance prediction [17,18]. These pieces of research have demonstrated that an AI model, as a powerful tool, has achieved great success in the applications of WWT-related fields.…”