## 3 Methodology  
For the retrieval of the most relevant articles concerning the applicability of ANNs in adsorptive processes, preference has been given to the application of Methodi Ordinatio 2.0, a multi-criteria decision support tool [24]. This methodology emerged as an alternative to the classical models already employed, such as the Management System of the Central Research Institute of Hungary (MSCRI), the Cochrane Collaboration method, and the Knowledge Development Process (ProKnow-C). For years, these methods have been successfully used for the selection of bibliographic material. However, they require time and substantial effort for research. Therefore, [PERSON] et al. [24] proposed a less labor-intensive approach to qualify the volume of articles obtained in a systematic literature review. The criteria defined for constructing the relevance list of works include the year of publication (Pub.Y), the journal impact factor (IF), and the number of citations (nCi). Figure 3 illustrates the flowchart of the basic steps employed in the search. Upon defining the scope of the research, a preliminary search was conducted in the databases of _Science Direct, Web of Science_ and _Scopus_. A keyword search focused on the utilization of adsorption with the assistance of NN was carried out to assess affinities. Subsequently, the terms \"_Artificial Neural Network_\", \"_Dyes_\", \"_Activated Carbon_\", and \"_Adsorption_\" were employed to gather relevant articles on the subject. The option to modify the omission of some of these terms was employed to encompass other works of equivalent prominence. The Boolean operator \"And\" was used between the previously selected words to minimize disparities in generated results and restrict the search exclusively to the proposed theme. The choice of the databases was based on their closer connection to the broader field of engineering. Only review and research articles available in the databases were selected, and these were transferred to the reference manager [PERSON]. Subsequently, the application's own filtering procedures were applied to eliminate duplicates, and those with abstracts and titles that did not align with the initial search were discarded. In the following step, exportation to another reference manager, JabRef, was undertaken, which allowed for more suitable transfer to the RankIn spreadsheet. The spreadsheet is an innovative tool that allows one to retrieve criteria more efficiently and prevent manual errors [20]. With the input of the acquired articles, it became possible to automatically fill in the spreadsheet with the Pub.Y and the impact factor data. This was followed by the direct collection of citation counts from Google Scholar. After entering all the fundamental criteria, the formula below (Equation (9)) was applied for the calculation of the InOrdiatio 2.0 index [20]. \[\text{InOrdinatio 2.0}=\left\{\left[\Delta\times(\text{IF})\right]-\left[\lambda \times\left(\frac{\text{Re.Y}-\text{Pub.Y}}{\text{HalfLife.Ci}}\right)\right]+ \Omega\times\left[\frac{\text{nCi}}{(\text{Re.Y}+1)-\text{Pub.Y}}\right]\right\} \tag{9}\]  
Here, \(\Delta\) is the conditioner of the degree of importance the researcher assigns to the impact factor (IF) term (values between 0 and 10 can be chosen, with higher values attributing more relevance to journal metrics). IF is the journal metric for the journal where each article was published. When readily available, this factor is automatically populatedin the RankIn spreadsheet. Its value is extracted from the latest Cite Score metric according to the following Equation (10):  
\[\text{Cite Score(M5P)}=0.9193\times\text{JCR}+5.9116 \tag{10}\]  
\(\lambda\) is the factor relating to the weight assigned to the recency of the articles selected for the final portfolio, also variable on a scale of 0 to 10. Re.Y is the year in which the current literature review was conducted; Pub.Y is the year of publication.