## 4 Results and Discussion
### Neural Networks in Adsorptive Processes  
The environmental application of AI technologies has focused its studies on the optimization of wastewater treatment processes (WWTPs), more specifically in process design [49; 50], water quality monitoring [51; 52], WWTP parameter optimization [53; 54], and performance prediction [55; 56]. Compared to all other approaches, adsorption remains an efficient, easy-to-operate and economical technique due to its low investment costs and better accessibility to different adsorbents and allows estimations [57]. Therefore, the integration of predictive tools in various types of adsorptive system configurations to achieve a better balance of optimal operational conditions is recurrent [17]. Response surface methodology (RSM) has commonly stood out as a widely used tool in adsorption. At the same time, the search for, and synthesis of, new materials that can offer efficiency, low costs, and effective recovery is notorious. Recent research has focused on the use of clay minerals, metal microcomposites, organic waste, and nanocomposites due to the potential for using reusable materials with high porosities and surface areas [55; 57]. Significantly, ANNs have also garnered attention in many scientific fields as some of the main tools for operational improvement. One key advantage of using ANNs in adsorption-based environmental remediation is their ability to model complex, nonlinear relationships between various factors that influence the adsorption process, such as adsorbent properties, pollutant concentrations, pH, temperatures, and contact times. These models can predict the adsorption capacities of different materials under varying environmental conditions, which aids in selecting the most effective adsorbent for a specific pollutant or environmental setting. ANNs can also be used to design and optimize new adsorbent materials. For example, by analyzing the structural properties of various materials, ANNs can predict which modifications will enhance adsorption efficiency. This accelerates the development of innovative adsorbents, such as nanomaterials or bio-based materials, for environmental cleanup. Furthermore, ANNs improve the real-time monitoring and control of adsorption systems. By continuously analyzing operational data, ANNs can predict system performance, detect potential issues, and suggest adjustments to maintain optimal adsorption rates. This proactive approach enhances process reliability and reduces operational costs. Consequently, there is a growing application of these networks in adsorption systems due to the increased availability of software equipped with dedicated artificial intelligence packages and the possibility of integration with other programming languages [58]. To better understand the impact of this type of technology, a survey was carried out of the main networks used in adsorption systems. As shown in Table 3, the results obtained make it clear that neural networks have been extensively applied in this area, especially in the removal of dyes and heavy metals. In these studies, key feeding factors for the network were considered: the initial concentration of the target pollutant, pH, contact time, temperature, and adsorbent dosages. These data are commonly divided for training, validation, and testing procedures with ratios of 60%, 20%, and 20% or 50%, 25%, and 25%, respectively. For the testing phase, the R\({}^{2}\), MS, and RMSE are notable criteria for evaluation and performance (Table 3). Among the various possible architectures, it is evident that the MLPNN continues to be the most accepted among researchers [2,8,10,11,15,32,33,34,59,60]. This is because the MLPNN architecture is easy to use and has the advantage of being able to approximate any relationship between input and output data through three typical layers [29]. The most used software is Matlab, except in the work of [PERSON] et al. [14], who used the Python language (Table 3). For instance, [PERSON] et al.