## 1 Introduction  
The discharge of dyes and pigments from various industries has generated large quantities of colored wastewater, which pose a potential threat to the environment [1; 2; 3; 4]. Malachite green (MG) (shown in Figure 1) is a cationic dye belonging to the triphenylmethane dye category, which is widely used for dyeing of textile, leather and paper, printing, and treating parasites, fungal and bacterial infections in fish and fish eggs [5]. However, this noxious dye can cause carcinogenic, mutagenic and teratogenic effects on humans and animals [6; 7; 8; 9]. Thereby, the removal of MG from wastewater before being discharged into surface water bodies is indispensible and significant. A variety of decolorization methods have been used for the removal of MG, including physical (adsorption), chemical (coagulating)and biological (decolorization-fermentation processes) [10; 11]. Since the chemical treatment method is unsuited for highly soluble dyes and longer time is required for biological method, adsorption is considered the efficient method due to its comparatively low cost and high removal efficiency [12; 13]. Nanomaterials have been used as promising materials for environmental remediation due to a series of unique properties, e.g., small size effect, surface effect and quantum size effect [14; 15]. Nano-sized zinc oxide is a wide-gap semiconductor with a high photo-catalysis activity, which has been widely used in the photodegradation of organic pollutants in wastewater [16; 17]. A wide range of studies have discussed the application of nanoscale zerovalent iron (nZVI) in the removal of heavy metals and organic pollutants [18; 19]. Due to its high reactivity, zerovalent zinc exhibited a faster removal of organic compounds than zerovalent iron [20]. To our knowledge, hitherto there have been no reports available in the literature regarding MG removal by nanoscale zerovalent zinc (nZVZ). Modeling and optimization are a vital stage in the contaminant removal process, which are beneficial for determining the optimum experimental condition and obtaining maximum removal efficiency [21]. Response surface methodology (RSM) as an experimental design tool was used to build models, evaluate the effects of factors and search for the optimum experimental conditions [22]. Artificial intelligence (AI) has recently emerged as a promising technology with tremendous development potential for actual applications, including intelligent search, autonomous driving, robots, big data, fingerprint identification and human-computer games [23; 24]. Artificial neural networks (ANNs) are a key AI tool commonly used as a black box that works better than RSM in terms of modeling and prediction, but they cannot obtain the global optimal solutions [25; 26; 27]. Particle swarm optimization (PSO) is inspired by the behavior of bird flocks, which has a potential to find the optimal solutions via \"particles\" movement [28]. The genetic algorithms (GA) based on the natural mechanics and natural selection is utilized for finding optimum solutions [29]. PSO or GA coupled with ANNs is used to achieve the optimum removal efficiency because it does not easily get trapped in a local optimum [30]. The aim of the present study was to investigate the feasibility that the the commercially available nZVZ could be utilized as a potential adsorbent for the removal of MG from aqueous solutions with the aid of the RSM, ANN-PSO and ANN-GA models. First of all, the nZVZ was characterized via X-ray diffraction and X-ray photoelectron spectroscopy. Then, the experiments for the adsorption of MG onto nZVZ were performed based on RSM design using the selected parameters, viz. initial pH, temperature, contact time and initial concentration. The RSM, ANN-PSO and ANN-GA models were applied to find the optimum process variables and maximum removal efficiency of MG. Next, the performance of these models was evaluated and the relative importance of the variables were obtained by sensitivity analysis.