(Li et al. 2015). A study demonstrated that iron oxides and aluminum oxide are effective adsorbents for Se removal (Kuan et al. 1998; Sheha and El‐Shazly 2010; Sharrad et al. 2012). Coagulation was found to be an effective process for removal of metal from water (Hu et al. 2015). They found that the Fe‐based coagulant was much more efficient than the Al‐based coagulants. It was further suggested that researchers’ pre‐reduction of Se (VI) to Se (IV) seems to be necessary to achieve effective Se removal. A synergistic effect of using ZVI was demonstrated by Liang et al. (2015) with magnetic effect and it was found that removal efficiency increased, with a reduction in removal time at a pH of 6.0. Graphene nano sheets, as a novel nano adsorbent, can also be used for removing various pollutants from water by certain modifications. To overcome the structural limits of graphene (aggregation) and graphene oxide (hydrophilic surface) in water, sulfonated graphene (GS) is prepared by diazotization reaction using sulfanilic acid (Shen and Chen 2015). Another technology demonstrated for removal of selenium from water is by sequestration by Cationic Layered Rare Earth Hydroxide Y2(OH)5Cl·1.5H2O. One study confirmed that Se (IV) and Se (VI) are successfully sorbed onto Y2(OH)5Cl·1.5H2O, which is also further supported by Raman and EDS measurements (Zhu et al. 2017). Another study demonstrated a promotional effect of the use of Mn2+ and Co2+ selenate removal by ZVI. It was found that Selenite (SeIV) was the predominant reductive product in the presence of Co2+; however, selenite and elemental Se (Se0) were the main reductive products in the presence of Mn2+. But this process shows promotional effect in anaerobic conditions (Tang et al. 2014).
Taking the role of AI further, there is another powerful area of predictive modeling which is a method used in predictive analytics to generate a statistical visualization of future behavior. Predictive analytics is the domain of data mining related to anticipating likelihoods and inclinations. Alternatively, AI deals with intelligent acts, i.e. the activities that describe them as intelligent. Subsequent to the thought process, the sole purpose is to evaluate the influence of AI algorithms for the implementation of intelligent predictive models. On piece of promising research (Pinto et al. 2009) answers many crucial issues by construction predictive models. These models stimulate prediction of manganese and turbidity echelons in treated water, to guarantee that the water supply does not distress community healthiness in a undesirable mode and observes the existing regulation. Additionally, popular supervised classification algorithms such as decision trees and the unsupervised k‐means algorithm build clustering models.
Recently, it has been interesting to note that the presence of selenium in plants has been modeled to show a tight borderline limit between nutritious prerequisite and toxic supplement in plants (Soil Science Society of America 2008). The AI algorithms beautifully model how the steep dose response curve caused by bioaccumulation properties have led to the description of selenium as a “tinderbox” modeled through anthropogenic events.
1.4 Selenium Research Dynamics Using AI Techniques
AI and Deep learning methods have spread their capabilities in depicting contests for water‐sanitation amenities and research forums. Deep learning presents an outstanding substitute to countless studies in optimization (Dentel 1995). Compared to out‐of‐date machine learning algorithms, deep learning has a robust learning capability to efficiently utilize data sets for data mining and knowledge mining. The objective of this investigation is to assess the prevailing unconventional methods. This paper further explores the boundaries and predictions of deep learning.
Furthermore, a novel placement of a machine learning ensemble in the field of water distribution networks was studied by Camarinha‐Matos and Martinelli (1998). This was an innovative application to govern and implement water distribution networks by a supervision classification, a distributed information management framework for water quality monitoring and recreation.
An alternate implementation of ANN along with SVM (Haghiabi et al. 2018) investigates water quality prediction. These authors reach a valuable outcome from their research, that “tansig” and “RBF”, which are transfer and kernel functions, demonstrate significant performance compared to other functions. SVM proves to be the most accurate model compared to other machine learning algorithms.
Finally, it is time to end up the discussion by looking at the most important issue of all, i.e. cost. It is extremely important to give a keen thought to the cost issue for wastewater treatment. Machine learning has been uniquely deployed (Torregrossa et al. 2018) for efficient energy cost modeling for wastewater treatment plants. The researchers have innovatively proposed cost as a parameter to evaluate the performance of the system.
Thus, these technologies ensure that performance is accurately predicted and assists in ensuring that efforts are made to deal with issues in advance. Machine learning generates innovative visions that can be used as evidence for future research on scheduling the distribution of the water resources
1.5 Conclusion
The presence of selenium in plants has been modeled to show a tight borderline limit between nutritious prerequisite and toxic supplement. The steep dose response curve caused by the bioaccumulation properties of selenium have led to the description of this element as a “tinderbox.” Water treatment for selenium removal is a component of successful selenium management strategy. Several technologies have been used by countries for selenium removal. There is a noticeable evolutionary role played by machine learning and artificial intelligence techniques in modeling and estimating the parameters contributing to efficient performance of systems.
At this stage, benchmarking plays a significant role in assessing the performance of technologies in terms of their value proposition, environmental impacts (following the principle of clean technology with proper treatment of sludge as product obtained) or, in other words, satisfying all the components of ASSURED analysis: A (Affordable), S (Scalable), S (Sustainable), U (Universal), R (Rapid), E (Excellent), D (Distinctive). A credible benchmarking by assessing the technologies based on the ASSURED parameters will help to screen technology which is more capable of being replicable, non‐disruptive, and scalable.
Acknowledgment
The authors are thankful to the Director, CSIR‐NISTADS, Management of Sinhgad Technical Education Society, Pune, and Management of IIS (deemed to be University), Jaipur for their continuous support and guidance in carrying out this research work.
Conflict of Interest
The authors do not have a conflict of interest.
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