Selenium Contamination in Water. Группа авторов
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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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