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IIT Mandi to use AI for wastewater management
IIT Mandi to use AI for wastewater management

IIT Mandi to use AI for wastewater management

The findings of the research will no longer require human intuition to choose the water treatment methods.

Published Updated

Key takeaways

  • IIT Mandi's research employs AI to enhance wastewater management by eliminating the need for human intuition in treatment method selection.
  • The study utilises Multi-Criteria Decision-Making techniques, combining qualitative and quantitative methods for effective solutions.
  • Biopipe, MBR, and SBR emerged as the most sustainable wastewater treatment options based on specific sustainability criteria.
  • The research's methodology can be adapted to various decision-making scenarios, from consumer products to socio-cultural policies.
5 min

A research team at IIT Mandi used AI-enhanced Operation Research (OR) tools to meet the socio-economic needs of specific environments and places to select wastewater management.

The paper is authored by Dr Atul Dhar, Associate Professor, School of Engineering and Dr Satvasheel Powar, Assistant Professor, School of Engineering, IIT Mandi and the findings have been published in the Journal of Cleaner Production.

Satvasheel Powar while explaining the research said that the findings of the research will no longer require human intuition to choose the water treatment methods. With the new techniques and the magnitude of the activity, several methods can be used considering the socio-economic fabric of today’s society.

The Multi-Criteria Decision-Making (MCDM) is a combination of alternative decisions and quantitative and qualitative research methods which helps in providing compact solutions. The MCDM method is equipped with AI features by the team to handle uncertainties of the decision-making process.

Dr Atul Dhar said in his explanation of the research that Biopipe, MBR and SBR were ranked the most sustainable options by MOORA and TOPSIS in a specific set of sustainability criteria which was validated by data analysis.

The study can be applied to various multi-criteria selection problems from simple consumer decision products to large scale investments and socio-cultural policies because the method incorporates a mix of technology and human sentiments.

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