Algal Concentration Optimization for Reducing Pollution Load Index (PLI) in Sewage Water

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Problem Statement

Water pollution caused by heavy metal contamination in sewage water poses a major threat to both public health and the environment. One effective technique to reduce this contamination involves cultivating algae, which can naturally absorb heavy metals. However, finding the optimal algal concentration for maximum pollution absorption typically involves manual experiments conducted over several days. This manual process significantly delays pollution control interventions and increases operational costs.

Project Objective

The objective of this project was to develop a machine learning-based solution that predicts the ideal algal concentration required to minimize the Pollution Load Index (PLI) in sewage water. By using historical experimental data, the goal was to automate what previously required days of laboratory work, enabling real-time decision-making for environmental researchers and wastewater treatment teams.

Solved Problem

Traditionally, researchers would spend several days conducting repetitive laboratory experiments to determine the most effective algal concentration for reducing PLI. With this solution:

• The model can now predict the optimal algal concentration in seconds

• What earlier took several days of manual experimentation is now fully automated and instant

• This saves time, resources, and allows for faster environmental response and treatment planning

Key Contributions and Outcomes

• Developed an intelligent system to automate PLI reduction planning using algal concentration prediction

• Enabled data-driven decision-making for wastewater treatment

• Significantly reduced manual workload and accelerated experimentation

• Delivered a real-world deployable solution using cloud-based machine learning infrastructure

Impact

This solution has the potential to be integrated into:

• Wastewater treatment plants

• Tamilnadu Government pollution control departments

• Research labs focusing on eco-remediation

By accelerating the algal treatment planning process, this project contributes to sustainable pollution management and faster environmental restoration.