AI Implementation at Bengaluru Sewage Plant Reduces Power Usage by 25%
AI tools at sewage plant helps cut down power consumption by 25%: BWSSB
Image: Deccan Herald
The Bangalore Water Supply and Sewerage Board (BWSSB) has successfully implemented an AI tool at the Nagasandra Sewage Treatment Plant, achieving a 25% reduction in power consumption, which translates to annual savings of approximately ₹50 lakh. This initiative will soon expand to all 34 sewage treatment plants in Bengaluru.
- 01The AI tool has led to a 25% reduction in power consumption at the Nagasandra Sewage Treatment Plant.
- 02Annual savings from this implementation amount to nearly ₹50 lakh (approximately $60,000 USD) per plant.
- 03BWSSB plans to roll out the AI tool across all 34 sewage treatment plants, potentially saving ₹20 crore (about $2.4 million USD) annually.
- 04The tool optimizes blower operations by automatically adjusting oxygen levels based on demand, reducing the need for constant operation.
- 05BWSSB spends approximately ₹90 crore (around $10.8 million USD) monthly on electricity, making power savings crucial for operational costs.
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The Bangalore Water Supply and Sewerage Board (BWSSB) has introduced an AI tool at the Nagasandra Sewage Treatment Plant (Phase II), resulting in a significant 25% reduction in power consumption. This innovation is projected to save nearly ₹50 lakh (approximately $60,000 USD) annually per plant. Following the successful pilot study, BWSSB Chairman Ramprasat Manohar announced plans to implement this AI tool across all 34 sewage treatment plants in Bengaluru. The AI system optimizes blower operations, which traditionally run continuously, by automatically adjusting to the oxygen demand in the sewage. This advancement aims to address the 70% of BWSSB's revenue spent on electricity charges. With the broader rollout, BWSSB anticipates total annual savings of around ₹20 crore (approximately $2.4 million USD). The board is also exploring additional AI applications, including robotic technology for leak detection in water pipelines.
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The implementation of the AI tool will significantly reduce electricity costs for BWSSB, allowing for better allocation of resources and potentially lower operational costs for water treatment.
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