Reduction in Energy Consumption by up to 25%
By leveraging AI and ML, one can reduce the energy consumption in certain processes up to 25%.
Factors like Aeration in activated sludge, wastewater quality and machinery used as well as external factors influence the amount of energy consumed. With the rising cost of energy, commitment to ESG commitments, and the ability to tap Grid for Smart Energy present some challenges and opportunities. By leveraging AI and ML, one can reduce the energy consumption in certain processes up to 25%. Leveraging AquaML, Data Engineering and Machine Learning pipeline, we are able to identify and predict the factors contributing to increasing energy costs, as well as mitigate the uncertainties and risks associated in the process. Be it Machinery like pumps, blowers or integrating Smart Grid, our zeroing in to the performance of a particular process, we use ESA, Vibration as well the Energy Consumption. And all these savings lead directly to lowering OPEX, Decarbonization credits and improved efficiencies. Further by leveraging PLC/SCADA, Advanced Sensor Data and real time communications (bi-directional), we can provide reliable recommendations and insights. The AI and ML, in conjunction with IIOT, can further provide further insights in your operations, including all the controls, and process optimization further leading the digital transformation of your water treatment operations. It helps improve water management through system controls, greater sensor integration, real-time analytics, and two-way communication. For the Operational Efficiency of the plant, the intelligence-based alerts and notifications allow you to get notifications on what matters the most. In addition, it can also provide insights volumetric flows, pipe clogging and distribution network performance.
-Full Feature Data Engineering – 100 plus data sources
- Automated Machine Learning – More than 85 Machine Learning Model
-Zero CapEx – Flexible Pricing Options
-Customized and localized to your needs