1. Use of Data Analytics to foster Energy Efficiency Workshop on Low GradeWHU for PaliTextile Industry Cluster, PACE-DTechnical Assistance Programme, 12th August, 2014 Umesh Bhutoria,Founder & CEO, E-Cube Energy

2. About Us E-Cube Energy is one of the emerging players in energy efficiency domain working with energy intensive industries to help them develop and implement sustainable energy efficiency practices. Largest portfolio in the Textile Industry with over 10 Lac Spindle capacity under management. In less than 2 years of operation developed first of its kind cloud based Energy Information Management and Analytics Portal EnView. Established saving potential of over 15 Million Units/Year across 8 textile units. Developed Normalization and Energy Forecasting Models for Textile and Iron & Steel Industry. Thought leaders in energy efficiency domain focusing on Energy Data Analytics. Released a white paper on Indian perspective on Energy Data Analytics 3. PaliTextile Cluster One of the biggest industrial clusters in Rajasthan. Most of the industries are Dyeing, Finishing/Processing units. Significant thermal energy consumption and thereby an opportunity to recover Low Grade Heat and optimize overall energy consumption pattern. Availability of quality data/information has been identified as biggest challenge in driving energy efficiency. Ref: Pali Textile Cluster, under BEE SME Programme 4. Challenges-Textile Energy Efficiency 5. Need for perspective shift on driving Energy Efficiency? 6. How can Data help? Performance Assessment Baseline vs Actual Energy Data Explore avenues for further energy saving/ optimization Forecasting Models/ Normalization 7. Case Study- Process Optimization One of the units had a 12 TPD Dye House. Product mix mainly comprised Polyester,Acrylic,Viscose, Carded Cotton, Combed Cotton. Their actual steam consumption was deviating significantly in comparison to the Budgeted Consumption. We looked at their steam consumption and production data (some 300 data sets),developed forecasting model and SKG indexes for all product types. SKG Index SKG ABP Polyester 3.55 4 Viscose 6.62 7 Acrylic 3.89 3.3 Carded cotton 14.61 7.5 Combed cotton 10.00 7.5 8. Case Study- Normalization PV V P Electric ity in MkWh UKG (Actual) Baseline 11.32% 33.72% 54.97% 38.02 2.79 Compliance 22.24% 26.20% 51.56% 43.58 2.85 % Change 10.92% -7.51% -3.41% 14.62% 2.15% 35.00 36.00 37.00 38.00 39.00 40.00 41.00 42.00 43.00 44.00 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% Baseline Compliance BaselineVs Compliance Period Electricty in mkWh Yarn A Yarn B Yarn C 9. Case Study- Normalization PV V P UKG Index 3.56 3.30 2.37 Calculated UKG Contribution for Baseline 0.403 1.113 1.303 Calculated UKG Contribution for Compliance 0.792 0.865 1.222 Expected Change in UKG attributed to Product Mix 2.13% Actual Change in UKG 2.15% Change in UKG attributed to factor other factors 0.02% 10. Case Study- Performance Assessment 800 900 1000 1100 1200 1300 1400 1500 19-03-2014 21-03-2014 23-03-2014 25-03-2014 27-03-2014 29-03-2014 31-03-2014 02-04-2014 04-04-2014 06-04-2014 08-04-2014 10-04-2014 12-04-2014 14-04-2014 16-04-2014 18-04-2014 20-04-2014 22-04-2014 24-04-2014 26-04-2014 28-04-2014 30-04-2014 02-05-2014 04-05-2014 06-05-2014 08-05-2014 10-05-2014 12-05-2014 14-05-2014 16-05-2014 18-05-2014 20-05-2014 22-05-2014 CD Energy/ Day Analysis Total kWh High Low 11. Case Study- Performance Assessment 1,309.94 1,029.06 280.88 - 200.00 400.00 600.00 800.00 1,000.00 1,200.00 1,400.00 CD Section Energy/Day Analysis Diff in kWh Avg kWh after optimisation Avg kWh before optimisation 12. Case Study- Supply Side Management TIME AIR Required WATER Actual Flow Optimised Flow SavingsVia Optimisation in KW (5%) SavingsVia Optimisation as per requirment 8:30 AM 68524.08 416.7 70000 70,000.00 0.00 - 9:30 AM 83580.69 444.07 70000 70,000.00 0.00 - 10:30 AM 72120.37 485.28 70000 70,000.00 0.00 - 11:30 AM 75014.99 534.11 70000 70,000.00 0.00 - 12:30 PM 78996 586.55 70000 70,000.00 0.00 - 1:30 PM 72712.78 608.88 70000 70,000.00 0.00 - 2:30 PM 66108.11 626.44 70000 70,000.00 0.00 - 3:30 PM 59403.46 619.61 70000 66,500.00 7.99 12.74 4:30 PM 56539.97 616.69 70000 66,500.00 7.99 19.13 5:30 PM 56539.97 616.69 70000 66,500.00 7.99 19.13 6:30 PM 56539.97 616.69 70000 66,500.00 7.99 19.13 13. Case Study- Supply Side Management 50000 55000 60000 65000 70000 75000 80000 85000 90000 1 2 3 4 5 6 7 8 9 10 11 AIR Required Actual Flow Optimised Flow 14. Case Study- Supply Side Management 13000 14000 15000 16000 17000 18000 19000 Total High Low 15. Metering tells whats obvious whereas Data Analytics predicts and tells us what should have happened 16. How can you get started? Select process/ problem areas and its scope Define Key Performance Indicators "KPIs" Identify data sources Initate data accquistion with time stamp Analyse energy and production/process KPI based benchmarking/ trend analysis Automatic Alerts/Offsite reports, energy saving projects Implementation of projects/ Highlight Best Case Practices 17. ThankYou! umesh@eetpl.in | 09831012510