Smart waste management using IoT
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This document proposes a smart waste management system using IoT for developing countries. It discusses motivations like environmental pollution from untreated waste. The proposed system features include being automated, time efficient, reducing losses, and creating jobs. It will use algorithms like linear regression, decision trees, and K-means clustering. Two algorithms are described: one uses a Decreasing Time Algorithm to assign smart bins and prioritize waste collection. The other uses DTA to assign waste collection trucks. The system was tested in a closed area experiment and showed improved efficiency over a normal system. Future plans include expanding the pilot program and generating revenue through compost sales and exporting treated waste.
Smart waste management using IoT
- 1. Smart Waste Management System using IoT
- 2. Presented by: RAFEE MIZAN KHAN CHOWDHURY-16341020 B.M RAKIB HASAN – 12321065 LABIB MD ISTIAQUE - 16141003 A.M.M.GOLAM YEAZDANI-13101215 Supervised by: DR. AMITABHA CHAKRABARTY
- 3. INTRODUCTION
- 4. MOTIVATION Environment is highly polluted by untreated waste Smart waste management systems are present in developed countries No smart system is present in developing countries To increase job opportunity
- 5. FEATURES 1. Automated process 2. Time efficient 3. Less system loss 4. Predictable output 5. Automated work assignment 6. Creating more job opportunity
- 6. Related Algorithms Linear Regression Functional Regression Decreasing Time Algorithm (DTA) Logistic Regression Decision Tree SVM Naive Bayes KNN K-Means Random Forest Dimensionality Reduction Algorithms Gradient Boost & Adaboost
- 7. ALGORITHM :1 Regional Smart Bin Mirpur Regional Waste collection office Mirpur Data process using DTA algorithm Data updates in System & creating priority list Assigning the waste collection on the basis of DTA result & priority list Fig: DTA for Employee Assign
- 8. ALGORITHM :2 Regional office Mirpur Central control center Data process using DTA algorith m Data update in system and creating priority list Assignin g the trucks on the basis of DTA results & priority list Recycle partner Fig: Truck Assign
- 9. Research area 1. Gulshan-1. 2. Mohakhali. 3. Banani.
- 10. Data Collection No Parameter 1992 In % 2005 In % 1 Glass/Metal/Or ganic 6.38 8.17 2 Plastics 1.74 4.1 3 Textile 1.83 4.57 4 Paper/cardboard 5.68 4.29 5 Food waste 84.37 78.87 Table: MSW composition (in %) at Dhaka between 1992 and 2005(Concern, 2009)
- 11. Data Collection Waste Composition Bangladesh (Dhaka) (% By Weight) Food and Vegetable 70 Paper Product 4 Plastics 5 Metals 0.13 Glass and Ceramics 0.25 Wood 0.16 Garden Waste 1.1 Other (Stone dirt etc) 5 Moisture 65 Table : Nature of Waste composition in Bangladesh
- 12. Data Collection Year Total population Urban population( % Total) Waste Generation rate(KG/ca p/day) Total Waste Generation( Tone/day) 1991 20872204 20.15 0.49 9873.50 2001 28808477 23.39 0.50 11695.00 2004 32765152 25.08 0.50 16382.00 2025(estima ted) 78440000 40.00 0.60 47064.00 Table : Growth in Solid Waste Generation in Urban Cities of Bangladesh
- 13. Data collection Education Level of the Workers Percent (%) Uneducated 45 Primary school pass 30 Under SSC pass 9 SSC pass 6 HSC pass 5 HSC pass to Under graduate 3 Graduated 2 Total 100 Table : Education Level of the Workers
- 14. Recent MSW management Fig: Flow of waste, recyclables and money in MSW management in Dhaka (adopted from Matter et al. (2013))
- 16. Closed Area Experiment We are assuming 1 employee Can collect 5 units of waste/hour Including human system loss
- 17. Results Smart System at 9am
- 18. Results Normal System at 9am
- 19. Results Smart System at 12pm
- 20. Results Normal System at 12pm
- 21. Results Smart System at 3pm
- 22. Results Normal System at 3pm
- 23. Results Smart System at 6pm
- 24. Results Normal System at 6pm
- 25. Visual Simulation of the System
- 26. Future work 1. We are already in talks with the local cantonment authority where they are al- ready planning to impose our plan in Dhaka Cantonment as a pilot project. After this, within 3 years, we want to implement this in Gulshan, Baridhara, Banani, Bashundhara R/A, Uttara. 2. We will sell the compost waste to the fertilizer producers and also bio gas treat- ment plants. We also have plans to export some of the treated waste and earn for- eign currency. 3. We also have plans to export some of the treated waste and earn foreign curren- cy. So, this is our revenue generation policy for the social business. 4. We will also have educational facility for the people under 18 and the children of the people working for us. We are planning to merge with Jaago Foundation, BRAC NGO to be our education facility provider.
- 27. Conclusion
- 28. Thank You