International Journal of Research In Science & Engineering Volume: 1 Issue: 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280 ESTIMATION OF DESKTOP E-WASTES IN DELHI USING MULTIVARIATE FLOW ANALYSIS Sumay Bhojwani1, Ashutosh Chandra2, Akriti Bhogal3, Mamita Devaburman4 1 Sumay Bhojwani, Dept. of Chemical Engg., IIT Delhi, sumay.25@gmail.com Ashutosh Chandra, Dept. of Chemical Engg., IIT Delhi, ashu.chandra10@gmail.com 3 Akriti Bhogal, Dept. of Chemical Engg., IIT Delhi, bhogal.akriti@gmail.com 4 Mamita Devaburman, Dept. of Chemical Engg., IIT Delhi, mamitadb@gmail.com 2 ABSTRACT This article uses the Material flow analysis for estimating e-wastes in the Delhi/NCR region. The Material flow analysis is based on sales data obtained from various sources. Much of the data available for the sales is unreliable because of the existence of a huge informal sector. The informal sector in India accounts for more than 90%. Therefore, the scope of this study is only limited to the formal one. Also, for projection of the sales data till 2030, we have used regression (linear) to avoid complexity. The actual sales in the years following 2015 may vary non-linearly but we have assumed a basic linear relation. The results of this study can be used to install a treatment plant for e-wastes in Delhi. The purpose was to know an approximate quantity of e-wastes that we will have by the year 2030 so that we start preparing ourselves for the ineluctable investment in the treatment of these ever-rising e-wastes. Keywords: E-wastes, Estimation, New Delhi, Multivariate flow analysis etc. ----------------------------------------------------------------------------------------------------------------------------1. INTRODUCTION Driven by a strong economic growth in the country, the quantities of e-wastes are expected to rise sharply in the coming years. The term e-waste or WEEE (Waste Electrical and Electronic equipment) includes any appliance that has reached its end-of-life and uses an electric supply or batteries. In an emerging economy like India, it is a very grave concern that the issue of e-wastes has not been investigated deeply by the authorities and proper care is still awaited. The ever growing amounts of wastes poses a huge environmental risk in an already late-bloomer like India (in terms of environmental protection). Currently in India, e-waste is recycled in two sectors: formal & informal. Of the total e-waste recycled in India, 95% is recycled in the Informal sector and only 5% in the formal one. The ewaste is often recycled in the informal sector due to the lack of proper legislations and lack of awareness. There is a lack of proper collection centers and procurement facilities. Therefore it is recommended to establish a formal recycling system, in order to manage e-waste from all user groups in a complete manner, to prevent damage to the environment and human health. [2] The benefits include implementing norms that promote the manufacture of longer-lasting products and using recyclable environmentally friendly raw materials. This also ensures that the products manufactured comply with the limitations imposed on hazardous substances. In India, this is slowly becoming a policy principle but the presence of informal sectors hampers its implementation. The formalization of the production, collection and recycling streams is a very important step for the assessment of e-wastes in India. Also, the lack of awareness among the general population regarding the repercussions on health and environment due to landfilling and improper handling of ewastes is a factor that hampers implementation of EPR in India. Another important reason is the lack of knowledge of the quantity of e-wastes generated each year and recycled. As a result, any government action taken on handling of e-wastes has been rendered useless. The following section will take you through the estimation process wherein we have used Material Flow analysis for estimating the quantities. IJRISE| www.ijrise.org|editor@ijrise.org International Journal of Research In Science & Engineering Volume: 1 Issue: 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280 2. METHODOLOGY The methodology is centered around the consumer behaviour and the sales of various electrical equipment in DelhiNCR. For understanding the process, we need to focus on two aspects: one is the sales, i.e. the rate at which various products are being manufactured and bought in Delhi and the second being the consumer behaviour, as in the usage characteristics of various products. The rate at which an equipment finds its way back to the waste stream (reaches its end-of-life) determines the amount of e-waste to be procured in each year. Finding sales data and consumer behaviour was extremely difficult due to lack of proper logistics and logbooks available on the internet. This was one of our major concerns while estimating the e-wastes. Thus, we had to device a way to approximate the amount of e-waste by considering the sales in all over India and then consider a fraction of it to be Delhi’s share. Some of this data was available for various components. MFA consists of the following aspects: (a) analysis of the system and the steps/processes involved right from the sale/production till the disposal and recycling of the product. (b) estimation of mass flows through the use of transport coefficients (c) Calculation of disposed/recycled e-wastes (d) Analysis of results via graphs, tables etc. To begin, we have to understand some terms: ‘Processes’, ‘Residence time’, ’flows’ ‘Transport coefficients’ and ‘Lifespans’. Processes are transformations, storage steps, reuse of any material or any stage where a material will stay for a certain time, known as residence time. Residence time is the time for which a material stays in a particular process. Flow is defined as transfer of material from one process to the other. Transport Coefficients are used to divide flow into multiple streams. It is the fraction of the total flow from a process. Lifespan is the measure of the end-of-life reaching ability of a product. 2.1 Model Description & Basic Analysis The model approximated for the scope of this analysis is as shown in Fig 1. The sales of a particular component (say, Desktops) are assumed to go through three processes - Businesses, Households and Government. Fig-1: Primary sorting and dismantling of e-waste Business process includes the components being bought for firms or businesses. Similarly, government process includes the components being purchased for government institutions and organizations. The household sector comprises those being purchased for personal use of the general population. Each Desktop (in any of the three sectors) can go through a Re-use step, Storage step or proceed directly towards Recycling/Disposal. However, a major chunk goes through to the re-use step. In the Storage process, computer equipment kept in storage for a period of time before it is disposed of or recycled is accounted. It has been assumed IJRISE| www.ijrise.org|editor@ijrise.org International Journal of Research In Science & Engineering Volume: 1 Issue: 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280 that when the product enters the storage step, it may go towards recycling after a residence time or go to a second storage process where against faces a small residence time before finally ending up in the recycling step. If the product enters the re-use process, it can either flow to a second storage or proceed towards the recycling step. The various assumptions in the model have been listed below: 1. There is no back-flow in any case considered here. 2. If an infinite time scale is considered, we can assume that there is no accumulation whatsoever. There exists a residence time though, but it has been assumed that all the products proceed to further steps once its residence time has reached. 3. At the beginning of the model analysis, all the units were empty. Then, due to sale of various computer equipment, the flow established and grew. The materials kept getting pushed ahead once their residence time reached. 4. Computer equipment flows in annual time steps. Monthly variations have not been considered. Based on the principle of mass conservation, we have a basic equation for the flows coming out of each process in terms of the transport coefficients and the amount of material incoming. All the flows coming out of a step in the year t, FX,out(t) , are equal to all the flows that entered the same process X in the year t - r, FX,in(t-r), where r is the residence time of computer equipment in that process. If a process has more than one outflow, transport coefficients T xy1 to Txyn have been used to calculate partitioning of flows coming out from process X to the downstream processes Y 1 to Yn. Suppose for example, the flow value for equipment going out of the household process can be given as: where THs1, THr and THrd are the transport coefficients for the flow coming out from the household process and going to the storage 1, re-use and recycled processes. Also, FPH(t-r) = FH,out(t) and that the sum of the above three transport coefficients equal to 1. Also, for the recycling process: The transport coefficients of various processes are listed below. Note that T xy1 has been used to calculate partitioning of flows coming out from process X to the downstream processes Y 1. Also, the transport coefficient data for Mobiles and Tablets have been assumed to be that of laptops because of the similarity in the increasing trends of usage due to ‘no’ data available in this regard. The transport coefficients listed are based on studies in emerging economies like Chile. [1] From To Parameter Transport Coefficients Desktop Production & Sale Government Government TPg 0.10 Business TPb 0.55 Households TPh 0.35 Households TPh 0.35 Storage 1 TGs1 0.20 Re-use TGr 0.50 IJRISE| www.ijrise.org|editor@ijrise.org International Journal of Research In Science & Engineering Volume: 1 Issue: 1 Business Households Re-use Storage 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280 Disposal/Recycling TGrd 0.30 Storage 1 TBs1 0.14 Re-use TBr 0.72 Disposal/Recycling TBrd 0.14 Storage 1 THs1 0.25 Re-use THr 0.54 Disposal/Recycling THrd 0.21 Storage 2 TRs2 0.50 Disposal/Recycling TRrd 0.50 Storage 2 TS1S2 0.10 Disposal/Recycling TS1rd 0.90 Table-1: Transport Coefficient Data for various Processes in the model assumed [1] Process Parameter Residence Time Desktops Government rg 4 Business rb 4 Households rh 6 Re-use rr 3 Storage 1 rs1 3 Storage 2 rs2 1 Table-2: The residence times for various processes [1] 3. RESULTS & DISCUSSIONS Year Sales in India (in numbers) Delhi Percentage Share Sales in Delhi (in numbers) Generated EWaste (in numbers) Weight of e-waste generated (in T/yr) 1997 574400 24.15% 138717 - - 1998 799058 22.86% 182664 - - 1999 1027190 23.21% 238410 - - 2000 1405327 22.95% 322522 - - 2001 1881640 22.78% 428637 - - 2002 2293643 20.28% 465150 - - 2003 3035591 10.99% 333611 - - 2004 3632619 15.72% 571047 - - IJRISE| www.ijrise.org|editor@ijrise.org International Journal of Research In Science & Engineering Volume: 1 Issue: 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280 2005 4614724 10.50% 484546 - - 2006 5490591 11.96% 656675 171488 4630 2007 5451656 12.21% 665790 268727 7256 2008 5992779 11.97% 717352 363512 9815 2009 5878916 12% 703722 400939 10825 2010 5740761 12% 687185 431558 11652 2011 5576002 12% 667463 486525 13136 2012 5393666 12% 645637 514084 13880 2013 5191943 12% 621490 574133 15502 2014 4985304 12% 596755 628565 16971 2015 4774924 12% 571572 664464 17940 2016 4551458 12% 544822 688034 18577 2017 4322974 12% 517472 685572 18510 2018 4083482 12% 488804 674315 18206 2019 3842965 12% 460013 654211 17664 2020 3602779 12% 431263 631835 17059 2021 3364275 12% 402713 607946 16414 2022 3136850 12% 375490 582905 15738 2023 2917898 12% 349280 556875 15036 2024 2708101 12% 324167 530064 14312 2025 2506619 12% 300049 502398 13565 2026 2308846 12% 276375 474267 12805 2027 2116750 12% 253381 445953 12041 2028 1930688 12% 231109 417767 11280 2029 1750748 12% 209569 390216 10536 2030 1579700 12% 189094 363454 9813 Table-3: Desktop Sales in India & Delhi & Weight of waste stream. [2] IJRISE| www.ijrise.org|editor@ijrise.org International Journal of Research In Science & Engineering Volume: 1 Issue: 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280 Fig-2: Desktop Sales & Recycled units vs year (1997-2030) Fig-3: Weight of Recycled units in T/yr (for 2006-2030) 3.1 Composition of a Personal Desktop Computer (based on a typical desktop computer weighing ~ 27 kg) Material Percentage Plastics 22.99% Lead 6.29% Aluminium 14.17% Iron 20.47% Tin 1.00% IJRISE| www.ijrise.org|editor@ijrise.org International Journal of Research In Science & Engineering Volume: 1 Issue: 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280 Copper 6.92% Zinc 2.20% Silica/Glass 24.88% Nickel 0.85% Table-4: Composition of a personal Desktop Computer [4] [Note that the compositions have been assumed to be constant due to lack of data in this regard. However, recent reports show the decreasing use of iron and increasing use of polymeric composites] Fig-4: Desktop Composition Pie-Chart [4] Based on the above composition, we have the following table which details the weights of materials disposed in various years: (note that for convenience, the weight disposed have been shown for some of the years and also, the metals have been grouped together). 3.2 Material Wise Analysis Year Plastics Aluminium Copper Iron Zinc 2013 3563817 2196576 1072710 3173177 341035 3856797 2014 3901693 2404828 1174411 3474017 373368 4222450 2015 4124528 2542173 1241485 3672427 394692 4463604 2016 4270833 2632349 1285523 3802695 408692 4621937 2017 4255549 2622929 1280922 3789086 407230 4605396 2018 4185677 2579862 1259890 3726873 400543 4529780 2019 4060887 2502948 1222329 3615761 388602 4394731 IJRISE| www.ijrise.org|editor@ijrise.org Glass International Journal of Research In Science & Engineering Volume: 1 Issue: 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280 2020 3921990 2417338 1180521 3492090 375310 4244416 2021 3773702 2325940 1135886 3360055 361120 4083936 2022 3618266 2230136 1089100 3221658 346246 3915723 2023 3456691 2130549 1040466 3077793 330784 3740865 2024 3290263 2027970 990371 2929608 314858 3560755 2025 3118535 1922125 938681 2776704 298424 3374909 2026 2943917 1814498 886120 2621226 281715 3185936 2027 2768162 1706170 833218 2464735 264896 2995731 2028 2593207 1598336 780557 2308959 248154 2806394 2029 2422186 1492926 729079 2156683 231788 2621313 2030 2256071 1390541 679078 2008777 215892 2441542 Table-5: Weight of different materials disposed over the years Overall Waste Trends Weight of Material disposed (kg/yr) 5000000 4500000 4000000 3500000 Plastics 3000000 Aluminium 2500000 copper 2000000 iron 1500000 zinc 1000000 glass 500000 0 2010 2015 2020 2025 2030 2035 Years Fig-5: Overall Trend of different materials disposed over the years IJRISE| www.ijrise.org|editor@ijrise.org International Journal of Research In Science & Engineering Volume: 1 Issue: 1 e-ISSN: 2394-8299 p-ISSN: 2394-8280 CONCLUSION Plastics Plastics are used in almost every electronic equipment – in the LCD, in the CRT, the outer body covering etc. Since the production of electronic equipments has increased over the years, weight of plastics found in the dumped electronic waste has also increased. Aluminium Magnesium is 33% lighter than aluminium. It therefore replaces aluminium alloys for virtually anything you want to make lighter and stronger. Since these days, everyone wants lighter and thinner devices that are durable enough to withstand the daily wear and tear, magnesium can make a big difference for these fragile products. Thus a decrease in the use of aluminium in electronic equipments has been predicted. Copper Copper is vital to the production of electronic connectors, circuitry wiring, printed circuit boards, micro-chips etc. It is cheaper and abundant than silver and solder will stick to it, unlike aluminium. Since usage of electronic devices is increasing day by day, use of copper is bound to increase over the years. Glass Many other types of glass like scratch resistant sapphire glass have been developed to replace it. But these are still not used more than say, gorilla glass, because it is expensive and has been used till now only in watches. Also, though the use of glass for touch screen devices has increased, it is compensated by the decline in the use of desktops which used to have 25% glass in their composition. Therefore, the weight of glass in e wastes is predicted to be roughly the same. REFERENCES Bernhard Steubing, H. B., “Assessing computer waste generation in Chile using material flow analysis” Waste Management , 30, pp. 473-482, 2009 [2] GTZ & BIRD, “e-Waste Assessment in India: Specific Focus on Delhi”, Delhi, 2007 [3] Feng Wang, J. H. “Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis” Waste Management (33), pp. 2397-2407, 2013 [4] ewasteguide.info. Retrieved from www.ewasteguide.info/valuable-substances, 1996 [1] IJRISE| www.ijrise.org|editor@ijrise.org