Accelerated circularity of consumer electronics using Block Chain and Optimized Machine Learning Algorithms
Date
2019-08
Authors
Mwongela, Stanslaus
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
At the heart of our economy lies a linear take, make, dispose model, relying on consuming big
amounts of finite products and fossil fuels. The challenge of consumer electronic waste is
colossal, and is continuously growing. In 2016 alone, there was a global generation of 44.7
million tons of e-waste, with an estimated raw material value of EUR 55 billion. 435 thousand
tonnes of the 44.7 million tonnes were mobile phones. Only 20 percent of e-waste is documented
to be collected and recycled under appropriate conditions, whereas the remaining 80 percent is
either thrown into the waste stream or dumped, traded or treated under substandard conditions.
The consequences of this linear electronics system pose environmental and health impacts both
at the production and disposal ends. At the same time, the economic opportunity is significant.
Taking smartphones as an example, almost 1.5 billion are shipped every year, with each unit
containing components worth over USD 100. This represents a potential USD 150 billion of
value that enters the market each year. This value should remain in the system. Even if the
materials present in smartphones were recovered through recycling the least valuable loop of a
circular economy they could be worth up to USD 11.5 billion. The need to rethink the system is
clear, but solutions so far have been limited. The circular economy coupled with the Al
opportunity presents a vision and a workable path forwards to meet these challenges, by
redesigning our economy to be one that creates rather than extracts value, and keeps the finite
technical resources in flow within the economy. The main purpose of this study is to build a
Block Chain Circularity Incentive System that facilitates the accelerated circularity of consumer
electronics. The main focus being a decision system that processes the block chain data of
consumer electronics use and disposal needs to allow circular product and material flows as well
as operate circular business models. A formal experimental design is proposed, more specifically,
a simple factorial design. As opposed to having one learning algorithm to process electronic
products usage on a Big Chain DB, this research proposes to run multiple algorithms and
aggregate the results into a single score. The research also proposes to collect primary data from
consumer electronics manufacturers, consumers of electronics, and E-waste recovery firms. The
primary data would be used to identify the challenges this stake holders face, train the learning
algorithms based on consumer electronics use and disposal needs and additionally test the Block
Chain Circularity Incentive System developed.
Description
Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenya
Keywords
Circular economy, Consumer electronics waste, Block-chain, Machine-learning