Transforming Circular Economy Models Through Data Analytics:A Predictive and Real Time Approach

The shift towards circular economy seeks to improve resource utilization, decrease pollution, and protect the environment by changing the traditional economy of ‘take-make-dispose’ to a closed looping system. A circular economy focuses on the continuous use of materials through recycling or regeneration to avoid resource exhaustion and waste generation. However, there are barriers to scaling up resource recovery including mapping out material flows, waste streams and other resources. This article discusses the role of data analytics tools enabled by IoT devices, sensors, and machine learning, in bridging the gap between traditional economy and circular economy. Life cycle analysis of products and material is carried out, so that ideal recovery points can be set, using models such as Random Forests and Long Short-Term Memory (LSTM) networks. The proposed framework connects data from IoT systems with industrial reports and waste management procedures providing accurate resource, waste and recycling management, and other operational related decision processes. This ultimately contributes to the goal of sustainability.