The power of data analytics to move towards innovative waste management

The power of data analytics to move towards innovative waste management

What cannot be measured cannot be managed efficiently. That is why the most innovative systems in waste management use models based on data analytics; they make it possible to optimize tasks and make predictions, provided that this information is properly collected, processed and analyzed. Without accurate data on volumes, consumption habits, types of waste, composition of waste materials, etc., any strategy is a blind bet.

Data analytics facilitates the collection of metrics and historical insights, whose predictive powers allow anticipating future behaviors, events or results. Equipped with sensors, garbage bins and collection trucks collect data whose analysis allows them to take control of the processes in real time, adjusting collection according to demand, reducing costs and emissions, etc.

Data, a key element
With data analytics in waste management, greater efficiency and sustainability is achieved, because:

Improving recycling. Knowing what is thrown away, where and how often helps to design more effective awareness campaigns and improve separation at source.
Drives the circular economy. The data allow us to identify opportunities to reuse waste as a raw material, for example, to convert organic waste into biofuels.
Transparency and governance. They facilitate decision-making based on evidence, not assumptions, which strengthens the management of recycling centers.
Basic concepts and KPIs
In a state-of-the-art recycling center, machines with cameras and sensors are set in motion to capture data. After collecting and analyzing the data, decision-makers can make informed and proactive decisions. Because with a data-driven model, reliable predictions can be made, more is known about the waste generated and deposited and in relation to its management.

What is the real scope? They can identify opportunities, mitigate risks and improve processes, and even ensure the maintenance of their equipment. Data analysis turns waste management into an intelligent strategy, proactive and even aligned with environmental objectives, if the right KPIs or Key Performance Indicators are established.

The relationship between data collection, Big Data analytics and the KPIs used forms a strategic triangle that transforms management into measurable action. Thanks to KPIs, recycling centers measure the efficiency and environmental impact of their operations: total volume of waste managed; cost of each ton treated; rate of waste recycled; proportion of waste diverted from landfill; percentage of waste selected for recycling; emissions avoided; average sorting time achieved by each type of waste; efficiency of raw materials recovered, operational metrics such as storage time and number of material outlets, etc. These KPIs help to evaluate the environmental performance and economic viability of the recycling processes.

With these KPIs, managers not only help to improve routes, but also optimize resources and processes; they are better prepared to ensure compliance with environmental regulations and, where appropriate, to justify and argue for subsidy applications.

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Using sensors, IoT devices capture data from their environment. Using available network connections, they send this data to a public or private cloud system (device-system-device) or to another device (device-device), or store it locally according to instructions for processing. After analyzing the accumulated data, valuable information is obtained to safely inform actions and decisions (Source: SAP.com).

Current analytical techniques in waste management
Modern techniques include the use of sensors with which to transmit information via the Internet (IoT or Internet of Things technology). Then, specific software for analyzing Big Data or large volumes of data uses analysis models based on machine learning or artificial intelligence-driven machine learning.

Machine learning is essential in contemporary waste management. It is used to sort materials in an automated manner using machine vision and robotics, with the ultimate goal of optimizing recycling efficiency.

Use cases and practical applications
At PICVISA we commercialize solutions that use data analytics for waste management. Our mission is to help recycling plants to increase their productivity with technological solutions capable of obtaining a large amount of data. For example, our EcoFlow solution, a flow analyzer based on Artificial Intelligence, applies a deep learning algorithm to a machine vision system to allow a perfect classification of the materials to be recycled and obtain valuable data for customer decision making.

And we offer our customers a DATA+ service that enables recycling plants to be 100% digitized and connected to Industry 4.0, analyzing Big Data to improve the control, efficiency and profits of these plants, while increasing process reliability by minimizing human error and providing information in real time. Specifically, Indumetal Recyclinga global leader in the recycling of electronic and metal waste, has revolutionized its operations by integrating PICVISA’s technology. Real-time waste analysis has transformed its processes, improving material recovery, optimizing efficiency and strengthening sustainability.

Insights for greater efficiency
Defining clear objectives, selecting the right tools and prioritizing data quality and cleanliness is essential to increase efficiency in waste management and obtain the best recycling rates. Undoubtedly, a successful data analysis applied to waste management manages to design a transparent, efficient and aligned strategy with the circular economy. That is our great challenge.