OPTIWASTE, AI Engine, Deep Learning and Machine Learning for Waste Logistics Optimization

Project description

The project seeks to minimize the economic cost, CO2 impact per Kg managed and optimization of the recyclability and consequent "circularity" of the waste managed in selective collection processes through the application of massive data analysis technologies, AI Artificial Intelligence, deep learning and machine learning.

It is intended through technological innovation to solve 3 objectives priorities in today's society:

• Environmental and sustainability objective: Reduce CO2 per Kg of waste managed
• Economic objective and economic sustainability: Reduce the economic cost per KG managed in terms of transport and final disposal: minimization of the necessary extra cost per new product and its negative impact on inflation (CPI).
• "Circularity" objective: improve the recyclability of managed waste and its reintegration into the production process, increasing the quality of the residue obtained by optimizing the logistics and custody cycle.

The project is based on the process of massive analysis of information from the databases data with transactional data and collection of waste generated for more than 17 years.

From this first phase:

• A forecast algorithm will be modeled mathematically.
• A logistics optimization will be designed.
• The most innovative AI artificial intelligence technologies will be applied, machine learning and deep learning, to obtain a forecast algorithm of the demand and evolutionary optimization of the logistics cycle.

The project involves the development of a supervised Machine Learning model, with predictive algorithms. The need to apply learning techniques as Supervised or Natural Language Processing (NLP) will be evaluated if necessary.

Contact data:

For further information, please contact:

Dr. Susana Val, Principal Investigator sval@zlc.edu.es

This project has received funding from the Ministry of Industry, Commerce and Tourism (Funding Program established for the support of Innovative Business Groups in 2022). Project reference AEI-010500-2022-90.