Machine learning for cleaner production in port of Casablanca.

Ports serves all the globe to receive all their materials needs but it consumes a huge energy (fuel, diesel) for handling heavy cargo then it produces an important unclean emission of CO2 making serious air pollution influencing our One earth, our One environment, our One society and our One health and it's the appropriate time to appreciate the importance of breathing a clean Diamond Air. The present paper aims to reduce unclean production of CO2 emission of port crane and improve the clean air gains, so authors studies the case of the most relevant Atlantic port located in Casablanca at Morocco and uses its operational data about daily energy of 11 RTGs crane collected for the last two years. In order to understand the behavior of this energy consumption, the paper analyzes its data using machine learning tool: regression analysis statistical method to find out the factors influencing the production and the degree of influence then authors treats factors highly influencing by introducing inexpensive strategies and practices to large investment in cleaner air. Finally, this work contributes on large reduction of energy with a gain of 674.520 Euro/year and reduce 1 907,928 tons of CO2 emission/year of tones Co2 emission/year, with these practices port of Casablanca can be on sustainable clean development. • The paper promotes clean production in ports as well as promoting good health and well-being, as advocated in the 17 Sustainable Development Goals developed by the United Nations. • A robust machine learning model was proposed to reduce unclean port production of Casablanca, Morocco. • Energy saving increased to 50% and Co2 emission decreased to 50%. [ABSTRACT FROM AUTHOR]