Waste management optimization with NLP modeling and waste-to-energy in a circular economy

• Published in: Scientific reports Vol. 14; no. 1; pp. 19859 - 24
• Main Authors: Hernández-Romero, Ilse María, Niño-Caballero, Javier Camilo, González, Lucy T., Pérez-Rodríguez, Michael, Flores-Tlacuahuac, Antonio, Montesinos-Castellanos, Alejandro
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.08.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705; 639/705/1041; 639/705/1042; Anaerobic digestion; Carbon dioxide; Carbon dioxide emissions; Circular economy; Decision making; Decision-making framework; Economics; Emissions; Emissions control; Energy conversion; Gasification; Humanities and Social Sciences; Incineration; Linear programming; Multi-objective optimization; multidisciplinary; Municipal Solid Waste (MSW); Pyrolysis; Science; Science (multidisciplinary); Social Carbon Cost (SCC); Sustainability management; Sustainable energy; Sustainable waste management; Waste management; Waste to energy; Municipal Solid Waste (MSW); Waste- to-Energy; Multi-objective optimization; Decision-making framework; Energy conversion; Social Carbon Cost (SCC)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-69321-7

This work presents a methodology integrating Non-Linear Programming (NLP) for multi-objective and multi-period optimization, addressing sustainable waste management and energy conversion challenges. It integrates waste-to-energy (WtE) technologies such as Anaerobic Digestion (AD), Incineration (Inc), Gasification (Gsf), and Pyrolysis (Py), and considers thermochemical, technical, economic, and environmental considerations through rigorous non-linear functions. Using Mexico City as a case study, the model develops waste management strategies that balance environmental and economic aims, considering social impacts. A trade-off solution is proposed to address the conflict between objectives. The economical optimal solution generates 1.79M$ with 954 tons of CO 2 emissions while the environmental one generates 0.91M$ and reduces emissions by 54%, where 40% is due to gasification technology. Moreover, the environmentally optimal solution, with incineration and gasification generates 9500 MWh/day and 5960 MWh/day, respectively, demonstrates the capacity of the model to support sustainable energy strategies. Finally, this work presents an adaptable framework for sustainable waste management decision-making.