Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Enhancing green ports in Dar es Salaam Port: facility optimization for emission reduction through Mamdani and Sugeno Fuzzy inference systems
This study rigorously assesses emissions from diverse equipment at Dar es Salaam Port, analyzing CO, NOx, SO2, PM10, and POC emissions across various areas. Detailed data collection includes machine specifications, and calculated emission factors that facilitate precise analysis. The research design includes both evaluation of emissions and a strategic phase for optimizing equipment towards reduction. This study employs Mamdani and Sugeno Fuzzy Inference Systems (FIS) to comprehensively analyze emissions from diverse equipment within Dar es Salaam Port. The FIS enhances precision in emission reduction target-setting by considering the intricate parameters, unique to each equipment type. In 2022, the cumulative emissions of CO, NOx, SO2, PM10, and POC amounted to 185,163, 92,908.4, 40,842.4, 8,067.53, and 9,178.614 pounds, respectively, forming a basis for evaluating sustainability initiatives. Strategic interventions are delineated for each equipment type, from advanced technologies for Rubber-Tired Gantry Cranes (RTG) and systematic replacements for Forklifts. Overarching initiatives include regulatory frameworks, alternative fuels, and technology transitions. The FIS models specify emission reduction targets, such as Mamdani proposing a reduction of 12,504.51 pounds of CO from Berthing Tugs, and Sugeno suggesting 3,751.353 pounds. These nuanced recommendations integrate into a strategic roadmap, guiding Dar es Salaam Port towards a sustainable future.
Enhancing green ports in Dar es Salaam Port: facility optimization for emission reduction through Mamdani and Sugeno Fuzzy inference systems
This study rigorously assesses emissions from diverse equipment at Dar es Salaam Port, analyzing CO, NOx, SO2, PM10, and POC emissions across various areas. Detailed data collection includes machine specifications, and calculated emission factors that facilitate precise analysis. The research design includes both evaluation of emissions and a strategic phase for optimizing equipment towards reduction. This study employs Mamdani and Sugeno Fuzzy Inference Systems (FIS) to comprehensively analyze emissions from diverse equipment within Dar es Salaam Port. The FIS enhances precision in emission reduction target-setting by considering the intricate parameters, unique to each equipment type. In 2022, the cumulative emissions of CO, NOx, SO2, PM10, and POC amounted to 185,163, 92,908.4, 40,842.4, 8,067.53, and 9,178.614 pounds, respectively, forming a basis for evaluating sustainability initiatives. Strategic interventions are delineated for each equipment type, from advanced technologies for Rubber-Tired Gantry Cranes (RTG) and systematic replacements for Forklifts. Overarching initiatives include regulatory frameworks, alternative fuels, and technology transitions. The FIS models specify emission reduction targets, such as Mamdani proposing a reduction of 12,504.51 pounds of CO from Berthing Tugs, and Sugeno suggesting 3,751.353 pounds. These nuanced recommendations integrate into a strategic roadmap, guiding Dar es Salaam Port towards a sustainable future.
Enhancing green ports in Dar es Salaam Port: facility optimization for emission reduction through Mamdani and Sugeno Fuzzy inference systems
Majid Mohammed Kunambi (Autor:in) / Hongxing Zheng (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Online bridge linguistic monitoring methods based on Mamdani fuzzy inference systems
| British Library Conference Proceedings | 2004
Application of Fuzzy Inference System Mamdani at Pelican Crossing
| Springer Verlag | 2022
Sustainability Ranking of Desalination Plants Using Mamdani Fuzzy Logic Inference Systems
| DOAJ | 2020
Implementation of Mamdani and Sugeno Method for Load Forecasting: A Case Study of Malang City
| BASE | 2018