Multi-objective optimization on total cost and carbon dioxide emission of coal supply for coal-fired power plants in Indonesia

2021 ◽  
pp. 101185
Author(s):  
Firly Rachmaditya Baskoro ◽  
Katsuhiko Takahashi ◽  
Katsumi Morikawa ◽  
Keisuke Nagasawa
2013 ◽  
Vol 856 ◽  
pp. 338-342 ◽  
Author(s):  
Chin Yee Sing ◽  
Mohd Shiraz Aris

Burning fossil fuel like coal in power plants released carbon dioxide that had been absorbed millions of years ago. Unfortunately, excessive carbon dioxide emission had led to global warming. Malaysia, as one of the major exporters of palm oil, has abundant oil palm mill residues that could be converted into value-added product like biomass fuel briquettes. Fuel briquette with palm kernel shell and palm mesocarp fibre as its main ingredients showed satisfactory fuel characteristics and mechanical properties as a pure biomass fuel briquette. The effects of adding some coal of higher calorific value to the satisfactory biomass fuel briquette were focused in this study. Various coal-biomass fuel blends were used, ranging from 0wt% coal to 50wt% coal. The fuel properties and mechanical properties of pure biomass briquette and briquettes with different amount of coal added were compared experimentally. From the fuel properties tests, it was found that as the coal content in the briquette was increased, the carbon content and calorific value increased. Mechanical property tests on the fuel briquettes showed a mixture of results, with some favored higher portion of coal in the briquette for better handling, transport and storage properties while some favored greater amount of biomass.


2014 ◽  
Vol 494-495 ◽  
pp. 1715-1718
Author(s):  
Gui Li Yuan ◽  
Tong Yu ◽  
Juan Du

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.


2015 ◽  
Vol 88 ◽  
pp. 335-346 ◽  
Author(s):  
Joan Carreras ◽  
Dieter Boer ◽  
Gonzalo Guillén-Gosálbez ◽  
Luisa F. Cabeza ◽  
Marc Medrano ◽  
...  

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