operational optimization
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Energy ◽  
2021 ◽  
pp. 122558
Author(s):  
Le Wu ◽  
Ting Yan ◽  
Qingyu Lei ◽  
Shuai Zhang ◽  
Yuqi Wang ◽  
...  

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6852
Author(s):  
Grant Buster ◽  
Paul Siratovich ◽  
Nicole Taverna ◽  
Michael Rossol ◽  
Jon Weers ◽  
...  

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.


2021 ◽  
Vol 7 ◽  
pp. 244-253
Author(s):  
Jona Maurer ◽  
Oliver M. Ratzel ◽  
Albertus J. Malan ◽  
Sören Hohmann

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1377
Author(s):  
Cevat Yaman ◽  
Suriya Rehman ◽  
Tanveer Ahmad ◽  
Yusuf Kucukaga ◽  
Burcu Pala ◽  
...  

Landfills are an example of an environment that contains highly complex communities of microorganisms. To evaluate the microbial community structure, four stainless steel pilot-scale bioreactor landfills with single- and double-layered geotextile fabric were used. Two reactors (R-1 and R-2) contained municipal solid waste (MSW) and sewage sludge, while the other two reactors (R-3 and R-4) contained only MSW. A single layer of geotextile fabric (R2GT3 and R3GT3) was inserted in the drainage layers of the two reactors (R-2 and R-3), while a double layer of geotextile fabric (R4GT2 and R4GT1) was inserted in one of the reactors (R-4). Scanning electron microscopy demonstrated that biomass developed on the geotextile fabrics after 540 days of bioreactor operation. The metagenomics analyses of the geotextile samples by 16S rRNA gene sequencing indicated that the geotextile bacterial communities were dominated by the phyla Firmicutes, Bacteroidetes, and Thermotogeae, while Proteobacteria were detected as the rarest bacterial phylum in all the geotextile samples. Treponema, Caldicoprobacter, and Clostridium were the most dominant anaerobic and fermentative bacterial genera associated with the geotextile fabric in the bioreactors. Euryarchaeota was the predominant archaean phylum detected in all the geotextile samples. In the archaeal communities, Methanosarcina, and Vadin CA11 were identified as the predominant genera. The diversity of microorganisms in landfill bioreactors is addressed to reveal opportunities for landfill process modifications and associated operational optimization. Thus, this study provides insights into the population dynamics of microorganisms in geotextile fabrics used in bioreactor landfills.


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