Thermodynamic Studies of Two Different Inclusion Compounds with the Same Guest:  Toward a General Understanding of Melting Behavior in Binary Compounds

1998 ◽  
Vol 10 (3) ◽  
pp. 833-839 ◽  
Author(s):  
Mary Anne White ◽  
R. Shane Harnish
Keyword(s):  
Inclusion Compounds ◽  
Melting Behavior ◽  
Thermodynamic Studies ◽  
Binary Compounds

Modeling fine particles and alkali metal compound behavior in a kraft recovery boiler

TAPPI Journal ◽  
2012 ◽  
Vol 11 (7) ◽  
pp. 9-14 ◽  
Author(s):  
AINO LEPPÄNEN ◽  
ERKKI VÄLIMÄKI ◽  
ANTTI OKSANEN
Keyword(s):  
Alkali Metal ◽  
Computational Models ◽  
Fine Particles ◽  
Black Liquor ◽  
Melting Behavior ◽  
Metal Compound ◽  
Effect Of Temperature ◽  
Particle Model ◽  
Boiler Furnace ◽  
Recovery Boiler

Under certain conditions, ash in black liquor forms a locally corrosive environment in a kraft recovery boiler. The ash also might cause efficiency losses and even boiler shutdown because of plugging of the flue gas passages. The most troublesome compounds in a fuel such as black liquor are potassium and chlorine because they change the melting behavior of the ash. Fouling and corrosion of the kraft recovery boiler have been researched extensively, but few computational models have been developed to deal with the subject. This report describes a computational fluid dynamics-based method for modeling the reactions between alkali metal compounds and for the formation of fine fume particles in a kraft recovery boiler furnace. The modeling method is developed from ANSYS/FLUENT software and its Fine Particle Model extension. We used the method to examine gaseous alkali metal compound and fine fume particle distributions in a kraft recovery boiler furnace. The effect of temperature and the boiler design on these variables, for example, can be predicted with the model. We also present some preliminary results obtained with the model. When the model is developed further, it can be extended to the superheater area of the kraft recovery boiler. This will give new insight into the variables that increase or decrease fouling and corrosion

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

Aggregation Behavior and Thermodynamic Studies of Hydrotropes: A Review

2020 ◽  
Vol 57 (3) ◽  
pp. 192-202 ◽  
Author(s):  
Akash D. Patel ◽  
Meghal A. Desai
Keyword(s):  
Aggregation Behavior ◽  
Thermodynamic Studies

Thermodynamic Studies of SN38 Hydrolysis in the Presence of Human and Bovine Serum Albumin

2011 ◽  
Vol 1 (2) ◽  
pp. 140-151 ◽  
Author(s):  
Balasubramanian Sivakumar ◽  
Rishi Thakur ◽  
Sasank Kunadharaju ◽  
Michalakis Savva
Keyword(s):  
Bovine Serum Albumin ◽  
Serum Albumin ◽  
Bovine Serum ◽  
Thermodynamic Studies
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