Selecting Robust Kinetic Models on Noisy Data Using the Bootstrap

Author(s):  
Cayle J Sharrock ◽  
Roelof Coetzer

A systematic approach to identifying a robust kinetic model fitted on noisy data is presented. The bootstrap coupled with Monte-Carlo simulations and exploratory data analysis techniques are employed to evaluate candidate model formulations to given sets of experimental data. The approach is applied in an industrial case study in determining the most practical rate expression for the water-gas shift reaction over a cobalt Fischer-Tropsch catalyst.

Exploratorydata analysis is a method to summarize main characteristics of data, and also to understand data more deeply using visualization techniques. This paper focuses on defining systematic approach in the form of well-defined sequence of steps to explore data in various aspects. Every organization produces lot of data. Organization needs to analyze this data very carefully to extract hidden patterns in the data. Task Centric EDA[2]produces actionable insights as outcome to improve business process.This uses Pythonprogramming language and Jupyter Notebook for data analysis. Python is an object oriented and interactive programming language, which contains rich sets of libraries likepandas, MATplotlib, seaborn[10]etc. We have used different types of charts and various types of parameters to analyze retail dataset and to improve sales using precision marketing.


Author(s):  
Maria do Carmo Rangel ◽  
Amalia Luz Costa Pereira ◽  
Gustavo Marchetti ◽  
Peterson Santos Querino ◽  
Alberto Albornoz

The effect of zirconium on the textural and catalytic properties of magnetite for the water gas shift reaction (WGSR) at high temperatures was studied in this chapter. The reaction is an important step in the industrial production of pure hydrogen. Samples with different amounts of zirconium (Zr/Fe (molar)= 0.1; 0.2;0.3; 0.4 and 0.5) were prepared from the decomposition of iron(III)hydroxoacetate doped with zirconium. It was found that zirconium increased the specific surface area of magnetite acting as spacer on the surface where it keeps the particles apart. Except for the zirconium-poorest solid, tetragonal zirconia was detected besides magnetite for all solids. Zirconium increased the intrinsic activity of the catalysts, stabilized the specific surface areas during reaction, and made the magnetite reduction to metallic iron more difficult. The zirconium-poorest is more active than magnetite and more resistant against deactivation by sintering and overreduction being attractive for WGSR.


2021 ◽  
Vol 35 (4) ◽  
pp. 3521-3528
Author(s):  
Guohui Cai ◽  
Yuanyuan He ◽  
Hongju Ren ◽  
Yingying Zhan ◽  
Chongqi Chen ◽  
...  

2020 ◽  
Author(s):  
Nore Stolte ◽  
Junting Yu ◽  
Zixin Chen ◽  
Dimitri A. Sverjensky ◽  
Ding Pan

The water-gas shift reaction is a key reaction in Fischer-Tropsch-type synthesis, which is widely believed to generate hydrocarbons in the deep carbon cycle, but is little known at extreme pressure-temperature conditions found in Earth’s upper mantle. Here, we performed extensive ab initio molecular dynamics simulations and free energy calculations to study the water-gas shift reaction. We found the direct formation of formic acid out of CO and supercritical water at 10∼13 GPa and 1400 K without any catalyst. Contrary to the common assumption that formic acid or formate is an intermediate product, we found that HCOOH is thermodynamically more stable than the products of the water-gas shift reaction above 3 GPa and at 1000∼1400 K. Our study suggests that the water-gas shift reaction may not happen in Earth’s upper mantle, and formic acid or formate may be an important carbon carrier, participating in many geochemical processes in deep Earth.<br>


2013 ◽  
Vol 33 (9) ◽  
pp. 1572-1577 ◽  
Author(s):  
Yuanzhuo ZHANG ◽  
Ziying YU ◽  
Fumin ZHANG ◽  
Qiang XIAO ◽  
Yijun ZHONG ◽  
...  

2008 ◽  
Vol 49 (4) ◽  
pp. 594-598 ◽  
Author(s):  
A. R. Dubrovskii ◽  
S. A. Kuznetsov ◽  
E. V. Rebrov ◽  
J. C. Schouten

Author(s):  
Nore Stolte ◽  
Junting Yu ◽  
Zixin Chen ◽  
Dimitri A. Sverjensky ◽  
Ding Pan

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