Wednesday, May 30, 2018

Is It Opera … Or Is It Operando?


A recent blog post by Haldor Topsoe researcher Dr. Pablo Beato reports progress in operando spectroscopy.

Quoting from his post …

I inherited the responsibility for the optical spectroscopy in Topsoe in 2010. Since then, I have been trying to further develop the methods and infrastructure to monitor our catalysts in dedicated reactors while they are actually working. This type of methodology is often called “operando spectroscopy” and has become a dedicated field in catalysis research. It is based on the idea that catalysts, and in particular the surface of catalysts, are very dynamic under reaction conditions, and, if we really want to understand the active state of a catalyst, we need to look at it while it is doing its job. That is of course easier said than done.
Ideally, we would use a conventional reactor, which is also used for lab-scale catalytic testing, and modify it to get access to the catalyst surface with an optical probe (a laser or other light source). However, in many cases we have to design new types of reactors that allow us to combine the catalytic testing with spectroscopy. The operando methodology is not limited to spectroscopy techniques related to catalysis, but also includes diffraction and microscopy techniques that are used to characterize functional materials while they are performing under realistic conditions.
source: https://blog.topsoe.com/from-science-to-dollars-operando-methodology?utm_source=hs_email&utm_medium=email&utm_content=63186794&_hsenc=p2ANqtz-9dWKekMApNv9OCFgf6ExFvov7D-a-dc6ZDBKBSAHyPvlNHwOqAR3MI1vnBnxJHJftqouCgg8qQ3zbGnSXnuBhIzuDshA&_hsmi=63186794
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According to Wikipedia …
Operando spectroscopy is a class of methodology, rather than a specific spectroscopic technique such as FTIR or NMR. Operando spectroscopy is a logical technological progression in in situ studies. Catalyst scientists would ideally like to have a "motion picture" of each catalytic cycle, whereby the precise bond-making or bond-breaking events taking place at the active site are known; this would allow a visual model of the mechanism to be constructed. The ultimate goal is to determine the structure-activity relationship of the substrate-catalyst species of the same reaction. Having two experiments—the performing of a reaction plus the real-time spectral acquisition of the reaction mixture—on a single reaction facilitates a direct link between the structures of the catalyst and intermediates, and of the catalytic activity/selectivity. Although monitoring a catalytic process in situ can provide information relevant to catalytic function, it is difficult to establish a perfect correlation because of the current physical limitations of in situ reactor cells. Complications arise, for example, for gas phase reactions which require large void volumes, which make it difficult to homogenize heat and mass within the cell. The crux of successful operando methodology, therefore, is related to the disparity between laboratory setups and industrial setups, i.e., the limitations of properly simulating the catalytic system as it proceeds in industry.
source: https://en.wikipedia.org/wiki/Operando_spectroscopy

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TIP: Search Google® Scholar using the phrase Pablo beato haldor

Here is one of the results from the search …

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Exploring Scaling Relations for Chemisorption Energies on Transition‐Metal‐Exchanged Zeolites ZSM‐22 and ZSM‐5
Dr. Samira Siahrostami
Dr. Hanne Falsig
Dr. Pablo Beato
Dr. Poul Georg Moses
Prof. Jens K. Nørskov
Dr. Felix Studt
First published: 12 January 2016
|https://doi.org/10.1002/cctc.201501049
Abstract
Copper exchange on all the different T sites of ZSM‐22 and ZSM‐5 is considered and the chemisorption energies of dioxygen, OH, and O species are studied. We show that for different T sites the adsorption energies vary significantly. The oxygen adsorption energy on copper‐exchanged zeolites is quite similar to those of the most selective catalysts for oxidation reactions, that is, Ag and Au surfaces. The chemisorption energies of oxygen, carbon‐, and nitrogen‐containing species on different transition metals exchanged in ZSM‐22 are also investigated. The study covers three different oxidation states, that is, 1+, 2+, and 3+ for the transition‐metal exchanges. Scaling relations are presented for the corresponding species. Chemisorption of O scales with chemisorption of OH for all three considered exchanges, whereas there are essentially rough correlations for NH2 and N as well as CH3 and source: https://onlinelibrary.wiley.com/doi/full/10.1002/cctc.201501049

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Thursday, May 24, 2018

What Can We Learn from Machines that Learn?

AI – Artificial Intelligence – is helping catalysis researchers leverage their time to produce fruitful results. Here, from Aramco et al., is one example …

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SAE Technical Paper 2018-01-0190, 2018
Ahmed Abdul Moiz, Pinaki Pal, Daniel Probst, Yuanjiang Pei, Yu Zhang, Sibendu Som, Janardhan Kodavasal
Affiliated: Argonne National Laboratory, Convergent Science Inc., Aramco Research Center
A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing
A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially-premixed compression ignition mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC). The outputs (targets) of interest from these simulations included five metrics related to engine performance and emissions. Over 2000 samples generated from CFD were then used to train an ML model that could predict these five targets based on the nine input features. A robust super learner approach was employed to build the ML model, where results from a collection of different ML algorithms were pooled together. Thereafter, a stochastic global optimization genetic algorithm (GA) was used, with the ML model as the objective function, to optimize the input parameters based on a merit function so as to minimize fuel consumption while satisfying CO and NOx emissions constraints. The optimized configuration from ML-GA was found to be very close to that obtained from a sequentially performed CFD-GA approach, where a CFD simulation served as the objective function. In addition, the overall turnaround time was (at least) 75% lower with the ML-GA approach, as the training data was generated from concurrent CFD simulations and employing the ML model as the objective function significantly accelerated the GA optimization. This study demonstrates the potential of ML-GA and high-performance computing (HPC) to reduce the number of CFD simulations to be performed for optimization problems without loss in accuracy, thereby providing significant cost savings compared to traditional approaches.
DOI: https://doi.org/10.4271/2018-01-0190
Citation: Moiz, A., Pal, P., Probst, D., Pei, Y. et al., "A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing," SAE Technical Paper 2018-01-0190, 2018, https://doi.org/10.4271/2018-01-0190.
Event: WCX World Congress Experience
source: https://www.sae.org/publications/technical-papers/content/2018-01-0190/
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TIP: Saudi Aramco is engaging in some pretty intense research. Take advantage ... in Google® Scholar, set up an email alert to follow Aramco.

It’s easy … here’s how …

  • Go to Google® Scholar (https://scholar.google.com)
  • Search for: Aramco
  • Find “Create Alert,” and click the link
  • Follow Google’s instructions


Sunday, May 20, 2018

A pinch of salt … A dash of Deepwater

Researching BP-British Petroleum?

If you want to view Deepwater related stuff, that’s easy.

On Google Scholar (https://scholar.google.com/), for example, enter the following search string …

"british petroleum" deepwater

But what if you want everything EXCEPT Deewater?

Here’s a TIP: add a DASH. The DASH stands for NOT, so your search string would be …

"british petroleum" -deepwater

The results will reflect articles written by or about British Petroleum, EXCLUDING those mentioning Deepwater.

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Jean Steinhardt served as Librarian, Aramco Services, Engineering Division, for 13 years. He now heads Jean Steinhardt Consulting LLC, producing the same high quality research that he performed for Aramco.

Follow Jean’s blog at: http://desulf.blogspot.com/ for continuing tips on effective online research
Email Jean at research@jeansteinhardtconsulting.com with questions on research, training, or anything else
Visit Jean’s Web site at http://www.jeansteinhardtconsulting.com/ to see examples of the services we can provide