A text mining framework for screening catalysts and critical process parameters from scientific literature - A study on Hydrogen production from alcohol

Kumar, Amit; Ganesh, Swathi; Gupta, Dileep Kumar; Kodamana, Hariprasad

doi:10.1016/j.cherd.2022.05.018

Cited by 15 publications

(17 citation statements)

References 35 publications

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“…At first, the algorithm compares V E A and V E B to transform them into the same (ll. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Here, we define between-shared variables V bs as the variables shared between E A and E B , and within-shared variables V ws,E i as the variables shared between the equations within an equation group E i (ll.…”

Section: Equivalence Judgment Of Equation Groupsmentioning

confidence: 99%

“…Here, we define between-shared variables V bs as the variables shared between E A and E B , and within-shared variables V ws,E i as the variables shared between the equations within an equation group E i (ll. [1][2][3]. V E A and V E B are transformed into the same by eliminating the variables appearing only in either equation group.…”

Section: Equivalence Judgment Of Equation Groupsmentioning

confidence: 99%

“…Before building the model, the researcher surveys previous studies and identifies the differences among the multiple models in the previous studies, which is an arduous task. Several studies have dealt with a text in the chemical engineering field using natural language processing techniques [2][3][4] , but no studies have aimed to reduce this kind of effort.…”

Section: Introductionmentioning

confidence: 99%

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Simple Algorithm for Judging Equivalence of Differential-Algebraic Equation Systems

Kato

Zhang

Kano

2022

Preprint

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Mathematical formulas play a prominent role in science, technology, engineering, and mathematics (STEM) documents; understanding STEM documents usually requires knowing whether equation groups containing multiple equations are equivalent. However, existing tools cannot judge the equivalence of two equation groups; thus, we develop an algorithm to judge such an equivalence using a computer algebra system. The proposed algorithm first eliminates variables appearing only in either equation group. It then checks the equivalence of the equations one by one. If each equation in one equation group is equivalent to an equation in the other, the equation groups are judged equivalent; otherwise, non-equivalent. We generated 50 pairs of equation groups for evaluation. The proposed method accurately judged the equivalence of all pairs. This method is expected to facilitate comprehension of a large amount of mathematical information in STEM documents. Furthermore, this is a necessary step for machines to understand equations, including process models.

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Section: Equivalence Judgment Of Equation Groupsmentioning

confidence: 99%

Section: Equivalence Judgment Of Equation Groupsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simple Algorithm for Judging Equivalence of Differential-Algebraic Equation Systems

Kato

Zhang

Kano

2022

Preprint

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“…Alternatively, one can use data-driven machine learning (ML) approaches to model the underlying trend of reaction kinetics if sufficient and representative data are available from the experiments. , A critical impediment in this direction is the availability of flexible machine learning models that can capture the tricky hidden underlying trend in the experimental data sets with sufficient accuracy. Further, machine learning and applications in catalysis are witnessing a progressive trend in recent times. − …”

Section: Introductionmentioning

confidence: 99%

“…Also, GPR has an impeccable ability to capture the hidden trends in sparse data sets. ,− In this study, we extensively leverage the prediction capabilities of GPR by tuning hyperparameters and kernels. The results of GPR are compared to those of deep neural nets, support vector machines, and regression models, among other machine learning methods …”

Section: Introductionmentioning

confidence: 99%

Multiobjective Bayesian Optimization Framework for the Synthesis of Methanol from Syngas Using Interpretable Gaussian Process Models

et al. 2022

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Methanol production has gained considerable interest on the laboratory and industrial scale as it is a renewable fuel and an excellent hydrogen energy storehouse. The formation of synthesis gas (CO/H 2 ) and the conversion of synthesis gas to methanol are the two basic catalytic processes used in methanol production. Machine learning (ML) approaches have recently emerged as powerful tools in reaction informatics. Inspired by these, we employ Gaussian process regression (GPR) to the model conversion of carbon monoxide (CO) and selectivity of the methanol product using data sets obtained from experimental investigations to capture uncertainty in prediction values. The results indicate that the proposed GPR model can accurately predict CO conversion and methanol selectivity as compared to other ML models. Further, the factors that influence the predictions are identified from the best GPR model employing "Shapley Additive exPlanations" (SHAP). After interpretation, the essential input features are found to be the inlet mole fraction of CO (Y(CO, in)) and the net inlet flow rate (Fin(nL/min)) for our best prediction GPR models, irrespective of our data sets. These interpretable models are employed for Bayesian optimization in a weighted multiobjective framework to obtain the optimal operating points, namely, maximization of both selectivity and conversion.

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Advances in materials informatics: a review

Sivan,

Satheesh Kumar,

Abdullah

et al. 2024

J Mater Sci

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A text mining framework for screening catalysts and critical process parameters from scientific literature - A study on Hydrogen production from alcohol

Cited by 15 publications

References 35 publications

Simple Algorithm for Judging Equivalence of Differential-Algebraic Equation Systems

Simple Algorithm for Judging Equivalence of Differential-Algebraic Equation Systems

Multiobjective Bayesian Optimization Framework for the Synthesis of Methanol from Syngas Using Interpretable Gaussian Process Models

Advances in materials informatics: a review

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