Machine Learning Based Interpretation of Microkinetic Data: A Fischer-Tropsch Synthesis Case Study

2021 ◽  
Author(s):  
Anoop Chakkingal ◽  
Pieter Janssens ◽  
Jeroen Poissonnier ◽  
Alan J Barrios ◽  
Mirella Virginie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Reactions ◽  
Tropsch Synthesis ◽  
Data Driven ◽  
Fischer Tropsch ◽  
Fischer Tropsch Synthesis ◽  
Artificial Neural

Machine-Learning (ML) methods, such as Artificial Neural Networks (ANN) bring the data-driven design of chemical reactions within reach. Simultaneously with the verification of the absence of any bias in the...

Fischer–Tropsch synthesis with Co/SiO2–Al2O3 catalyst and steady-state modeling using artificial neural networks

Fuel ◽  
1998 ◽  
Vol 77 (15) ◽  
pp. 1763-1768 ◽  
Author(s):  
Bijay K Sharma ◽  
Mahendra P Sharma ◽  
Sisir Kumar Roy ◽  
Suresh Kumar ◽  
Shilpa B Tendulkar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Tropsch Synthesis ◽  
Fischer Tropsch ◽  
Fischer Tropsch Synthesis ◽  
Artificial Neural ◽  
Al2o3 Catalyst

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Abdolhossein Rezaei Nejad ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Masoumeh Ahmadi Majd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Status ◽  
Leaf Water ◽  
Leaf Water Status ◽  
Non Invasive ◽  
Color Features ◽  
Artificial Neural

Bottlenecks and potentials for the gasification of lignocellulosic biomasses and Fischer-Tropsch synthesis: A case study on the production of advanced liquid biofuels in Brazil

2021 ◽  
Vol 245 ◽  
pp. 114629
Author(s):  
Henrique Real Guimarães ◽  
Jéssica Marcon Bressanin ◽  
Ingrid Lopes Motta ◽  
Mateus Ferreira Chagas ◽  
Bruno Colling Klein ◽  
...  
Keyword(s):  
Tropsch Synthesis ◽  
Fischer Tropsch ◽  
Fischer Tropsch Synthesis

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

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