scholarly journals Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rasool Pelalak ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Mashallah Rezakazemi ◽  
Saeed Shirazian
Keyword(s):  
Machine Learning ◽  
Bubble Column ◽  
Input Parameter ◽  
Bubble Column Reactor ◽  
Learning Approach ◽  
Membership Functions ◽  
Column Reactor ◽  
Input And Output ◽  
Machine Learning Approach ◽  
Anfis Method

AbstractTo understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas–liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.

scholarly journals Functional input and membership characteristics in the accuracy of machine learning approach for estimation of multiphase flow

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
Saeed Shirazian
Keyword(s):  
Learning Process ◽  
Liquid Velocity ◽  
Bubble Column ◽  
Three Dimensional ◽  
Bubble Column Reactor ◽  
Membership Functions ◽  
Turbulence Flow ◽  
Machine Learning Approach ◽  
Anfis Method ◽  
Prediction Capability

Abstract In the current study, Artificial Intelligence (AI) approach was used for the learning of a physical system. We applied four inputs and one output in the learning process of AI. In the learning process, the inputs are space locations of a BCR (bubble column reactor), which are x, y, and z coordinate as well as the amount of gas fraction in BCR. The liquid velocity is also considered as output. A variety of functions were used in learning, such as gbellmf and gaussmf functions, to examine which functions can give the best learning. At the end of the study, all of the results were compared to CFD (computational fluid dynamics). A three-dimensional (3D) BCR was used in this research, and we studied simulation by CFD as well as AI. The data from CFD in a 3D BCR was studied in the AI domain. In AI, we tuned for various parameters to achieve the best intelligence in the system. For instance, different inputs, different membership functions, different numbers of membership functions were used in the learning process. Moreover, the meshless prediction was used, meaning that some data in the BCR have not participated in the learning, and they were predicted in the prediction process, which gives us a special capability to compare the results with the CFD outcomes. The findings showed us that AI can predict the CFD results, and a great agreement was achieved between CFD computing nodes and AI elements. This novel methodology can suggest a meshless and multifunctional AI model to simulate the turbulence flow in the BCR. For further evaluation, the ANFIS method is compared with ACOFIS and PSOFIS methods with regards to model’s accuracy. The results show that ANFIS method contains higher accuracy and prediction capability compared with ACOFIS and PSOFIS methods.

scholarly journals Prediction of Multi-Inputs Bubble Column Reactor Using a Novel Hybrid Model of Computational Fluid Dynamics and Machine Learning

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Shahab Shamshirband ◽  
Ely Salwana ◽  
Kwok-wing Chau ◽  
Joseph H. M. Tah
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Bubble Column Reactor ◽  
Model Parameters ◽  
Gas Velocity ◽  
Inference System ◽  
Anfis Method ◽  
Input Variables

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system  (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

scholarly journals Sensitivity Study of ANFIS Model Parameters to Predict the Pressure Gradient with Combined Input and Outputs Hydrodynamics Parameters in the Bubble Column Reactor

2019 ◽  
Author(s):  
Shahab Shamshirband ◽  
Amir Mosavi ◽  
Kwok-wing Chau
Keyword(s):  
Multiphase Flow ◽  
Pressure Gradient ◽  
Learning Process ◽  
Bubble Column ◽  
Bubble Column Reactor ◽  
Model Parameters ◽  
Function Type ◽  
Column Reactor ◽  
Anfis Method ◽  
Inputs And Outputs

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

scholarly journals Sensitivity Study of ANFIS Model Parameters to Predict the Pressure Gradient with Combined Input and Outputs Hydrodynamics Parameters in the Bubble Column Reactor

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Shahab Shamshirband ◽  
Kwok-wing Chau
Keyword(s):  
Pressure Gradient ◽  
Learning Process ◽  
Chemical Engineering ◽  
Bubble Column ◽  
Bubble Column Reactor ◽  
Model Parameters ◽  
Function Type ◽  
Column Reactor ◽  
Anfis Method ◽  
Inputs And Outputs

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the ANFIS method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to  almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs.

scholarly journals Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Azam Marjani ◽  
Saeed Shirazian
Keyword(s):  
Liquid Velocity ◽  
Bubble Column ◽  
Machine Learning Algorithms ◽  
Column Height ◽  
Bubble Column Reactor ◽  
Data Set ◽  
Inference System ◽  
Column Reactor ◽  
Anfis Method ◽  
Cfd Method

AbstractFor understanding the complex behavior of fluids in a multiphase chemical bubble column reactor, a combination of the computational fluid dynamic (CFD) method and the adaptive network-based fuzzy inference system (ANFIS) method is used to predict bubble flow inside a reactor based on the function of column height. In this study, the Euler–Euler model is employed as a CFD method. In the Eulerian method, continuity and momentum governing equations are mathematically computed for each phase, while the equations are connected together by source terms. After calculating the flow pattern and turbulence flow in the reactor, all data sets are used to prepare a fully artificial method for further prediction. This algorithm contains different learning dimensions such as learning in different directions of reactor or large amount of input parameters and data set representing “big data”. The ANFIS method was evaluated in three steps by using one, two, and three inputs in each one to predict the liquid velocity in the x-direction (Ux). The x, y, and z coordinates of the location of the node of the liquid were considered as the inputs. Different percentages of data and various iterations and membership functions were used for training in the ANFIS method. The ANFIS method showed the best prediction using three inputs. This combination also shows the ability of computer science and computational methods in learning physical and chemical phenomena.

scholarly journals Outomatiese lemma-identifisering vir Afrikaans

Literator ◽  
2008 ◽  
Vol 29 (1) ◽  
pp. 65-92
Author(s):  
H.J. Groenewald ◽  
G.B. Van Huyssteen
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Text Processing ◽  
Learning Approach ◽  
Input And Output ◽  
Base Form ◽  
Alternative Approach ◽  
Machine Learning Approach ◽  
Word Forms ◽  
Future Work

Automatic lemmatisation for Afrikaans Automatic lemmatisation is a general normalisation procedure in text processing, where all inflected forms of a lexical word are normalised to a single lemma (i.e. a meaningful, uninflected base form from which more complex word forms could be formed). Traditionally, lemmatisers are developed by writing language-specific rules to identify lemmas. In this article an alternative approach is investigated, namely a machine learning approach, to develop a lemmatiser for Afrikaans (LIA: “Lemmaidentifiseerder vir Afrikaans”). An overview regarding the process of inflection in Afrikaans is provided with the aim of identifying the categories of inflection that are relevant for lemmatisation in Afrikaans. The format of the input and output is described with special reference to the nine inflectional categories for Afrikaans that the system should be able to handle. Then the task of lemmatisation as a classification task for machine learning is described, and a concise introduction to memory-based learning is provided. The development and evaluation of LIA is discussed in detail, and it is illustrated how the performance of the initial classifier is improved through feature selection and parameter optimisation. The best classifier reaches an accuracy of 92,8%. The article concludes with a view on some future work.

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