Prediction of Pressure Drop in Venturi Scrubbers by Multi-Gene Genetic Programming and Adaptive Neuro-Fuzzy Inference System

2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Hadi Esmaeili ◽  
Ali Mohebbi
Keyword(s):  
Experimental Data ◽  
Pressure Drop ◽  
Genetic Programming ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Good Precision ◽  
Inference System ◽  
Venturi Scrubber ◽  
New Approaches ◽  
Neuro Fuzzy

AbstractStudying the pressure drop in venturi scrubbers had been the subject of many types of researches due to its importance for removing pollutants from polluted gas. In this study, two new approaches based on Multi-Gene Genetic Programming (MGGP) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to predict the pressure drop in venturi scrubbers. The main parameters studied were the throat gas velocity of venturi scrubbers (Vgth), the liquid to gas flow rate ratio (L/G), and the axial distance of the venturi scrubbers (z) as the inputs to the network, while the pressure drop was as the output. One set of experimental data, which was gathered from five different venturi scrubbers including a circular and an adjustable prismatic venturi scrubber with a wetted wall irrigation, a rectangular venturi scrubber and two ejector venturi scrubbers with different throat diameters were applied for this study. The results of ANFIS and MGGP were compared with experimental data and those values from Artificial Neural Networks (ANNs) from our previous work. In this work, the coefficient of the determination (i. e. R2value) was used to show the prediction ability of these new approaches. Results showed that MGGP and ANFIS can accurately predict the pressure drop in venturi scrubbers with R2values of 0.9972 and 0.9734, respectively. The results also showed that MGGP has more precision than ANFIS and ANNs. Therefore, based on MGGP, two correlations were generated for two clusters of data. The comparison results between one of these correlations (i. e. correlation 1 with R2value equal to 0.9937) and other models showed that our correlation has a very good precision and can predict the pressure drop in a more agreement with the experimental data.

Adaptive Neuro-Fuzzy Inference System Modelling of Surface Roughness in High Speed Turning of AISI P 20 Tool Steel

2011 ◽  
Vol 314-316 ◽  
pp. 341-345
Author(s):  
Bo Di Cui
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Tool Steel ◽  
Fuzzy Inference System ◽  
High Speed ◽  
Fuzzy Inference ◽  
System Modelling ◽  
Inference System ◽  
High Speed Turning ◽  
Neuro Fuzzy

Accurate predictive modelling is an essential prerequisite for optimization and control of production in modern manufacturing environments. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the surface roughness in high speed turning of AISI P 20 tool steel. Experiments were designed and performed to collect the training and testing data for the proposed model based on orthogonal array. For decreasing the complexity of the ANFIS structure, principal component analysis (PCA) was used to deal with the experimental data. The comparison between predictions and experimental data showed that the proposed method was both effective and efficient for modelling surface roughness.

Adaptive Neuro-Fuzzy Modeling of the Thermal Conductivity of Alumina-Water Nanofluids

2012 ◽  
Author(s):  
Mehdi Mehrabi ◽  
Mohsen Sharifpur ◽  
Josua P. Meyer
Keyword(s):  
Experimental Data ◽  
Thermal Conductivity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Design Parameters ◽  
Thermal Conductivity Ratio ◽  
Conductivity Ratio ◽  
Inference System ◽  
Input Design ◽  
Neuro Fuzzy

By using on Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as experimental data, a model was established for the prediction of the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In the ANFIS the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters. In the development of the model, the empirical data was divided into train and test sections. The ANFIS network was instructed by eighty percent of the experimental data and the remaining data (twenty percent) were considered for benchmarking. The results which were obtained by the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) model were in good agreement with the experimental results.

Adaptive Neuro-Fuzzy Inference System Modeling of MRR and WIWNU in CMP Process With Sparse Experimental Data

2008 ◽  
Vol 5 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Lih Wen-Chen ◽  
S.T.S. Bukkapatnam ◽  
P. Rao ◽  
N. Chandrasekharan ◽  
R. Komanduri
Keyword(s):  
Experimental Data ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
System Modeling ◽  
Inference System ◽  
Neuro Fuzzy

Application of Multiple Regression and Adaptive Neuro-Fuzzy Inference System for Prediction of Surface Roughness in EDM

2012 ◽  
Author(s):  
S. S. Baraskar ◽  
S. S. Banwait
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Multiple Regression ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy

A manufacturing system is oriented towards higher production rate, better quality and reduced cost and time to make a product. Surface roughness is an index parameter for determining the quality of a machined product and is influenced by various input process parameters. Surface roughness prediction in Electrical Discharge Machine (EDM) is being attempted with many methodologies, yet there is a need to develop robust, autonomous and accurate predictive system. This work proposes the application of hybrid intelligent technique, multiple regression and adaptive neuro-fuzzy inference system (ANFIS) for prediction of surface roughness in EDM. An experimental data set is obtained with current, pulse-on time and pulse-off time as input parameters and surface roughness as output parameter. Central composite rotatable design was used to plan the experiments. Multiple regression model is developed using the experimental data, to generate additional input-output data set. The input-output data set is used for training and validation of the proposed technique. After validation, data are forwarded for prediction of surface roughness. The proposed hybrid model for the prediction of surface roughness has very good agreement with the experimental results.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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