scholarly journals Thermal Conductivity Modeling of Aqueous CuO Nanofluids by Adaptive Neuro-Fuzzy Inference System (ANFIS) Using Experimental Data

2017 ◽  
Vol 62 (2) ◽  
pp. 202 ◽  
Author(s):  
Mohammad Hemmat Esfe
Keyword(s):  
Thermal Conductivity ◽  
Input Data ◽  
Fuzzy Inference ◽  
Regression Coefficient ◽  
Good Precision ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Triangular Function ◽  
Best Responses ◽  
Conductivity Modeling

In this article, thermal conductivity data of aqueous nanofluids of CuO have been modeled through one of the instruments of empirical data modeling. The input data of 5 different volume fractions of nanofluid obtained in four temperatures through experiments have been considered as network inputs. Also, triangular function, due to providing the best responses, has been used as membership function in ANFIS structure. The modeling results show that fuzzy networks are able to model thermal conductivity results of nanofluids with good precision. Regression coefficient of this modeling has been 0.99.

APLIKASI SISTEM CERDAS UNTUK PREDIKSI ENERGI LISTRIK PEMAKAIAN SENDIRI DI PT INDONESIA POWER SUB UNIT PLTA KABUPATEN WONOGIRI

2017 ◽  
Vol 8 (2) ◽  
pp. 489
Author(s):  
Herliyani Hasanah ◽  
Nurmalitasari Nurmalitasari
Keyword(s):  
Fuzzy Inference System ◽  
Input Data ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy Input

Kebutuhan akan energi listrik menjadi kebutuhan primer nasional. Dalam keberlangsungan proses produksi energi listrik pada pembangkitan – pembangkitan diperlukan energi listrik untuk pemakaian sendiri. Dalam penelitian ini dibangun sebuah aplikasi sistem cerdas untuk memprediksi energi listrik pemakaian sendiri di PT Indonesia Power sub unit PLTA Wonogiri. Pada penelitian ini menggunakan 2 kelompok input, yaitu input FIS (Fuzzy Inference System) dan input pada NN (Neuro Fuzzy). Input data  merupakan data produksi harian energi listrik di PLTA Wonogiri selama kurun waktu 2010 – 2016. Variabel data yang digunakan dalam penelitian ini adalah data produksi listrik untuk pemakaian PLTA Wonogiri adalah energi listrik yang dihasilkan PLTA Wonogiri dengan satuan KwH (f), elevasi muka air waduk dengan satuan meter (a1) dan debit air yang masuk ke turbin dengan satuan /detik (a2).  Output yang diperoleh adalah pusat centroid (m), derajat keanggotaan (mf), bobot (w) dan konsekuen parameter ( c ). Dari hasil pengujian diperoleh keluaran dengan performansi yang optimal pada saat Fuzzy C Means 2 kelas dengan parameter laju pembelajaran 0.4, momentum 0.6 dengan bessar Mean Percentage Error 0.377970875.  Kata kunci:  prediksi, pemakaian sendiri, energi listrik, fuzzy inference system, neuro fuzzy

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.

scholarly journals A neuro-fuzzy model to predict the inflow to the guardialfiera multipurpose dam (Southern Italy) at medium-long time scales

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
L.F. Termite ◽  
F. Todisco ◽  
L. Vergni ◽  
F. Mannocchi
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Input Data ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Computational Technique ◽  
Inference System ◽  
The Past ◽  
Neuro Fuzzy ◽  
Multipurpose Dam

Intelligent computing tools based on fuzzy logic and artificial neural networks have been successfully applied in various problems with superior performances. A new approach of combining these two powerful tools, known as neuro-fuzzy systems, has increasingly attracted scientists in different fields. Few studies have been undertaken to evaluate their performances in hydrologic modeling. Specifically are available rainfall-runoff modeling typically at very short time scales (hourly, daily or event for the real-time forecasting of floods) with in input precipitation and past runoff (i.e. inflow rate) and in few cases models for the prediction of the monthly inflows to a dam using the past inflows as input. This study presents an application of an Adaptive Network-based Fuzzy Inference System (ANFIS), as a neuro-fuzzy-computational technique, in the forecasting of the inflow to the Guardialfiera multipurpose dam (CB, Italy) at the weekly and monthly time scale. The latter has been performed both directly at monthly scale (monthly input data) and iterating the weekly model. Twenty-nine years of rainfall, temperature, water level in the reservoir and releases to the different uses were available. In all simulations meteorological input data were used and in some cases also the past inflows. The performance of the defined ANFIS models were established by different efficiency and correlation indices. The results at the weekly time scale can be considered good, with a Nash- Sutcliffe efficiency index E = 0.724 in the testing phase. At the monthly time scale, satisfactory results were obtained with the iteration of the weekly model for the prediction of the incoming volume up to 3 weeks ahead (E = 0.574), while the direct simulation of monthly inflows gave barely satisfactory results (E = 0.502). The greatest difficulties encountered in the analysis were related to the reliability of the available data. The results of this study demonstrate the promising potential of ANFIS in the forecasting of the short term inflows to a reservoir and in the simulation of different scenarios for the water resources management in the longer term.

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.

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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