scholarly journals Performance Comparison of the Single Axis and Two-Axis Solar System using Adaptive Neuro-Fuzzy Inference System Controls

2020 ◽  
Vol 190 ◽  
pp. 00005
Author(s):  
Chairul Imron ◽  
Imam Abadi ◽  
Ilham Amirul Akbar ◽  
Jauharotul Maknunah ◽  
Yusilawati Ahmad Nor ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Tracking System ◽  
Maximum Energy ◽  
Performance Comparison ◽  
Energy Yield ◽  
The Sun ◽  
Inference System ◽  
Solar Tracking ◽  
Neuro Fuzzy

Solar energy is one of the renewable energy that gets more attention from many countries. Solar photo voltaic (PV) takes the right position to get the maximum energy yield. The study was conducted by comparison of performance with two methods of tracking the sun with one axis and two axes by using ANFIS control (Adaptive Neuro-Fuzzy Inference System). The solar tracking system is a system that operates on the sun by using a light sensor and controls the photovoltaic to always perpendicular to the sun by changing the pitch and yaw axis of the sun tracing properties. LDR (Light Dependent Resistor) is one of the light sensors whose resistance changes depending on the intensity of incoming light. Direct current (DC )motor is used as a PV drive panel in a solar tracking system. A two-axis solar tracking system has a greater power output than a tracking system with a single photovoltaic panel that does not use a tracking system (fixed).

An experimental modelling and performance validation study: Top gas pressure tracking system in a blast furnace using soft computing methods

2021 ◽  
pp. 095440892110331
Author(s):  
Yasin Tunckaya
Keyword(s):  
Blast Furnace ◽  
Fuzzy Inference System ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Tracking System ◽  
Computing Methods ◽  
Gas Pressure ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy

The blast furnace is a master iron-producing plant of iron and steel factories and affected by several process parameters as well as top gas pressure , which is a key process control phenomenon to maintain stability and operational productivity in such plants. Blast furnace operation is not tolerant to any interruption, unbalanced operations, momentary disturbances or loss of control due to its nature of intensive chemical reactions and heat balance requirements. Consequently, it is crucial to monitor and control top gas system components of the furnace with instrumentation measurements to maintain stable, efficient operation and system safety ongoing. In this study, a novel top gas pressure tracking system is developed using the chronologically obtained live process data of Erdemir BF#2 in Turkey. Eight process parameters are considered as input parameters as per the plant maintenance team's recommendations and soft computing methods, artificial neural networks and adaptive neuro fuzzy inference system are employed and a statistical regression tool, autoregressive integrated moving average, is also applied for comparison. Performance and success ratio analysis is carried out using coefficient of determination ( R2), mean absolute percentage error and root mean squared error terms. The best performing model output for the adaptive neuro fuzzy inference system is found to be 0.95, 1.21 and 0.023, and slightly lower performance is obtained for the artificial neural network model with the output values of 0.94, 0.029 and 1.32 against R2, mean absolute percentage error and root mean squared error terms, respectively. The maximum prediction error is found to be 9.85% and 10.2%, and the average prediction error is found to be 1.19% and 1.29% for adaptive neuro fuzzy inference system and ANN models, respectively, for optimum simulations. The proposed neuro-fuzzy-driven top gas pressure prediction system is unique in the literature and should be integrated into existing control systems to improve operational awareness and sustainability or can be used as input guidance for a possible future top gas recovery system.

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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