Artificial Neural Network Simulation of Cyclone Pressure Drop: Selection of the Best Activation Function in Iraq

Demi̇r, Selami; Karadeniz, Aykut; Demır, Neslihan Manav

doi:10.15244/pjoes/62907

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…Geri beslemeli yapay sinir ağı (BPANN) için bir Excel Visual Basic for Applications (Excel VBA) programı kullanılmıştır [19,20]. BPANN'de girdi katmanı olarak çamur yaşı (θ s ), aerobik tankta hidrolik bekletme süresi (θ h ), geri devir oranı (R s ), iç geri devir oranı (R n ), aerobik tanktaki çözünmüş oksijen konsantrasyonu (C) ve giriş suyunda KOİ konsantrasyonu ve giriş suyunda TN konsantrasyonu olmak üzere 7 adet nöron kullanılmıştır.…”

Section: Yapay Sinir Ağıunclassified

“…Hava kirliliğinden su ve toprak kirliliğine kadar birçok alanda muhtelif prosesleri öğrenip modellemek için kullanılan yapay sinir ağları [14][15][16][17][18][19][20], biyolojik atıksu arıtma proseslerinin modellenmesi amacıyla da uygulanmıştır [21][22][23]. Ne var ki biyolojik atıksu arıtma proseslerinin yapay sinir ağları ile modellenmesi amacıyla gerçekleştirilen çalışmaların çoğu ağ topolojisinin kurulması üzerine odaklanmış; ağda kullanılan aktivasyon fonksiyonları geri planda kalmıştır.…”

Section: Introductionunclassified

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Bi̇yoloji̇k Atiksu Aritma Tesi̇sleri̇nde Karbon Ve Azot Gi̇deri̇m Veri̇mleri̇ni̇n Tahmi̇ni̇ Amaciyla Yapay Si̇ni̇r Ağlarinin Kullanimi

Demır¹

2017

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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ÖZBiyolojik azot giderimi gerçekleştirilen atıksu arıtma proseslerinin (AO prosesi) modellenmesi amacıyla Aktif Çamur Modeli No. 1 (ASM1) kullanılagelmişse de bu modelde ihtiyaç duyulan girdi parametrelerinin tahmin edilmesi çok zaman almaktadır. Bu çalışma kapsamında, ASM1 kadar detaylı girdi verisi gerektirmeyen geri beslemeli yapay sinir ağlarının ( USE OF ARTIFICIAL NEURAL NETWORKS AS A TOOL TO PREDICT CARBON AND NITROGEN REMOVAL EFFICIENCIES IN BIOLOGICAL WASTEWATER TREATMENT PLANTS ABSTRACTAlthough Activated Sludge Model No. 1 (ASM1) was used for modelling biological nitrogen removal processes, estimation of input parameters required to run this model necessitates complicated laboratory analyses. In this study, the performance of Backpropagation Artificial Neural Networks (BPANN), which requires considerably less numbers of input parameters, in predicting chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), and total nitrogen (TN) removal efficiencies was tested. For this purpose, four activation functions were employed in BPANN. Results suggested that COD, TKN, and TN removal efficiencies in AO processes can be accurately estimated using BPANN, with the highest learning and prediction capacity when Sinc function is employed. The mean square errors (MSEs) with Sinc-BPANN were calculated as 2.5010 -4 for COD removal efficiency, 4.1510 -4 for TKN removal efficiency, and 2.6510 -4 for TN removal efficiency. Therefore, the Sinc-BPANN is concluded to be an efficient tool for estimating nonlinear nature of COD, TKN, and TN removal efficiencies in AO processes using considerably less numbers of input parameters.

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Section: Yapay Sinir Ağıunclassified

Section: Introductionunclassified

Bi̇yoloji̇k Atiksu Aritma Tesi̇sleri̇nde Karbon Ve Azot Gi̇deri̇m Veri̇mleri̇ni̇n Tahmi̇ni̇ Amaciyla Yapay Si̇ni̇r Ağlarinin Kullanimi

Demır¹

2017

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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“…Two performance parameters of a cyclone separator are pressure drop and collection efficiency. Many research studies have been dedicated to cyclones' collection efficiency and pressure drop including the study by Demir et al [1,2], and the optimum design of a cyclone separator is usually based on these operating parameters in addition to particulate loading as well as particulate characteristics. A great number of research studies pertaining to the optimization of cyclone dimensions have been performed since cyclone separators have been introduced in the beginning of twentieth century [3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of the Effects of Helical Roof on the Performance of Stairmand High Efficiency Cyclone

Karadeniz

Demi̇r

2021

International Journal of Chemical Engineering

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Cost-effectiveness of a standard and a modified Stairmand high-efficiency type cyclone was compared at various inlet gas velocities. The modified design was obtained by replacing the roof of the standard design with a helical roof. Experiments were conducted by both standard and helical roof designs at the same operating conditions. Results showed that helical roof leads to reduced pressure drop in cyclones while having a negative impact on particle collection efficiency. Reductions in pressure drop can reach up to 30%, while particle collection efficiency is reduced by up to 8% simultaneously. Overall, the treatment cost of a cyclone separator can be reduced by 14.1–20.8%. Results indicated that helical roof design cyclones can be used to reduce overall treatment cost by cyclone separators.

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“…Considering the benefits of computer‐aided methods of teaching and lack of open‐source tools in the field of wastewater treatment, authors believe that it will be a valuable contribution to implement an open‐source modeling tool based on ASM3. Microsoft Excel and visual basic for applications (VBA) provide a perfect platform for implementation of any kind of engineering software thanks to several properties of Microsoft (MS) Excel, several benefits of which are shown in previous studies [4–11].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Activated Sludge Model No. 3 as an educational tool: bioXL3

Demi̇r

Demır

2020

Comp Applic In Engineering

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Implementation of an Microsoft (MS) Excel add‐in, called bioXL3, is presented in this paper. bioXL3 is an educational tool for undergraduate‐ and graduate‐level education in the environmental engineering field. The add‐in is an open‐source tool and is used for the simulation of conventional biological wastewater treatment plants based on Activated Sludge Model No. 3. The software is developed using visual basic for applications (VBA) in MS Excel and is tested with a number of sample model setups. All formulations used in VBA programming are provided here. Also, simulation results of a sample predenitrification system are summarized. The performance of the software in engineering education was tested by a teaching session. Results indicated that the add‐in can prove useful in undergraduate and graduate‐level education in environmental engineering.

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Artificial Neural Network Simulation of Cyclone Pressure Drop: Selection of the Best Activation Function in Iraq

Cited by 6 publications

References 12 publications

Bi̇yoloji̇k Atiksu Aritma Tesi̇sleri̇nde Karbon Ve Azot Gi̇deri̇m Veri̇mleri̇ni̇n Tahmi̇ni̇ Amaciyla Yapay Si̇ni̇r Ağlarinin Kullanimi

Bi̇yoloji̇k Atiksu Aritma Tesi̇sleri̇nde Karbon Ve Azot Gi̇deri̇m Veri̇mleri̇ni̇n Tahmi̇ni̇ Amaciyla Yapay Si̇ni̇r Ağlarinin Kullanimi

Experimental Investigation of the Effects of Helical Roof on the Performance of Stairmand High Efficiency Cyclone

Implementation of Activated Sludge Model No. 3 as an educational tool: bioXL3

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