Artificial neural network-based modeling of pressure drop coefficient for cyclone separators

Zhao, Ben; Su, Yaxin

doi:10.1016/j.cherd.2009.11.010

Cited by 62 publications

(45 citation statements)

References 18 publications

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“…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

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: 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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“…With the interdisciplinary development of modern computational technologies, artificial neural networks (ANNs), as typical artificial intelligence (AI) algorithms, have become an attractive approach for modeling highly complicated and nonlinear system [12,38,43]. The ANN can be described as a group of simple processing elements called neurons arranged in parallel layers that are fully interconnected by weighted connections.…”

Section: Introductionmentioning

confidence: 99%

Process modeling and evaluation of petroleum refinery wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor using poly ethyleneimine (PEI)/titania (TiO2) multilayer film on quartz tube

et al. 2014

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In this study, poly ethyleneimine (PEI)/Titania (TiO 2 ) multilayer film on quartz tubes have been successfully fabricated via a layer-by-layer (LbL) self-assembly method. Scanning electron microscopy (SEM) and Brunauer-Emmett-Teller (BET) surface area analysis were carried out for characterization of the layers on quartz tube. The SEM pictures showed that the film surface is smooth and uniform. The BET characterization verified the formation of multilayer thin film. The photocatalytic activity of the PEI/TiO 2 multilayer deposited on the quartz tubes was evaluated in the treatment of raw petroleum refinery wastewater (PRW) under UV light irradiation in three annular photocatalytic reactors. This study examined the impact of initial chemical oxygen demand (COD) concentration, H 2 O 2 concentration, pH and reaction time on the PRW treatment and the results were used to generate both a response surface methodology (RSM) model and an artificial neural network (ANN) model. Maximum COD removal (98 %) was achieved at the optimum conditions (initial COD concentration of 300 mg/l, hydrogen peroxide concentration of 8.8 mM, pH of 5 and reaction time of 120 min). A comparison between the model results and experimental data gave a high correlation coefficient (R ANN 2 = 0.9632, R RSM 2 = 0.943) and showed that two models were able to predict COD removal from PRW by PEI/TiO 2 /UV process. However, ANN model was superior to RSM model with higher value of coefficient of determination (0.9632 ANN [ 0.94 RSM ) and the lower root mean square error (RMSE) (3.377 AAN \ 3.569 RSM ). The average percentage error for ANN and RSM models was 0.18 and 0.73, respectively, indicating the superiority of ANN in capturing the nonlinear behavior of the system. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

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“…Chen et al (2007), Hsu et al (2005), and Tsai et al (2008Tsai et al ( , 2011Tsai et al ( , 2012) applied a modified axial flow cyclone to assess the exposure of nano-sized and respirable particles at different workplaces. Zhao et al (2010Zhao et al ( , 2012 predicted the cylinder-on-cone cyclone separation efficiency and pressure drop by developing two different models. The cylinder-on-cone cyclone was also used in Chemical Looping Combustion (CLC) process (He et al, 2007) and Flidised Bed Combustion (FBC) boiler process (Gu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Study and Application of a Cyclone for Removing Amine Droplets from Recycled Hydrogen in a Hydrogenation Unit

Zhang

et al. 2014

Aerosol Air Qual. Res.

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Recycled hydrogen is fed into a hydrogenation unit using a compressor with oil gas, amine droplets, and other hazardous gases at a super-high operating pressure after having been passed through a desulfurization system. This study developed a gas-liquid cyclone for removing amine droplets in recycled hydrogen based on computational fluid dynamics (CFD), an experimental study and industrial application. The results showed that the highest separation efficiency of the gas-liquid cyclone was 94.8% (experiment) and 97% (CFD) under 20°C and atmospheric pressure. Sampling and analysis showed that the highest separation efficiency for industrial application was 99.9% under 50°C and 10.7 MPa. The device purified the recycled hydrogen, and reduced both raw material losses and desulfurizer consumption. The energy consumption of the cycled hydrogen compressor and the pollution discharge of the hydrogenation unit were also reduced.

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Artificial neural network-based modeling of pressure drop coefficient for cyclone separators

Cited by 62 publications

References 18 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

Process modeling and evaluation of petroleum refinery wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor using poly ethyleneimine (PEI)/titania (TiO2) multilayer film on quartz tube

Study and Application of a Cyclone for Removing Amine Droplets from Recycled Hydrogen in a Hydrogenation Unit

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