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Öğe The Effect of the Crack-Stiffness Relationship on the System Response in Columns of an SDOF Steel Structure Model under Harmonic and Earthquake Excitations(Hindawi Ltd, 2024) Aggumus, Huseyin; Daskin, Mahmut; Haskul, Mehmet; Turan, AbdullahIn this study, the effects of cracks in the columns of a single degree of freedom (SDOF) steel structure model on the system responses due to the decrease in the equivalent spring coefficient were investigated. The crack in the columns was modeled as an edge crack, and the crack was thought to behave like a spring. The crack is considered as the mode I crack type (opening mode) specified in linear fracture mechanics. In order to examine the responses of the system under the influence of different excitations, the changes in displacement, acceleration, and power spectral density responses under the influence of harmonic excitation at a frequency equal to the fundamental frequency of the SDOF model and six different earthquake excitations were examined.Öğe Flame stability measurement through image moments and texture analysis(Iop Publishing Ltd, 2023) Golgiyaz, Sedat; Cellek, M. Salih; Daskin, Mahmut; Talu, M. Fatih; Onat, CemIn this article, the first two moments of the image, mean and standard deviation, uniform local binary pattern (LBP) texture analysis methods were experimentally investigated in coal-fired boilers to measure flame stability. The first two moments of the flame image were used to evaluate the flame stability in terms of color and brightness (average gray value). Although the radiation signal of the flame is widely obtained by the spectral analysis method, the radiation signal of the flame was obtained by the LBP texture analysis method in this study. The flame stability measurement technique proposed in this study does not require prior knowledge about charged coupling devices camera features. Therefore, it can be easily applied to measure flame stability without expensive and complicated adaptation processes. Flame stability was measured with R = 0.9868 accuracy with the proposed method. The experimental results show that the proposed texture analysis method is more effective than current spectral analysis methods. The results obtained within the scope of this study also show that it can be easily applied to existing closed-loop control systems to monitor flame stability.Öğe Generalizability of empirical correlations for predicting higher heating values of biomass(Taylor & Francis Inc, 2024) Daskin, Mahmut; Erdogan, Ahmet; Gulec, Fatih; Okolie, Jude A.Designing efficient biomass energy systems requires a thorough understanding of the physicochemical, thermodynamic, and physical properties of biomass. One crucial parameter in assessing biomass energy potential is the higher heating value (HHV), which quantifies its energy content. Conventionally, HHV is determined through bomb calorimetry, but this method is limited by factors such as time, accessibility, and cost. To overcome these limitations, researchers have proposed a diverse range of empirical correlations and machine-learning approaches to predict the HHV of biomass based on proximate and ultimate analysis results. The novelty of this research is to explore the universal applicability of the developed empirical correlations for predicting the Higher Heating Value (HHV) of biomass. To identify the best empirical correlations, nearly 400 different biomass feedstocks were comprehensively tested with 45 different empirical correlations developed to use ultimate analysis (21 different empirical correlations), proximate analysis (16 different empirical correlations) and combined ultimate-proximate analysis (8 different empirical correlations) data of these biomass feedstocks. A quantitative and statistical analysis was conducted to assess the performance of these empirical correlations and their applicability to diverse biomass types. The results demonstrated that the empirical correlations utilizing ultimate analysis data provided more accurate predictions of HHV compared to those based on proximate analysis or combined data. Two specific empirical correlations including coefficients for each element (C, H, N) and their interactions (C*H) demonstrate the best HHV prediction with the lowest MAE (similar to 0.49), RMSE (similar to 0.64), and MAPE (similar to 2.70%). Furthermore, some other empirical correlations with carbon content being the major determinant also provide good HHV prediction from a statistical point of view; MAE (similar to 0.5-0.8), RMSE (similar to 0.6-0.9), and MAPE (similar to 2.8-3.8%).Öğe Prediction of combustion states from flame image in a domestic coal burner(Iop Publishing Ltd, 2021) Onat, Cem; Daskin, Mahmut; Toraman, Suat; Golgiyaz, Sedat; Talu, Muhammed FatihCoal is still a strategic fuel for many developing countries. The environmental impact of emissions resulting from the widespread use of coal worldwide is a matter of serious debate. In this perspective, clean coal burning technologies are in demand. In this study, a measurement system that estimates emission from flame images in a domestic coal burner is proposed. The system consists of a charge-coupled device camera, image processing software (real time image acquisition, noise reduction and extracting features) and artificial intelligence elements (classification of features by neural networks). In feature extraction stage, only five flame region features (G(x), G(y) , trace, L (2) and L (infinity) norm) is extracted. G(cx) and G(cy) are the instantaneous change of the horizontal and vertical components of center mass of the flame image. These features are new concepts for emission estimation from the flame image. The proposed system makes a difference with its simpler structure and higher accuracy compared to its counterparts previously presented in the literature.Öğe Prediction of Excess Air Factor in Automatic Feed Coal Burners by Processing of Flame Images(Editorial Office Chinese Journal Mechanical Engineering, 2017) Talu, Muhammed Fatih; Onat, Cem; Daskin, MahmutIn this study, the relationship between the visual information gathered from the flame images and the excess air factor lambda in coal burners is investigated. In conventional coal burners the excess air factor lambda. can be obtained using very expensive air measurement instruments. The proposed method to predict lambda for a specific time in the coal burners consists of three distinct and consecutive stages; a) online flame images acquisition using a CCD camera, b) extraction meaningful information (flame intensity and brightness)from flame images, and c) learning these information (image features) with ANNs and estimate lambda. Six different feature extraction methods have been used: CDF of Blue Channel, Co-Occurrence Matrix, L (a)-Frobenius Norms, Radiant Energy Signal (RES), PCA and Wavelet. When compared prediction results, it has seen that the use of co-occurrence matrix with ANNs has the best performance (RMSE = 0.07) in terms of accuracy. The results show that the proposed predicting system using flame images can be preferred instead of using expensive devices to measure excess air factor in during combustion.Öğe Preparation of poly(acrylamide-co-2-acrylamido-2-methylpropan sulfonic acid)-g-Carboxymethyl cellulose/Titanium dioxide hydrogels and modeling of their swelling capacity and mechanic strength behaviors by response surface method technique(Wiley, 2021) Boztepe, Cihangir; Daskin, Mahmut; Erdogan, Ahmet; Sarici, TalhaIt is very important that new generation, unique, high mechanical strength, and biocompatible hydrogel composites are developed due to their potential to be used as biomaterials in the biomedical field. Modeling of the swelling capacity and mechanical strength behavior of hydrogels is a domain of steadily increasing academic and industrial importance. These behaviors are difficult to model accurately due to hydrogels show very complex behavior depending on the content. In this study, a series of poly(acrylamide-co-2-acrylamido-2-methylpropan sulfonic acid)-g-carboxymethyl cellulose/TiO2 (poly(AAm-co-AMPS)-g-CMC/TiO2) superabsorbent hydrogel composites were prepared by free-radical graft copolymerization in aqueous solution. Structural and surface morphology characterizations were conducted by using Fourier-transform infrared spectroscopy and scanning electron microscope analysis techniques. For modeling the equilibrium swelling capacity and fracture strength behaviors of hydrogels, the composition parameters (such as mole ratio of AMPS/AAm, wt% of CMC, and wt% of TiO2) was proposed by response surface method (RSM) Design Expert-10 software. Statistical parameters showed that the RSM model has good performance in modeling the swelling capacity and mechanic fracture strength behaviors of poly(AAm-co-AMPS)-g-CMC/TiO2 hydrogel composites. According to the RSM model results, the maximum swelling capacity and fracture strength values were calculated as 270.39 g water/g polymer and 159.23 kPa, respectively.Öğe Synthesis of magnetic responsive poly(NIPAAm-co-VSA)/Fe3O4 IPN ferrogels and modeling their deswelling and heating behaviors under AMF by using artificial neural networks(Elsevier, 2022) Boztepe, Cihangir; Daskin, Mahmut; Erdogan, AhmetSynthesis of stimuli-responsive hydrogels and modeling their behaviors under stimulus are very important for the use of smart materials in biomedical applications. In the present study, magnetic field responsive and novel poly (N-Isopropylacrylamide-co-Vinylsulfonic acid)/Fe3O4 interpenetrating polymer network (poly(NIPAAm-coVSA)/Fe3O4 IPN) hydrogel composite (ferrogel) series were successfully synthesized. Firstly, temperature responsive poly(NIPAAm-co-VSA) IPN hydrogels containing various amount of vinylsulfonic acid (VSA) were synthesized by two polymerization method: emulsion and solution polymerization. Then, Fe3O4 nanoparticles were loaded to hydrogel systems through in situ reduction of Fe2+/Fe3+ ions. Their chemical structures, magnetic properties and surface morphologies were characterized by FT-IR, VSM and SEM analysis techniques. The effects of the VSA content in ferrogel composition on deswelling and heating behavior of ferrogels under various of alternating magnetic fields (AMFs) were experimentally investigated. In order to characterize their heating and deswelling behaviors by magnetic induction heating, heating and deswelling kinetics under 1.37, 1.64 and 1.91 mT magnetic fields were investigated. Their fully swollen states showed complex deswelling and heating behaviors. Artificial neural networks (ANNs) in MATLAB were used to model these behaviors. The ANN models which associated input variables, were able to accurately predict complex deswelling and heating behaviors of magnetic field-responsive poly(NIPAAm-co-VSA)/(FeO4)-O-3 IPN hydrogel composites.Öğe Unburnt carbon estimation through flame image and gauss process regression(Taylor & Francis Ltd, 2024) Golgiyaz, Sedat; Demir, Usame; Cellek, Mehmet Salih; Daskin, Mahmut; Talu, M. Fatih; Onat, CemThe presence of unburned carbon in coal-burning systems undoubtedly causes a loss in the amount of energy that can be obtained from the system, and also reveals an inadequacy in terms of the usability of the ashes. The expensiveness of existing unburned carbon prediction methods is one of the reasons why these technologies cannot be used. This situation requires working on alternative non-combustible carbon technologies. In this paper, a new approach is presented for estimating unburned carbon in a small-scale coal burner system using the Gaussian regression model and CCD camera-acquired flame image. The proposed approach evaluates brightness, fluctuation amplitude, area, and radiation signal properties of the flame image. The proposed non-combustible carbon estimation technique does not require prior knowledge of CCD camera features. In the feature acquisition phase, results were obtained for each natural component of the flame image in RGB colour space separately, in pairs, all together and for three artificial colour channels (grey image). With the proposed method, the unburned carbon estimation was obtained with an accuracy of R = 0.9664 when all colour channels of the RGB image were used together. This result shows that unburned carbon can be estimated from the instantaneous flame images obtained with the CCD camera.
