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Öğe Application of arti ficial intelligence in modeling of the doxorubicin release behavior of pH and temperature responsive poly(NIPAAm-co-AAc)-PEG IPN hydrogel(Elsevier, 2020) Boztepe, Cihangir; Kunkul, Asim; Yuceer, MehmetModeling of the drug release behavior of stimuli-responsive hydrogels is a domain of steadily increasing academic and industrial importance. It is very difficult to accurately predict the drug release kinetic of this type drug carrier materials due to environmental variables in the body such as pH and temperature. In this study, a pH- and temperature-responsive poly(N-Isopropyl acrylamide-co-Acrylic acid)/Poly(ethylene glycol) (poly(NIPAAm-co-AAc)/PEG) interpenetrating polymer network (IPN) hydrogel was synthesized by free radical solution polymerization in the presence of poly(NIPAAm-co-AAc) microgels and PEG. The synthesized IPN hydrogels showed rapid pH- and temperature-responsive deswelling behavior. The textural properties and surface morphology of poly(NIPAAm-co-AAc) IPN hydrogel were characterized by scanning electron microscopy (SEM) analysis technique. The doxorubicin (DOX) was loaded to the hydrogels by swelling the hydrogels in the DOX solution. The cumulative release of DOX has been investigated as a function of time, pH and temperature. Experimental DOX release data obtained were successfully modeled using ANNs, LS-SVM and SVR methodologies. To evaluate the performance of these models, four statistical parameters: correlation coefficient (R), root mean squared error (RMSE), mean square error (MSE) and mean absolute percentage error (MAPE) were calculated. It was found that the developed ANN model show best performance in modeling the DOX release behavior of poly(NIPAAm-co-AAc)/PEG IPN hydrogels.Öğe APPLICATION OF SIMULATED ANNEALING ALGORITHM FOR THE MAGNETIC FILTRATION PROCESS(Yildiz Technical Univ, 2019) Yildiz, Zehra; Yuceer, MehmetThe optimization of the magnetic filtration processes parameters on the separation performance of corrosion particles from waste-water suspensions by magnetic filter have been discussed. By using the magnetic filter performance formulas presented in the literature, a base model for magnetic filter performance is selected and the magnetic filter cleaning coefficient is optimized by changing several selected filter process parameters. The magnetic field intensity, diameter of the matrix elements (balls), filter length and filtration velocity are chosen as the inputs parameters and cleaning coefficient as output parameter. The Simulated Annealing (SA) Algorithm was applied to the model. Four variables were successfully optimized.Öğe An artificial neural network model for the effects of chicken manure on ground water(Elsevier, 2012) Karadurmus, Erdal; Cesmeci, Mustafa; Yuceer, Mehmet; Berber, RidvanIn the areas where broiler industry is located, poultry manure from chicken farms could be a major source of ground water pollution, and this may have extensive effects particularly when the farms use nearby ground water as their fresh water supply. Therefore the prediction the extent of this pollution, either from rigorous mathematical diffusion modeling or from the perspective of experimental data evaluation bears importance. In this work, we have investigated modeling of the effects of chicken manure on ground water by artificial neural networks. An ANN model was developed to predict the total coliform in the ground water well in poultry farms. The back-propagation algorithm was employed for training and testing the network, and the Levenberg-Marquardt algorithm was utilized for optimization. The MATLAB 7.0 environment with Neural Network Toolbox was used for coding. Given the associated input parameters such as the number of chickens, type of manure pool management and depth of well, the model estimates the possible amount of total coliform in the wells to a satisfactory degree. Therefore it is expected to be of help in future for estimating the ground water pollution resulting from chicken farms. (C) 2011 Elsevier B. V. All rights reserved.Öğe Artificial neural network models for HFCS isomerization process(Springer London Ltd, 2010) Yuceer, MehmetThis work presents an approach to the modeling of a real industrial isomerization reactor by using artificial neural networks (ANN) pre-processed with principal component analysis (PCA). The initial model considered the output fructose concentration as the output variable, while the flow rate of substrate to the reactor as the principal input variable. Then, the ANN model was restructured and inversely trained by assuming the exit fructose concentration as the input variable and the feed flow rate as the output variable. Results indicate good performance by the application of the developed strategy to an extensive industrial data set. The results are expected to be useful in future, controlling the fructose concentration in the HFCS isomerization reactor.Öğe BROMATE REMOVAL PREDICTION IN DRINKING WATER BY USING THE LEAST SQUARES SUPPORT VECTOR MACHINE (LS-SVM)(Yildiz Technical Univ, 2020) Karadurnius, Erdal; Goz, Eda; Taskin, Nur; Yuceer, MehmetThe main objective of this study was to develop Least Squares Support Vector Machine (LS-SVM) algorithm for prediction of bromate removal in drinking water. Adsorption method known as environmental-friendly and economical was used in the experimental part of this study to remove this harmful compound from drinking water. Technically (pure), HCl-, NaOH- and NH3-modified activated carbons were prepared as adsorbent. Experimental studies were carried out with synthetic samples in three different concentrations. To forecast bromate removal percentage particle size and amount of the activated carbon, height and diameter of the column, volumetric flowrate, and initial concentration were selected as the input variables Radial basis kernel function was selected as activation function in algorithm. Algorithm parameters that gamma and sigma(2) values set as 415 and 3.956 respectively. To evaluate model performance some performance indices were calculated. Correlation coefficient (R), mean absolute percentage error (MAPE%) and root mean square error (RMSE) value for the training and testing phase R:0.996, MAPE%: 2.59 RMSE: 2.14 and R:0.994, MAPE%: 3.21 RMSE: 2.51 respectively. These results obtained from this study were compared with the ANN model previously developed with the same input data. As a result, LS-SVM has better performance than ANN.Öğe COMPARISON OF CONTROL STRATEGIES FOR DISSOLVED OXYGEN CONCENTRATION IN ACTIVATED SLUDGE PROCESS(Parlar Scientific Publications (P S P), 2016) Akyurek, Evrim; Karadurmus, Erdal; Yuceer, Mehmet; Goz, Eda; Atasoy, Ilknur; Berber, RidvanDifferent control algorithms were compared and tested for activated sludge wastewater treatment process. Proportional-integral-derivative control (PID), Model Predictive Control (MPC) with linear model, MPC with non-linear model, Nonlinear Autoregressive-Moving Average (NARMA-L2) control, Neural Network Model Predictive Control (NN-MPC) and optimal control with Sequential Quadratic Programming (SQP) algorithm were evaluated via simulation of activated sludge model. Controlled and manipulated variables were selected as dissolved oxygen level and aeration rate, respectively. Rise time, overshoot, Integral Absolute Error (IAE) and Integral Square Error (ISE) were calculated for each controller. It was concluded that NARMA-L2 controller and optimal control with SQP would outperform the other control strategies.Öğe Comparison of Control Strategies for Dissolved Oxygen Control in Activated Sludge Wastewater Treatment Process(Elsevier Science Bv, 2009) Akyurek, Evrim; Yuceer, Mehmet; Atasoy, Ilknur; Berber, RidvanSix control strategies; PID control, Model Predictive Control (MPC) with linear model, MPC with non-linear model, Nonlinear Autoregressive-Moving Average (NARMA-L2) control, Neural Network Model Predictive Control (NN-MPC) and optimal control with sequential quadratic programming (SQP) algorithm were evaluated via simulation of activated sludge wastewater treatment process. Controller performance assessment was based on rise time, overshoot, Integral Absolute Error (IAE) and Integral Square Error (ISE) performance criteria. As dissolved oxygen level in the aeration tank plays an important role in obtaining the effluent water quality, and in operating cost, it was chosen as the controlled variable. It was concluded consequently that NARMA-L2 controller and optimal control with SQP would outperform the others in achieving the specified objective.Öğe Control vector parameterization approach in optimization of alternating aerobic-anoxic systems(Wiley, 2009) Balku, Saziye; Yuceer, Mehmet; Berber, RidvanDetermination of the optimal aeration profile for an activated sludge system in which nitrification and denitrification take place sequentially in a single reactor (alternating aerobic-anoxic) is an attractive optimization problem because of complexities involved in, and high computational times required for solution. The rigorous dynamic modeling and start-up simulation of such a system, together with aeration profile optimization by an evolutionary algorithm (EA), were tackled in a previous study. In this paper an easy-to-implement dynamic optimization technique based on sequential quadratic programming method and control vector parameterization approach is provided. In comparison with EA, the proposed algorithm gives better results in shorter computation times. Copyright (C) 2009 John Wiley & Sons, Ltd.Öğe EVALUATION OF ELECTROMAGNETIC FILTRATION EFFICIENCY USING LEAST SQUARES SUPPORT VECTOR MODEL(Oficyna Wydawnicza Politechniki Wroclawskiej, 2015) Yuceer, Mehmet; Yildiz, Zehra; Abbasov, TeymurazThe present study aims to apply a least squares support vector model (LS-SVM) for predicting cleaning efficiency of an electromagnetic filtration process, also called quality factor, in order to remove corrosion particles (rust) of low concentrations from water media. For this purpose, three statistical parameters: correlation coefficient, root mean squared error and mean absolute percentage error were calculated for evaluating the performance of the LS-SVM model. It was found that the developed LS-SVM can be used to predict the effectiveness of electromagnetic filtration process.Öğe FRACTIONAL-ORDER CONTROL STRATEGIES FOR THE ACTIVATED SLUDGE PROCESS(Parlar Scientific Publications (P S P), 2018) Goz, Eda; Yuceer, MehmetActivated sludge process has a complex and nonlinear characteristics, therefore, various conventional control algorithms are incapable of controlling activated sludge process. On the other hand, optimizations of waste water treatment plants have been inevitable due to the strict regulations. This paper deals with the application of fractional order proportional integral and derivative controller ((PID mu)-D-lambda), fractional order proportional integral (PI lambda) and classical PID and PI controller for activated sludge waste water treatment plants. For this purpose, a simpler model with single aeration bioreactor that includes biological process with one type of substrate and microorganism was used. Since the level of dissolved oxygen in the aeration tank is important for the effluent quality standards and minimizing the operating cost, it was chosen as the controlled variable. Moreover, manipulated variable has been defined as aeration rate. Performances of PID, PI, fractional order PID and fractional order PI controller were evaluated with integral square error (ISE). The simulation results indicated that fractional order PID controller exhibited a better performance than fractional order PI, conventional PID and PI controllers. Controller parameters were calculated via various optimization strategies such as particle swarm optimization (PSO), genetic algorithm (GA) and sequential quadratic programming (SQP). The values of fractional order PID and PI controller parameters were almost the same even if different optimization methods were tried for determining the controller parameters. Fractional order PID and fractional order PI controller parameters were obtained as K-p = 29.8916, K-I = 29.913, K-D = 29.8909, lambda = 1.0934, mu = 0.01612 and K-p = 29.9999, K-I = 29.9999, lambda = 0.995, respectively. Similarly, conventional PID parameters were not affected by different optimization methods. Both GA and PSO show the same controller parameters (K-p = 29.9997, tau(I) = 29.9998, tau(D) = 29.9993). Furthermore, conventional PID parameters that are manipulated by Ziegler Nichols method were K-p = 46, tau(I) = 115, tau(D) = 4.6.Öğe INVESTIGATION OF INFRARED DRYING BEHAVIOUR OF SPINACH LEAVES USING ANN METHODOLOGY AND DRIED PRODUCT QUALITY(Polska Akad Nauk, Polish Acad Sciences, 2015) Sarimeseli, Ayse; Yuceer, MehmetEffects of infrared power output and sample mass on drying behaviour, colour parameters, ascorbic acid degradation, rehydration characteristics and some sensory scores of spinach leaves were investigated. Within both of the range of the infrared power outputs, 300-500 W, and sample amounts, 15-60 g, moisture content of the leaves was reduced from 6.0 to 0.1 +/-(0.01) kg water/kg dry base value. It was recorded that drying times of the spinach leaves varied between 3.5-10 min for constant sample amount, and 4-16.5 min for constant power output. Experimental drying data obtained were successfully investigated by using artificial neural network methodology. Some changes were recorded in the quality parameters of the dried leaves, and acceptable sensory scores for the dried leaves were observed in all of the experimental conditions.Öğe Kinetic and artificial neural network modeling techniques to predict the drying kinetics of Mentha spicata L.(Wiley, 2019) Karakaplan, Nihan; Goz, Eda; Tosun, Emir; Yuceer, MehmetThis study presented both the empirical and artificial neural network (ANN) approaches to estimate the moisture content of Mentha spicata. Two different types of drying methods (in shade and in oven (35 and 50 degrees C)) were used to investigate the drying kinetics of the Mentha spicata samples. The effects of drying methods on effective diffusion coefficient, moisture ratio (MR), drying rate, and activation energy were investigated. Moreover, six different thin layer drying models (Page, Diffusion approach, Newton, Modified Henderson, Henderson and Pabis and Pabis and Midilli) and an ANN with feed forward structure were used to define the drying kinetics of these samples. In order to estimate the kinetic model parameters, sequential quadratic programming (SQP) was used. Model performances were evaluated based on the coefficient of determination (R-2), root mean square error (RMSE) and mean absolute percentage error (MAPE%) values. In the kinetic part of the modeling study, the Midilli model provided better results than the others. However, the ANN had the best results when a total assessment was made. The effective diffusion coefficient values were found in the range between 1.31 x 10(-12) and 4.43 x 10(-12) m(2)/s. The activation energy was obtained as 44.31 kJ/kmol. The R-2, MAPE%, and RMSE values for the ANN test data were 1.00, 0.2257, and 5.9447 x 10(-4), respectively. In the future, different modeling approaches will be applied to describe this drying process. Practical applications Drying is a process where heat transfer and mass transfer take place together. Modeling is an innovative approach used in evaluation of experimental data and has increasing popularity in recent years. ANNs are a powerful data-driven method, and they have a very broad area of usage from medicine to engineering issues. Empirical models are another approach for describing experimental data. In this study, these two modeling approaches were used to obtain the MR. Humidity is a condition that needs to be checked in food safety and protection. Therefore, it is very important to ensure control with robust modeling techniques. In this study, the developed ANN model had a high R-2 value (R-2 = 1.00). This indicated that it may be used successfully in real applications.Öğe A large-scale measurement, analysis and modelling of electromagnetic radiation levels in the vicinity of GSM/UMTS base stations in an urban area(Oxford Univ Press, 2016) Karadag, Teoman; Yuceer, Mehmet; Abbasov, TeymurazThe present study analyses the electric field radiating from the GSM/UMTS base stations located in central Malatya, a densely populated urban area in Turkey. The authors have conducted both instant and continuous measurements of high-frequency electromagnetic fields throughout their research by using non-ionising radiation-monitoring networks. Over 15 000 instant and 13 000 000 continuous measurements were taken throughout the process. The authors have found that the normal electric field radiation can increase similar to 25 % during daytime, depending on mobile communication traffic. The authors' research work has also demonstrated the fact that the electric field intensity values can be modelled for each hour, day or week with the results obtained from continuous measurements. The authors have developed an estimation model based on these values, including mobile communication traffic (Erlang) values obtained from mobile phone base stations and the temperature and humidity values in the environment. The authors believe that their proposed artificial neural network model and multivariable least-squares regression analysis will help predict the electric field intensity in an environment in advance.Öğe A model for molecular weight prediction in acrylonitrile polymerization(Taylor & Francis Inc, 2008) Atasoy, Ilknur; Berber, Ridvan; Yuceer, MehmetThe number and weight-average molecular weights in acrylonitrile polymerization have been calculated previously by Peebles([2]). However, the foundations for the two critically important expressions leading to the calculation of molecular weights were not disclosed in detail, no dynamics were presented, and predictions were not in good agreement with the experimental data, particularly in terms of polydispersity index. The present work focuses on the same issue, and brings a new rigorous dynamic model, based on the kinetics given by Peebles([2]). The new, more detailed model defines the chain lengths in terms of the leading moments of active and dead polymer, provides the prediction of reactor dynamics in compliance with the practice in industry, and estimates the polydispersity index of the polymer with better agreement to the experimental data.Öğe MODELING MICROWAVE DRYING KINETICS OF THYME (THYMUS VULGARIS L.) LEAVES USING ANN METHODOLOGY AND DRIED PRODUCT QUALITY(Wiley-Hindawi, 2014) Sarimeseli, Ayse; Coskun, Mehmet Ali; Yuceer, MehmetEffects of microwave power output and sample mass on drying behavior, color parameters, rehydration characteristics and some sensory scores of thyme leaves were investigated. Within the range of the microwave power outputs, 180-900 W, and sample amounts, 25-100 g, moisture content of the leaves were reduced to 0.1 +/- (0.01) from 4.05 kg water/kg dry base value. Drying times of the leaves were found to be varying between 3.5 and 15.5 min for constant sample amount, and 6.5 and 20.5 min for constant power output. Experimental drying data obtained were successfully modeled using artificial neural networks methodology. Statistical values of the test data were found to be 0.9999, 4.0937 and 0.025 for R-square, MAPE (%) and RMSE, respectively. Some changes were recorded in the quality parameters, and acceptable sensory scores for the dried leaves were observed in all of the experimental conditions (P < 0.05).Öğe Modeling of drug release behavior of pH and temperature sensitive poly(NIPAAm-co-AAc) IPN hydrogels using response surface methodology and artificial neural networks(Elsevier, 2017) Brahima, Sanogo; Boztepe, Cihangir; Kunkul, Asim; Yuceer, MehmetAn interpenetrated polymer network (IPN) poly(NIPAAm-co-AAc) hydrogel was synthesized by two polymerization method: emulsion and solution polymerization. The pH- and temperature-sensitive hydrogel was loaded by swelling with riboflavin drug, a B2 vitamin.The release of riboflavin as a function of time has been achieved under different pH and temperature environments. The determination of experimental conditions and the analysis of drug delivery results were achieved using response surface methodology (RSM). In this work, artificial neural networks (ANNs) in MATLAB were also used to model the release data. The predictions from the ANN model, which associated input variables, produced results showing good agreement with experimental data compared to the RSM results. (C) 2017 Elsevier B.V. All rights reserved.Öğe Modeling of Swelling Behaviors of Acrylamide-Based Polymeric Hydrogels by Intelligent System(Taylor & Francis Inc, 2015) Boztepe, Cihangir; Solener, Musa; Yuceer, Mehmet; Kunkul, Asim; Kabasakal, Osman S.Hydrogels based on acrylamide (AAm) were synthesized by free radical polymerization in an aqueous solution using N, N'-methylenebisacrylamide (MBAAm) as crosslinker. To obtain anionic hydrogels, 2-acrylamido-2-methylpropanesulfonic acid sodium salt (AMPS) and acrylic acid (AAc) were used as comonomers. The swelling behaviors of all hydrogel systems were modeled using an artificial neural network (ANN) and compared with a multivariable least squares regression (MLSR) model and phenomenal model. The predictions from the ANN model, which associated input parameters, including the amounts of crosslinker (MBA) and comonomer, and swelling values with time, produce results that show excellent correlation with experimental data. The parameters of swelling kinetics and water diffusion mechanisms of the hydrogels were calculated using the obtained experimental data. Model analysis indicated that the ANN models could accurately describe complex swelling behaviors of highly swellable hydrogels.Öğe Modeling of the Size Distribution Resulting from Dissolution of Spherical Solid Particles in Turbulent Flow(Taylor & Francis Inc, 2012) Ekmekyapar, Ahmet; Kunkul, Asim; Yuceer, Mehmet; Kelbaliyev, GudretThe process of dissolution of solid particles in turbulent flow regime is of importance in many industrial applications. A new size distribution takes place due to dissolving during the motion of a solid-liquid suspended system in a stirred vessel. An analytical relationship was derived to represent the concentration profile in diffusion boundary layer between solid and liquid. An expression was obtained between mass transfer flow from spherical particle area and particle size changing with time during dissolution of solids. A mathematical model was developed for calculating particle size distribution varying with time during dissolution of spherical solid particles. The Focker-Planck equation was used to construct the distribution function varying with particle size. Model parameters were estimated by the Genetic Algorithm, the validity of the model was confirmed with experimental data.Öğe A modeling study of micro-cracking processes of polyurethane coated cotton fabrics(Sage Publications Ltd, 2018) Gunesoglu, Sinem; Yuceer, MehmetPolyurethane (PU) coating became popular in recent decades to achieve water resistance in clothing fabrics with enhanced visual properties. But reduced breathability of coated fabric is a setback for the clothing industry; therefore, there have been various attempts to achieve breathable water-resistant coatings. A new and facile method of enhancing breathability of PU-coated fabrics, which has been called micro-cracking, has been recently studied and highly encouraging outcomes have been obtained for the use of the process in industry. But when any process is considered to have industrial applications, it is essential to conduct not only the optimization but also modeling studies to find out whether the outputs are predictable; the process is controllable and allows us to see how the results are affected by process parameters. This work conducts a modeling study of micro-cracking processes of PU-coated samples to complete this evaluation. For this purpose, an artificial neural network (ANN) and a least square support vector model (LS-SVM) are developed for the prediction of various properties of PU-coated fabrics after micro-cracking. The results showed that the effects of micro-cracking process on various properties of coated fabric could be predicted through ANN or LS-SVM modeling; specifically, the ANN exhibited better performance in the test set of the data. Thus, it is concluded that the results and the measurements were found to be compatible for defining the process as an industrial alternative.Öğe Neural network based control of the acrylonitrile polymerization process(Wiley-V C H Verlag Gmbh, 2007) Atasoy, Ilknur; Yuceer, Mehmet; Ulker, Ekrem Olguz; Berber, RidvanAcrylic fiber is commercially produced by free radical polymerization, initiated by a redox system. Industrial production of polyacrylonitrile is a variant of aqueous dispersion polymerization, which takes place in a homogenous phase under isothermal conditions with perfect mixing. The fact that the kinetics are a lot more complicated than those of ordinary polymerization systems makes it difficult to control the molecular weight. On the other hand, abundant data is being gathered in industrial polymerization systems, and this information makes the neural network based controllers a good candidate for managing such a difficult control problem. Multilayer neural networks have been applied successfully in the identification and control of dynamic systems. In this work, the neural network based control of continuous acrylonitrile (ACN) polymerization is studied, based on a previously developed new rigorous dynamic model for the polymerization of acrylonitrile. Two typical neural network controllers are investigated, i.e., model predictive control and NARMA-L2 (Nonlinear Auto Regressive Moving Average) control. These controllers are representative of the variety of common ways in which multilayer networks are used in control systems. The results present a comparison of the two common neural network controllers, and indicate that the model predictive controller requires a larger computational time.
