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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 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 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 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.Öğe On-line Statistical Process Monitoring and Fault Diagnosis in Batch Baker's Yeast Fermentation(Wiley-V C H Verlag Gmbh, 2009) Berber, Ridvan; Atasoy, Ilknur; Yuceer, Mehmet; Deniz, GuelnihalThis study involves real-time monitoring and fault diagnosis in batch baker's yeast fermentation. A specific Real Time Statistical Process Analysis and Control (RT-SPAC) program was developed to monitor instantaneous reaction conditions. The air flow rate fed to the reactor, temperature, pH, and dissolved oxygen concentration in a laboratory-size fermenter were monitored and recorded by means of on-line sensors. Under control of the RT-SPAC program, 22 batch baker's yeast fermentation operations were carried out. In the first 20 operations, an ordinary process was followed under previously defined nominal operating conditions. Historical data collected from these batches were then used for on-line Dynamic Principal Component Analysis (DPCA) in the course of the following two batches. The last two batches were implemented such that some deliberate faults (in temperature and pH) were introduced during the operation. The results indicated that the software was capable of capturing the process faults, and furthermore the possible causes of these faults were identified by contribution plots.Öğe OPTIMISATION OF OPERATING CONDITIONS IN FED-BATCH BAKER'S YEAST FERMENTATION(Technical Univ Wroclaw, 2013) Atasoy, Ilknur; Yuceer, Mehmet; Berber, RidvanSaccharamyces cerevisia known as baker's yeast is a product used in various food industries. Worldwide economic competition makes it a necessity that industrial processes be operated in optimum conditions, thus maximisation of biomass in production of saccharamyces cerevisia in fed-batch reactors has gained importance. The facts that the dynamic fermentation model must be considered as a constraint in the optimisation problem, and dynamics involved are complicated, make optimisation of fed-batch processes more difficult. In this work, the amount of biomass in the production of baker's yeast in fed-batch fermenters was intended to be maximised while minimising unwanted alcohol formation, by regulating substrate and air feed rates. This multiobjective problem has been tackled earlier only from the point of view of finding optimum substrate rate, but no account of air feed rate profiles has been provided. Control vector parameterisation approach was applied the original dynamic optimisation problem which was converted into a NLP problem. Then SQP was used for solving the dynamic optimisation problem. The results demonstrate that optimum substrate and air feeding profiles can be obtained by the proposed optimisation algorithm to achieve the two conflicting goals of maximising biomass and minimising alcohol formation.Öğe Optimization of Molasses and Air Feeding Profiles in Fed-Batch Baker's Yeast Fermentation(Elsevier Science Bv, 2009) Atasoy, Ilknur; Yuceer, Mehmet; Berber, RidvanThis work focuses on maximization of the amount of biomass in the production of baker's yeast in fed-batch fermenters while minimizing the undesirable alcohol formation, by regulating the molasses and air feed rates. An optimization mechanism coupled with a state estimation algorithm and an Artificial Neural Network model based on original industrial data has been designed. As substrate and biomass concentrations required within this structure can not be measured on-line, these variables were predicted by an artificial neural network model using other measurable variables. Non-measurable substrate concentrations were successfully estimated by Kalman filtering using industrial data and thus, obtained new data sets were used for training the neural network model. Subsequently, through an SQP based optimization algorithm feeding profiles yielding maximum biomass and minimum alcohol formation were obtained. When computed results were compared to the industrial data, it was seen that molasses feeding profiles were compatible whereas aeration profiles were considerably different. The reason of this discrepancy was due to the agitation of the industrial fermenter with air at high air flow rates in order to provide better mixing in the reactor. Since the aeration profile obtained is associated with only the reproduction of microorganisms, it is postulated that the suggested optimization strategy may be industrially applicable for the maximization of biomass where enough agitation is provided by other means.,
