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• MakaleAçık Erişim2026Scopus SJRQ1/r/gumushane/13510
  Semi-experimental approach for hydrogen storage of as-produced zeolitic imidazolate framework-8 without post-processing

  Öztürk, Banu; Asan, Abdurrahman; Köse, Dursun Ali; Kalkişim, Ahmet Tahir; Öztürk, Zeynel

  Low-cost hydrogen storage systems are essential for the widespread use of hydrogen, particularly in mobile platforms such as unmanned aerial vehicles, to increase their range. Hydrogen storage in metal–organic frameworks (MOFs), which offer high capacity through adsorption, is crucial to achieving this goal. This study investigates the hydrogen storage capacity of zeolitic imidazolate framework-8 (ZIF-8), produced via reactive extrusion, which allows for high production capacity and low-cost manufacturing of MOFs. Furthermore, particular attention was given to the storage capacity of ZIF-8 in its as-produced form. An atomistic model of the as-produced ZIF-8, including impurities selected from a series of simulated models containing different amounts of impurity, was developed. The validity of the selected structure was then verified through experimental powder X-ray diffraction and Brunauer–Emmett–Teller (BET) surface area measurements. Subsequently, hydrogen adsorption capacities for varying pressures and temperatures were calculated. The hydrogen adsorption simulations indicated that the atomistic model of the as-produced ZIF-8 exhibited a hydrogen storage capacity of 1.76 wt.% at 77 K and 1 bar, corresponding to a BET surface area of 516 m2/g. In comparison, the experimentally synthesized as-produced ZIF-8 showed a capacity of 1.145 wt.% under the same conditions, with a BET surface area of 520 m2/g. The study demonstrates that the gas storage performance of experimentally produced structures, even without further purification, can be accurately determined through simulation models in this semi-experimental study.

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• BildiriYalnız künye2017/r/gumushane/15278
  Artificial intelligent models for thermodynamic properties of R152a and R134a refrigerants in automobile air conditioning systems

  Kalkisim, Ahmet Tahir; Bilen, Kadir; Hasiloglu, Abdussamet

  With the increase in environmental awareness, the quest for alternative refrigerants for automobile air conditioning systemsis a necessity for the protection of the ozone layer and the prevention of global warming. Therefore, it is important that the refrigerant to be used as an alternative must be at least thermodynamically sufficient and within specified environmental limits. In this study, R152a refrigerant which is an alternative gas for R134a that's use in automobile air conditioning systems will be removed within a certain schedule has been examined thermodynamically. Artificial intelligent model was generated and compared by using data obtained from the examination. Through this model, system was tested with untrained data. The generated model has been very close to test data and successful in high precision.

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• BildiriAçık Erişim2016/r/gumushane/15266
  The comparison of performance by using alternative refrigerant R152a in automobile climate system with different artificial neural network models

  Kalkisim, A.T.; Hasiloglu, A.S.; Bilen, K.

  Due to the refrigerant gas R134a which is used in automobile air conditioning systems and has greater global warming impact will be phased out gradually, an alternative gas is being desired to be used without much change on existing air conditioning systems. It is aimed to obtain the easier solution for intermediate values on the performance by creating a Neural Network Model in case of using a fluid (R152a) in automobile air conditioning systems that has the thermodynamic properties close to each other and near-zero global warming impact. In this instance, a network structure giving the most accurate result has been established by identifying which model provides the best education with which network structure and makes the most accurate predictions in the light of the data obtained after five different ANN models was trained with three different network structures. During training of Artificial Neural Network, Quick Propagation, Quasi-Newton, Levenberg-Marquardt and Conjugate Gradient Descent Batch Back Propagation methodsincluding five inputs and one output were trained with various network structures. Over 1500 iterations have been evaluated and the most appropriate model was identified by determining minimum error rates. The accuracy of the determined ANN model was revealed by comparing with estimates made by the Multi-Regression method.

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  AtıfScopus1·OpenAlex2

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• MakaleYalnız künye2014Scopus SJRQ2/r/gumushane/15453
  The performance of alternative refrigerant gas R152a as Mobile air conditioning refrigerant

  Bilen, Kadir; Kalkisim, Ahmet T.; Solmus, Ismail

  In this study, the possible alternative replacement of R134a with R152 in the air conditioning (AC) system of an automobile has been investigated theoretically. The properties of the R152a is presented and compared with three different refrigerants (R134a, R22 and R12). At a constant condenser temperature, the effect of evaporator temperature (Te=-20 °C - 0 °C) and compressor speed (n = 600 - 2,000 rpm) on the cooling load, the condenser heat load, compressor work, the mass flow rate of refrigerant and performance coefficient of the AC system have been examined with the help of a commercial computer program for refrigerants investigated. It was found that R152a and R134a do not lead to any significant difference on the performance of the refrigeration system. This result shows that with small modifications on the AC systems of current automobiles commonly using R134a, R-152a can be used as an alternative refrigerant since their compression ratios are almost the same.

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  AtıfScopus9·WoS7·OpenAlex5

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