International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The fossil fuels and the natural gas reserves that have undertaken the role of the locomotive of the industrial period are limited. It is also the biggest factor in environmental problems. All these reasons lead to the need for alternative fuels or resources. Hydrogen is the candidate to be one of these alternatives; is an unlimited clean and efficient fuel. Hydrogen may assume the role of carrier in the process of storage of other alternative energy sources. Today, interest in hydrogen energy is increasing. One of the reasons for this is that hydrogen can be produced from renewable energy sources such as water, biomass, wind and sun as well as hydrogen from primary energy sources. There is no polluting gas emissions when hydrogen is used as fuel. The HHO dry cell is a device that converts water into HHO (oxyhydrogen) gas. In this study, performance of HHO dry cell with 12×12 plate combination was experimentally investigated and modeled with a Rule-Based Mamdani-Type Fuzzy (RBMTF) modeling technique. Input parameters are; plate number, time, current; output parameter is mass flow rate. The coefficient of multiple determination (R2=98.5) for the mass flow rate. RBMTF results indicated that RBMTF can be successfully used in HHO dry cell with 12×12 plate combination.

Polystyrene (PS) is a versatile plastic used to produce a wide variety of consumer products. It is used as a hard, solid plastic, mostly as food packaging and laboratory products. When polystyrene is mixed with various colorants, additives or other plastic materials, it is used to make electronic parts, automobile parts, toys, pots and equipments and more. Polystyrene is a vinyl polymer. It is structurally a long hydrocarbon chain with a phenyl group attached to the carbon atom. Polystyrene is produced by free radical vinyl polymerization from monomer styrene. In this study, the effect of production parameters on the formation of PS nanofibers was investigated. For this purpose, solutions were prepared at various mixing ratios (12 wt %, 14 wt % and 16 wt %) consisting of PS + dimethylformamide (DMF). The nanofiber structure was determined from these solutions. Electrospin method was used in production of nanofibers.

In this study, microbial fuel cell’s energy conversion performance experimentally investigated from the chemical energy of the organic waste to electrical energy by means of microorganisms. Microbial fuel cell (MFC) consists of two cells which has 15x15x15 cm³ volume. One part of the cell conserves the mud (anode) the other part conserves the water (cathode). The membrane of the microbial fuel cell has 8×8 cm² area. Two different samples were used in the experiments which are active and settlement mud. The power, volt and current values of the active and settlement mud for different temperature, resistance and bubble were determined. The temperature values consist of ΔT = 8°C, ΔT = 10°C, ΔT = 12°C, ΔT = 14°C. ΔT=Tenvironment- Tmud. For every ΔT value 2 different bubble values were examined (High=21,5 g/h, low=3,5 g/h). For every bubble effect 7 different resistance values were determined (1. Resistance= 3,75 Ω; 2. Resistance =7,5 Ω; 3. Resistance =10,5; 4. Resistance = 14,5 Ω; 5. Resistance = 16 Ω; 6. Resistance = 19 Ω; 7. Resistance = 21,5 Ω) and the performance of the 8×8 cm2 membrane of the MFC is detected. As a result; with the increase of the temperature, resistance and bubble effect the voltage production increases and correspondingly the current decreases. When all the experimental results are evaluated,the highest voltage production (687 mV) occurred at ΔT = 14°C and 21,5 Ω with the high bubble effect in the settlement mud. Also, in this study, MFCs performances in terms of voltage, current, temperature, power was modeled with Rule-Based Mamdani-Type Fuzzy (RBMTF) modeling technique. Input parameters ΔT and time; output parameter power was described by RBMTF if-the rules. 1792 experimental data sets, which obtained for power according to ΔT and time, were used in the training step. The comparison between experimental data and RBMTF is done by using coefficient of multiple determination (R2). The actual values and RBMTF results indicated that RBMTF can be successfully used in MFC.