Modeling of some pistachio drying characteristics in fix, semi fluid and fluid bed dryer

Modeling of pistachio (cv. Ohadi) drying kinetic was carried out in a thin layer dryer of fix, semi fluid and fluid bed. Initial moisture content of pistachio samples was about 50.3% (d.b.).  Three drying characteristics of effective diffusivity, energy of activation and specific energy consumption were computed and influences of drying parameters were investigated.  Air temperature levels of 45, 60, 75 and 90℃ and bed conditions of fix, semi fluid and fluid bed were applied in these experiments.  Six mathematical models were used to predict the moisture ratio of pistachio samples in thin layer drying. Results showed that the Two-term model had supremacy in prediction of pistachio drying behavior.  Effective moisture diffusivity of pistachio was calculated using Fick’s second law.  Maximum and minimum values of effective moisture diffusivity calculated between 8.60×10-9 m2/sand 1.98×10-9 m2/s, respectively.  The calculated values of energy of activation for pistachio samples were computed between 30.52 kJ/mol and 35.26 kJ/mol for temperature boundary of 45 to 90℃ and bed conditions of fix to fluid bed.  Specific consumption of energy in thin layer drying of pistachio varied between 0.531×106 and 1.447×106 kJ/kg. Increase in air temperature in each bed condition cause decrease inspecific consumption of energy.  These pistachio properties are necessary to design the dryer system and to adjust the dryer in the best point.Keywords: Two-term model, thin layer drying, pistachio, diffusivity, energy&nbsp

The Influence of Diesel–Ethanol Fuel Blends on Performance Parameters and Exhaust Emissions: Experimental Investigation and Multi-Objective Optimization of a Diesel Engine

Compression combustion engines are a source of air pollutants such as HC and Co, but are still widely used throughout the world. The use of renewable fuels such as ethanol, which is a low-carbon fuel, can reduce the emission of these harmful gases from the engine. A fundamental analysis is proposed in this research to experimentally examine the emission characteristics of diesel–ethanol fuel blends. Furthermore, a multi-objective genetic algorithm (e-MOGA) was developed based on the experimental data obtained to fine the most effective or Pareto set of engine emission and performance optimization solutions. So, the optimization problem had two inputs and seven objectives. For this purpose, input variables for the search space were S (rpm) varied in the range of (1600–2000) and E (%) varied in the range of (0–12). These design variables were chosen to be varied in a prespecified range with a lower and upper band as same as experimental conditions. A diesel engine using (DE2, DE4, DE6, DE8, DE10, and DE12) diesel–ethanol fuel blends, at the various speed of 1600 to 2000 rpm, was utilized for the experiment. The findings showed that the use of diesel–ethanol fuel blends decreased the concentration of CO and HC emissions by 3.2–30.6% and 7.01–16.25%, respectively, due to the high oxygen content of ethanol. As opposed to CO and HC emissions, the NOx concentration showed an increase of 7.5–19.6%. This increase was attributed to the high combustion quality in the combustion chamber, which resulted in a higher combustion chamber temperature. The optimization results confirmed that the shape of the Pareto front obtained from multi-objective ϵ-Pareto optimization could be convex, concave, or a combination of both. A new parameter was introduced as emission index or EI for selection of the best solution among the Pareto set of solutions. This parameter had a minimum value of 4.61. The variables levels for this optimum solution were as follows: engine speed = 1977 rpm, ethanol blend ratio = 10%, CO = 0.27%, CO2 = 6.81%, HC = 3 ppm, NOx = 1573 ppm, SFC = 239 g/kW·h, P = 56 kW, and T = 269.9 N·m. The EI index had a maximum value of 8.26. Conclusively, we can say that the optimization algorithm was successful in minimizing emission index for all ethanol blend ratios, especially at higher engine speeds