In this study, a Homogeneous charge compression ignition-Direct injection (HCCI-DI) engine was experimentally investigated with waste cooking oil (WCO) biodiesel and its blends with diesel as the DI fuel and gasoline as the premixed fuel. 20% of the gasoline was introduced as the premixed charge and remaining 80% of the fuel was supplied directly into the cylinder at 23°before top dead centre (TDC). The experimental results were compared with DI combustion. Early start of combustion (SOC) was observed from the WCO fueled DI combustion. Lean homogeneous combustion from the gasoline premixed HCCI-DI engine increased the g bt up to 4.23% compared with DI combustion. NOx emissions decreased up to 11% for the WCO fueled HCCI-DI combustion unlike WCO fueled DI combustion. WCO biodiesel-fueled HCCI-DI combustion emitted 6.67% less HC emissions than diesel-fueled DI combustion. ANN modeling was projected to predict the emission and performance characteristics of the gasoline premixed HCCI-DI engine. Response surface methodology (RSM) was accustomed to optimize the engine operating parameters. Keywords HCCI-DI Á Waste cooking oil Á Artificial neural network Á Response surface methodology Á Gasoline premixing List of symbols Nomenclature P Cylinder pressure, bar m Number of data set P max Peak cylinder-pressure, bar R Correlation coefficient R 2 Coefficient of determination V Volume, m 3 Greek symbols g bt Brake thermal efficiency c Adiabatic exponent h HRRmax Crank angle corresponding HRR max h pmax Crank angle corresponding P max Subscripts t Actual observation n Crank angle interval,°CA o Predicted output value Abbreviations ANN Artificial neural network ANOVA Analysis of variance ATAC Active thermo atmosphere combustion CI Compression ignition CO Carbon monoxide CZO Copper-doped zinc oxide DI Direct injection EGR Exhaust gas recirculation

Original scientific paper https://doi.org/10.2298/TSCI190602459SThis study deals with the performance evaluation of a simple low-cost greenhouse dryer using energy and exergy analysis. Drying experiments were conducted under the open Sun and in greenhouse dryer with two different cover sheets of ultra violet polyethylene and drip lock under passive mode and active mode for two vegetables with medicinal values: ivy gourd (coccinia grandis) and turkey berry (solanum torvum). Thermal efficiency, exergy efficiency, and improvement potential were evaluated and presented. The experiment showed that performance of the greenhouse dryer was better than the open sun drying. Thermal efficiencies were up to 30.64% and exergy efficiency values were up to 0.09% and the maximum values were obtained during the drying of ivy gourd with the drip lock sheet under active mode. The results showed that this dryer could be used for drying agricultural products at low cost by the farmers in order to produce value added products from their harvested products.Drying or dehydration of food products decreases the moisture in the products to reduce weight, for easy transportation and increase shelf life. Traditionally agricultural products have been dried under the open Sun. It is a simple and economic process but has a lot of disadvantages, like depending on weather, slow process, loss due to birds, dust and other contamination. Best quality of dried products can be achieved with industrial hot air dryers, but it requires a huge amount of energy. Approximately 9.25% of the available energy in the developed countries is being consumed by drying process [3]. So, new cost and energy effective systems are essential to reduce the energy consumption. The objective of a drying system is to supply more heat to the product than available in the ambient [4]. It needs a proper thermodynamic analysis of a drying system.Energy and exergy analysis using First and Second law of thermodynamics should be performed for finding out the energy interactions and thermodynamic behavior of a drying system [5]. The analysis of a dryer should provide a quantitative measure of inefficiencies for a designer to design better systems [6]. The exergy analysis delivers suitable information to select the appropriate design and operational procedure for better performance [7].Exergy is the maximum work attained from a stream of matter, heat or work as it comes to equilibrium with a reference environment [8]. By carrying out exergy accounting in smaller and smaller subsystems, we are able to draw a map of how the destruction of exergy is scattered over the engineering system [9]. The lesser thermal efficiency of drying systems, higher price of fossil fuel and electricity and the GHG emitted from drying systems increase the importance for exergy analysis of drying systems [10]. Various exergetic indicators used in food industry are absolute exergy loss, exergetic efficiency, improvement potential, exergy destruction ratio, entropy generation, and cumulative exergy loss. Grass...

Experiments have been carried out to compute performance, combustion and emission characteristics of a homogeneous charge compression ignition-direct injection (HCCI-DI) engine in which 20% of the fuel was premixed in the intake manifold and the remaining 80% of the fuel was injected directly. Gasoline was selected as the premixed fuel and three different fuel combinations, namely, diesel, B50 (50% waste cooking oil (WCO) and 50% diesel by volume) and WCO were selected as direct injection (DI) fuels. 100 ppm of FeCl 3 nanoadditive was blended with the DI fuels aimed at enhancing favourable fuel properties. The experimental investigations show a reduction of 54.17% and 50% in hydrocarbon (HC) and carbon monoxide (CO) emissions, respectively, in the case of WCO fuelled DI combustion compared with the diesel fuelled combustion. Significant increase in the cylinder pressure (p cyl) and the rate of heat release (ROHR) values was observed when the FeCl 3 nanoadditive blended fuel was used. Also, with this type of fuel smoke emissions were reduced by 34.8%. Significant increase in the brake thermal efficiency (η bth) with reduced nitrogen oxide (NO x) emissions was observed in the HCCI-DI combustion. Artificial neural network (ANN) was used for forecasting the performance of and emissions from the engine in different operating conditions. The technique for order preference by similarity to ideal solution (TOPSIS) was used for optimizing engine input parameters, which can result in maximum efficiency and minimum emissions.

No abstract

No abstract