Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil

Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility with the petroleum-based diesel fuel (PBDF). Therefore, in this study, the prediction of the engine performance and exhaust emissions is carried out for five different neural networks to define how the inputs affect the outputs using the biodiesel blends produced from waste frying palm oil. PBDF, B100, and biodiesel blends with PBDF, which are 50% (B50), 20% (B20) and 5% (B5), were used to measure the engine performance and exhaust emissions for different engine speeds at full load conditions. Using the artificial neural network (ANN) model, the performance and exhaust emissions of a diesel engine have been predicted for biodiesel blends. According to the results, the fifth network is sufficient for all the outputs. In the fifth network, fuel properties, engine speed, and environmental conditions are taken as the input parameters, while the values of flow rates, maximum injection pressure, emissions, engine load, maximum cylinder gas pressure, and thermal efficiency are used as the output parameters. For all the networks, the learning algorithm called back-propagation was applied for a single hidden layer. Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) have been used for the variants of the algorithm, and the formulations for outputs obtained from the weights are given in this study. The fifth network has produced R² values of 0.99, and the mean % errors are smaller than five except for some emissions. Higher mean errors are obtained for the emissions such as CO, NO_x and UHC. The complexity of the burning process and the measurement errors in the experimental study can cause higher mean errors.     

A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for ?stanbul Atatürk Airport.     

Wind turbine speed control of a contactless piezoelectric wind energy harvester

ABSTRACT

Wind turbine control is an important task to make the electricity generation secure in terms of energy demand and machine safety. It also yields to control the desired power level and optimized energy because of the assignment of turbine speed. The contactless piezoelectric wind energy harvester (CPWEH) used in this study has three piezoelectric layers located around the shaft with 120 degrees apart and they are buckled by the magnetic force without any physical contact. The superiority of this device is to generate energy for low wind speeds such as 1.5 m/s. However, for high speeds, high total harmonic distortions (THDs) govern the waveforms, thus controlling the turbine speed becomes necessary for optimizing the output power. Encouraged by this, a small low inertia dc generator is coupled with the wind turbine, and the generator terminals are connected to a resistor through a power switch to generate a braking torque that opposes to wind speed direction. By controlling the switch properly, turbine speed is ensured to remain within a certain band, which accordingly prevents the turbine from rotating very fast at damaging wind speeds. Several experiments are performed on the developed CPWEH with/without the presented control scheme which prove the existence of promising performance of our proposal.     

Prioritization of chemical carcinogenesis testing: the use of production data

Based on previous work, this paper aims to show how approximate methods of estimating exposure can lead to a preliminary ranked list of chemicals for regulation of carcinogens. This list is likely to be more reliable, than those so far generated. The paper argues four main points. Firstly that an indication of the route by which the total production of a hazardous material is distributed and enters the human body should be explicitly included in calculations. Second, simple calculations based on conservative bounds should be made to establish a ranking or set of priorities for regulation. Third, an improved system for establishing regulatory priorities can be based on formal analysis using regret functions. Finally some simple, bounded, calculations based on commonly used herbicides and pesticides are made and it is shown how some regulatory processes could be modified.     

Anticonvulsant activity of furanocoumarins and the essential oil obtained from the fruits of Heracleum crenatifolium

The anticonvulsant activity of furanocoumarins, coumarin mixture and the essential oil obtained from the fruits of Heracleum crenatifolium was examined against maximal electroshock (MES)-induced seizures in mice. Bergapten showed significant anticonvulsant activity. The furanocoumarins isolated from the fruits of the plant were identified using thin-layer chromatography, melting points and spectroscopic methods (IR, MS, ¹H NMR) as isobergapten (1), pimpinellin (2), bergapten (3), isopimpinellin (4), sphondin (5) and byak-angelicol (6). The essential oil content of the fruits were found as 5.5%. Twenty-two compounds representing 99.3% of the essential oil obtained from the fruits of H. crenatifolium were determined and the major components were identified as octanol and octyl acetate (3.1% and 88.4% respectively) by GC and GC–MS.     

Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies

Recently, global warming and its effects have become one of the most important themes in the world. Under the Kyoto Protocol, the EU has agreed to an 8% reduction in its greenhouse gas (GHG) emissions by 2008–2012. The GHG emissions (total GHG, CO₂, CO, SO₂, NO₂, E (emissions of non-methane volatile organic compounds)) covered by the Protocol are weighted by their global warming potentials (GWPs) and aggregated to give total emissions in CO₂ equivalents. The main subject in this study is to obtain equations by the artificial neural network (ANN) approach to predict the GHGs of Turkey using sectoral energy consumption. The equations obtained are used to determine the future level of the GHG and to take measures to control the share of sectors in total emission. According to ANN results, the maximum mean absolute percentage error (MAPE) was found as 0.147151, 0.066716, 0.181901, 0.105146, 0.124684, and 0.158157 for GHG, SO₂, NO₂, CO, E, and CO₂, respectively, for the training data with Levenberg–Marquardt (LM) algorithm by 8 neurons. R² values are obtained very close to 1. Also, this study proposes mitigation policies for GHGs.     

Effects of annealing on the structural and magnetic properties of flame spray pyrolyzed MnFe2O4 nanoparticles

In this study, manganese ferrite (MnFe₂O₄) nanoparticles were produced through flame spray pyrolysis (FSP). To investigate the effects of heat treatment, the nanoparticles were annealed between 400 and 650°C for 4 h in air in a comparative manner. The structural, chemical, morphological, and magnetic properties of the nanoparticles were evaluated using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), dynamic light scattering (DLS), and vibrating sample magnetometry (VSM), respectively. The XRD results showed that the nanoparticles synthesized by the FSP method exhibited the MnFe₂O₄ spinel ferrite structure. The annealing process led to the decomposition of MnFe₂O₄ into various phases. According to the morphological analysis, the as-synthesized particles were hemispherical–cubic in shape and had an average particle size of less than 100 nm. In addition, the chemical bond structures of the nanoparticles were confirmed in detail by XPS elemental analysis. The highest saturation magnetization was recorded as 33.50 emu/g for the as-produced nanoparticles. The saturation magnetization of the nanoparticles decreased with increasing annealing temperature, while coercivity increased.     

Biosynthesis of the Myxobacterial Antibiotic Corallopyronin A

Corallopyronin A is a myxobacterial compound with potent antibacterial activity. Feeding experiments with labelled precursors resulted in the deduction of all biosynthetic building blocks for corallopyronin A and revealed an unusual feature of this metabolite: its biosynthesis from two chains, one solely PKS‐derived and the other NRPS/PKS‐derived. The starter molecule is believed to be carbonic acid or its monomethyl ester. The putative corallopyronin A biosynthetic gene cluster is a trans‐AT‐type mixed PKS/NRPS gene cluster, containing a β‐branching cassette. Striking features of this gene cluster are a NRPS‐like adenylation domain that is part of a PKS‐type module and is believed to be responsible for glycine incorporation, as well as split modules with individual domains occurring on different genes. It is suggested that CorB is a trans‐acting ketosynthase and it is proposed that it catalyses the Claisen condensation responsible for the interconnection of the two chains. Additionally, the stereochemistry of corallopyronin A was deduced by a combination of a modified Mosher's method and ozonolysis with subsequent chiral GC analyses.