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SUMMARYThis paper presents the energy and exergy analyses of sugar production stages by using the operational data from Bor Sugar Plant, Turkey. For these purposes, all stages of sugar production, considered as a steady-state open thermodynamic system, were analysed by employing the first and second law of thermodynamics. In this regard, the first and second law efficiencies, the magnitude and place of exergy losses in these production stages were estimated and discussed in detail. It was concluded that the exergy loses took place mostly during the sherbet production process (Z I,sp =96.8% Z II,sp =49.3%) because of the irreversibility in the sub-operation stages, which are vapour production, circulation sherbet mixing and bagasse compression. Therefore, it is generally suggested that the irreversibility, mostly stem from the finite temperature differences at the production stages, should be reduced to conduct more productively the sugar production process.

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SUMMARYThermodynamics plays an important role to perform the energy and exergy analyses of the industrial processes. The first law is widely used in engineering practice and is the basis of the heat-balance method of analysis that is commonly used in energy systems performance analysis. However, the second law involves the reversibility or irreversibility of processes and is a very important aspect in the exergy method of energy systems analysis. From the viewpoints of energy conservation and environmental benefits, cogeneration system can be considered as one of sustainable energies. The exergy analysis allows for improvements not necessarily attainable via energy methods, like increased efficiency, reduced fuel use, and reduced environmental emissions. From this point of view, in this study, exergy analysis of an actual Diesel engine-based cogeneration plant with a total capacity of 11.52 MW electrical powers, 9 t h À1 of steam and 140 t h À1 of hot water is carried out by analyzing the components of the system separately. The results show that 39.86% of the exergy entering the plant is converted to electrical power. The net steam production of the plant constitutes 8% of the total exergy input and the hot water production of the plant constitutes only 1.26% of the total exergy input. The remaining 50.88% of the exergy input is lost. Total exergy destruction in the engine is mostly due to the highly irreversible combustion process in the engine, heat losses from engine and friction. Small improvements in engine design and operation can provide better utilization of plant performance compared to large and expensive improvements in other components.

The Heavy Weight Deflectometer (HWD) is a Non-Destructive Test (NDT) equipment used to assess the structural condition of airfield pavement systems. This paper presents an Artificial Neural Networks (ANN) based approach for non-destructively estimating the stiffness properties of rigid airfield pavements subjected to full-scale dynamic traffic testing using simulated new generation aircraft gears. HWD tests were routinely conducted on three Portland Cement Concrete (PCC) test items at the Federal Aviation Administration's (FAA) National Airport Pavement Test Facility (NAPTF) to verify the uniformity of the test pavement structures and to measure pavement responses during full-scale traffic testing. Substantial corner cracking occurred in all three of the rigid pavement test items after 28 passes of traffic had been completed. Trafficking continued until the rigid items were deemed failed. The study findings illustrate the potential of ANN-based models for routine and real-time structural evaluation of rigid pavement NDT data. Reference to this paper should be made as follows: Ceylan, H., Reference to this paper should be made as follows: Ceylan, H., . "Neural Networks Based Concrete Airfield Pavement Layer Moduli Backcalculation," Journal of Civil Engineering and Environmental Systems, Vol. 25, Issue no. 3, pp. 185-199. "Neural Networks Based Concrete Airfield Pavement Layer Moduli Backcalculation," Journal of Civil Engineering and Environmental Systems, Vol. 25, Issue no. 3, pp. 185-199. ABSTRACTThe Heavy Weight Deflectometer (HWD) is a Nondestructive Test (NDT) equipment used to assess the structural condition of airfield pavement systems. This paper presents an Artificial Neural Networks (ANN) based approach for nondestructively estimating the stiffness properties of rigid airfield pavements subjected to full-scale dynamic traffic testing using simulated new generation aircraft gears. HWD tests were routinely conducted on three Portland Cement Concrete (PCC) test items at the Federal Aviation Administration's (FAA's) National Airport Pavement Test Facility (NAPTF) to verify the uniformity of the test pavement structures and to measure pavement responses during full-scale traffic testing. Substantial corner cracking occurred in all three of the rigid pavement test items after 28 passes of traffic had been completed. Trafficking continued until the rigid items were deemed failed. The study findings illustrate the potential of ANN based models for routine and real-time structural evaluation of rigid pavement NDT data.