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Abstract This paper investigates the anode electrokinetics during the start-up or warm-up period of a high-temperature proton exchange membrane fuel cell (HT-PEMFC) from a temperature above 100 °C, to the desired temperature of 180 °C, where carbon monoxide (CO) contaminated hydrogen (H2) is used as the fuel. The heating strategy considered in this study involves an initial heating of the HT-PEMFC by an external source to a temperature above 100 °C, after which current is drawn, where electrochemical reaction heating is expected to contribute to the heating process. Thus, a numerical transient three-dimensional model is derived addressing the anode electrokinetics under a specific temperature increase rate during the warm-up process. Operational parameters such as temperature increase rate, initial start-up temperature, CO volume fraction and extracted current density are varied and their effects on the CO and hydrogen coverage fractions on the electrode, the anode overpotential and the cell potential with respect to time, are studied. The results indicate that parameters such as temperature increase rate, initial start-up temperature, CO volume fraction and extracted current density would significantly affect the anode overpotential and the cell potential. Specifically, it is critical to reduce the extracted current density and CO volume fraction, and increase the temperature increase rate, to avoid a sudden shoot up of the anode overpotential that can result in the premature shutdown of the fuel cell. Having a low initial start-up temperature does result in a higher base value of the anode overpotential. The results also indicate that if the fuel cell reaches 130 ± 5 °C before the anode overpotential is overly high, then the fuel cell would not shut down prematurely due to the high anode overpotential.

Abstract Current conventional agricultural systems using intensive energy has to be re-vitalized by new integrated approaches relying on renewable energy resources, which can allow farmers to stop depending on fossil resources. The aim of the present study was to compare wheat production in dryland (low input) and irrigated (high input) systems in terms of energy ratio, energy efficiency, benefit/cost ratio and amount of renewable energy use. Data were collected from 50 irrigated and 50 dryland wheat growers by using a face-to-face questionnaire in 2009. The results showed that the total energy requirement under low input was 9354.2 MJ ha −1 , whereas under high input systems it was 45367.6 MJ ha −1 . Total energy input consumed in both dryland and irrigated systems could be classified as direct, indirect, renewable and non-renewable energies which average in two wheat production systems were 47%, 53%, 24% and 76%, respectively. Energy ratios of 3.38 in dryland and 1.44 in irrigated systems were achieved. The benefit–cost ratios were 2.56 in dryland and 1.97 in irrigated wheat production systems. Based on the results of the present study, dry-land farming can have a significant positive effect on energy-related factors especially in dry and semi-dry climates such as Iran.

Abstract The almost two-dimensional steady-state rates of heat loss from arrays of uniformly-spaced vertical rectangular fins, extending upwards—in otherwise stagnant air—from horizontal heated bases, have been measured. (The vertical air gaps between the fins were closed at their sides, by insulated vertical end-barriers.) The effects of various combinations of height, thickness and spacing of the fins, for different base temperatures (in the range 40 to 100°C), have been studied. For the configuration considered, in a normal ambient environment (∼ 20°C), there is an optimal fin spacing (⋍ 16 mm) corresponding to the greatest steady-state rate of free convective/conductive heat loss through the air from the finned system, and this is almost independent of the temperature of the heat exchanger base (in the range 40–100°C). At this optimal spacing for base temperatures not greater than 50°C, the convective/conductive heat transfer rate from the array increases with the fin height up to about 60 mm, so that it would be uneconomic to employ taller fins if convection/conduction is dominant compared with radiation. If the radiation contribution is also considered, then the optimal spacing corresponding to the maximum total steady-state rate of heat loss through the air is somewhat less than the optimal spacing for which, under the same temperature conditions, the maximum steady-state rate of convective/conductive heat leak occurs. The greater the emissivity of the heat exchanger surfaces, the narrower the optimal uniform gaps between the fins. A two-dimensional finite-difference computer program has been composed to predict the temperature distribution throughout the heat exchanger for a stipulated ambient environmental temperature and experimentally-determined distribution of the heat transfer coefficient over the surfaces of the exchanger. This enables, for instance, any hot spots to be located prior to a proposed design being built.

Abstract The present work focuses on the numerical simulation of Moderate or Intense Low oxygen Dilution combustion condition, using the Partially-Stirred Reactor model for turbulence-chemistry interactions. The Partially-Stirred Reactor model assumes that reactions are confined in a specific region of the computational cell, whose mass fraction depends both on the mixing and the chemical time scales. Therefore, the appropriate choice of mixing and chemical time scales becomes crucial to ensure the accuracy of the numerical simulation prediction. Results show that the most appropriate choice for mixing time scale in Moderate or Intense Low oxygen Dilution combustion regime is to use a dynamic evaluation, in which the ratio between the variance of mixture fraction and its dissipation rate is adopted, rather than global estimations based on Kolmogorov or integral mixing scales. This is supported by the validation of the numerical results against experimental profiles of temperature and species mass fractions, available from measurements on the Adelaide Jet in Hot Co-flow burner. Different approaches for chemical time scale evaluation are also compared, using the species formation rates, the reaction rates and the eigenvalues of the formation rate Jacobian matrix. Different co-flow oxygen dilution levels and Reynolds numbers are considered in the validation work, to evaluate the applicability of Partially-Stirred Reactor approach over a wide range of operating conditions. Moreover, the influence of specifying uniform and non-uniform boundary conditions for the chemical scalars is assessed. The present work sheds light on the key mechanisms of turbulence-chemistry interactions in advanced combustion regimes. At the same time, it provides essential information to advance the predictive nature of computational tools used by scientists and engineers, to support the development of new technologies.

Abstract Motivated by a real-world case, this paper deals with uncertainty issues in the resilient and sustainable electricity supply chain network design. Uncertainty is always found in electricity demand prediction and perfect foresight is not possible. The resilience of electricity networks in face of uncertainty can prevent catastrophic impacts. Besides, due to the growing concerns about social impacts, considering social measures along with the other measures is becoming critical when analyzing the network costs. In this paper, a multi-objective optimization model is developed, which consists of the total cost in the first objective, resiliency measures in the second objective, and some aspects of corporate social responsibility in the third objective. The resiliency measures minimize de-resiliency, including successive establishment, distributed generators inadequacy, congestion through electrical lines, and energy dissatisfaction level. A novel robust approach is also proposed to deal with the uncertainty of electricity demand based on the light robust programming and possibilistic theory in the fuzzy logic. By investigating a real case in Iran, the contributions of the considered measures, and the effects of uncertainty are analyzed. The analyses reveal that by applying the proposed model the decision-makers can enhance corporate social responsibility and resiliency by 50% and 20%, respectively, although it increases the total cost by 50%. Moreover, the results of applying the proposed fuzzy-robust approach show that how the decision-makers can effectively allocate the uncertainty budget and protection level to attain more robust solutions in terms of standard deviation and mean value of the total cost.

In power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to different forecast values. Therefore, appropriate probabilistic models that can provide accurate information for conditional forecast error distributions are of great need. On the basis of Gaussian mixture model, this paper constructs analytical conditional distributions of forecast errors for multiple wind farms with respect to forecast values. The accuracy of the proposed probabilistic models is verified by using historical data. Thereafter, a fast sampling method is proposed to generate scenarios from the conditional distributions which are non-Gaussian and interdependent. The efficiency of the proposed sampling method is verified.

Recent improvements in low-temperature heat-to-power (LTHtP) technologies have led to an increase in efficiency at lower temperatures and lower cost. LTHtP has so far not been used in district heat ...

Abstract In Concentrated Solar Power Plants, steam turbines controlled with standard Proportional Integrative Derivative (PID) methods may suffer from performance downgrading in power generation when the steam conditions deviate from nominal ones. An enhancement of standard steam turbine controller can be the key to achieve optimal performance also in non-nominal steam conditions. This paper presents the improvement of the PID control concept by exploiting Fuzzy Logic, an artificial intelligence technique that allows taking into account the human experience and knowledge on the system behavior. A real Concentrated Solar Power Plant has been modeled focusing on generated power control loop, its stability and performance analysis, knowledge useful to design a Fuzzy Inference System. A fuzzy logic controller is proposed to continuously adapt the PID parameters, to improve the steam turbine governor action. Its performance is compared to the classical PID tuned according to three different approaches. The fuzzy logic PID controller extends the simplicity of PID and adapts the control action to actual operating condition by providing the system with a sort of “decision-making skill”. The possibility to design implementable algorithms on a Programmable Logic Controller, which have stringent computational speed and memory requirements, has been explicitly taken into account in the developed work, through the minimization of the controller complexity with a reduced number of fuzzy sets and fuzzy rules within the fuzzy inference system.