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Abstract A new formulation for the evaporation, flashing, condensation processes taking place in the effects of thermal desalination systems which simulates the operation of both forward and parallel/cross configurations is coupled with an exergo-economic model based on the SPECO method. The thermo-economic model uses accurate properties for the seawater, brine, pure water and vapour and is solved with an equation solver which does not require the development of a specific solution algorithm as in most previous studies. This flexible model is used to analyze the influence of the number of effects N and the temperature difference ΔT e between effects on the technical and economic performance of multi-effect desalination systems with ejector vapour compression. In particular, it is shown that the performance calculated by an earlier black-box approach is not attainable by technically and economically realistic systems. It is also shown that for each feed configuration and a given number of effects there exists an optimum value of ΔT e which minimizes the cost of the produced potable water. This last result forms the basis of a procedure that combines black-box results with the optimum value of ΔT e and can be used to select the appropriate system for any specific application.

Abstract Clamping mechanisms have significant effect on the performance of polymer electrolyte membrane (PEM) fuel cells. In this paper, PEM fuel cell with new clamping mechanism is designed to study the contact pressure distribution over the active area of PEM fuel cell's membrane electrode assembly (MEA). The clamping pressure is pneumatically exerted on the PEM fuel cell assembly. A comparison between the conventional and new clamping mechanism is carried out with simulation, and the numerical results are validated against experimental investigation performed in the fuel cell technology research laboratory. The experimental results are gathered using embedded pressure measurement films in the designed single cell. The results achieved via finite element method are in good agreement with experimental results. It is concluded that the contact pressure distribution of MEA for the new clamping mechanism is more uniform than the conventional one.

An expression for the optimum length of a flat-plate solar collector that maximizes the life-cycle savings of the collector is derived. An expression has been obtained also for the optimal distribution of a finite amount of thermal insulation that minimizes the energy loss from the back side of a flat-plate solar collector.

Abstract Process heaters are typically located outside and subject to the weather. Although heaters are typically tuned at a given set of conditions, actual operating conditions vary significantly from season to season and sometimes even within a given day. Unfortunately, most heaters are not properly adjusted for actual operating conditions. Ambient air temperature, pressure and humidity all significantly impact process heater efficiency. This paper shows how changing ambient conditions can reduce efficiency if proper adjustments are not made. Combustion efficiency is related to air:fuel ratio and to air–fuel mixing. A general industry rule-of-thumb is that operating at 2–3% excess O 2 (dry basis) results in the best combination of efficiency and flexibility. At higher O 2 levels, efficiency is reduced because the additional O 2 and N 2 absorb heat, much of which exits the exhaust stack. At lower O 2 levels, efficiency can be substantially reduced because some fuel is uncombusted. Low O 2 levels can also lead to soot and coke buildup on process tubes reducing heat transfer to the process fluid and reducing efficiency. Several examples demonstrate how ambient conditions affect heater efficiency. Calculations and graphs for a wide range of operating conditions demonstrate how efficiency can be affected by changes in ambient conditions for process heaters.

Abstract A criterion, based on optimization principles, for determining the SAT setpoint in VAV systems is presented. It is generally accepted that conventional SAT reset controls (SATRC), bounded by either space humidity or ductwork size, will save cooling and/or heating energy. How-ever, the ventilation consequences and penalty resulting from increased fan power have generally been overlooked. Ventilation is impacted since changes in the SAT setpoint change the primary airflow rate and the operation of economizer cycles, i.e. the distribution of fresh outdoor air (OA). These changes may result in extra energy demand and ventilation inefficiency if the reset criterion is not appropriate. This optimization concept simultaneously reduces energy consumption and meets ventilation requirements. Simulation results illustrate that the use of the optimized SATRC saves more energy than a conventional one.

Abstract This article introduces an effective stochastic operation framework for optimal energy management of the shipboard power systems including large, nonlinear and dynamic loads. The proposed framework divides the ship power system into several agents, which coordinate with each other based on their demands/supplies until. The alternating direction method of multipliers (ADMM) is deployed as the multi-agent framework to solve the reformulated distributed energy management problem in the ship. Two types of turbo-generators are considered in the proposed system model, including single-shaft and twin-shaft models, to increase the part-load efficiency in certain times when facing variable speed operation. The proposed distributed framework is equipped with a recursive mechanism, which helps the ship system for running optimal load scheduling when facing insufficient power generation. In order to model the uncertainty effects associated with the forecast error in the interval-ahead load demand, a stochastic framework based on unscented transform is devised which can work in the nonlinear and correlated environments of shipboard power systems. Due to the nonlinear cost function in each agent, a powerful optimization algorithm based on modified θ-firefly algorithm (Mθ-FOA) is proposed. This is a phasor algorithm, which helps for escaping from premature convergence and getting trapped in local optima. The appropriate performance of the proposed stochastic model is examined on the real dataset of a ship power system. The simulation results show the high robustness, guarantied consensus, economic operation and feasible solution when power generation shortage based on load shedding in the system.

Abstract Accurate power forecasting is of great importance to the turbine control and predictive maintenance. However, traditional physics models and statistical models can no longer meet the needs of precision and flexibility when thermal power plants frequently undertake more and more peak and frequency modulation tasks. In this study, the recurrent neural network (RNN) and convolutional neural network (CNN) for power prediction are proposed, and are applied to predict real-time power of turbine based on DCS data (recorded for 719 days) from a power plant. In addition, the performances of two deep learning models and five typical machine learning models are compared, including prediction deviation, variance and time cost. It is found that deep learning models outperform other shallow models and RNN model performs best in balancing the accuracy-efficient trade-off for power prediction (the relative prediction error of 99.76% samples is less than 1% in all load range for test 216 days). Moreover, the influence of training size and input time-steps on the performance of RNN model is also explored. The model can achieve remarkable performance by learning only 30% samples (about 216 days) with 3 input time-steps (about 60 s). Those results of the proposed models based on deep-learning methods indicated that deep learning is of great help to improve the accuracy of turbine power prediction. It is therefore convinced that those models have a high potential for turbine control and predictable maintenance in actual industrial scenarios.

Abstract To improve the conversion efficiency of thermophotovoltaic devices, we designed a thermophotovoltaic system based on an InAs/InGaAsSb/GaSb three-junction tandem cell. The tandem cell can recover photons in the wavelength range of 200–3650 nm and therefore enhance the output power of the system. To further improve system performance, we designed a multilayer circular truncated cone metamaterial emitter matching the tandem cell. Existing TPV systems based on multi-junction tandem PV cells can achieve conversion efficiencies of 33.3%–41%, while the thermophotovoltaic system coupled with the multilayer circular truncated cone metamaterial can recover more photons of 1.44 mol/(m2·s) and achieve a higher conversion efficiency of 52.8% at 1773 K. The thermophotovoltaic system designed here demonstrates an extremely high energy conversion efficiency and has good application prospects.

Abstract In this paper, energy and exergy analyses of the geothermal-based hydrogen production via thermochemical water decomposition using a new, four-step copper–chlorine (Cu–Cl) cycle are conducted, and the respective cycle energy and exergy efficiencies are examined. Also, a parametric study is performed to investigate how each step of the cycle and its overall cycle performance are affected by reference environment temperatures, reaction temperatures, as well as energy efficiency of the geothermal power plant itself. As a result, overall energy and exergy efficiencies of the cycle are found to be 21.67% and 19.35%, respectively, for a reference case.