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Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.

In order to mitigate the power density shortage of current energy storage systems (ESSs) in pure electric vehicles (PEVs or EVs), a hybrid ESS (HESS), which consists of a battery and a supercapacitor, is considered in this research. Due to the use of the two ESSs, an energy management should be carried out for the HESS. An optimal energy management strategy is proposed based on the Pontryagin's minimum principle in this research, which instantaneously distributes the required propulsion power to the two ESSs during the vehicle's propulsion and also instantaneously allocates the regenerative braking energy to the two ESSs during the vehicle's braking. The objective of the proposed energy management strategy is to minimize the electricity usage of the EV and meanwhile to maximize the battery lifetime. A simulation study is conducted for the proposed energy management strategy and also for a rule-based energy management strategy. The simulation results show that the proposed strategy saves electricity compared to the rule-based strategy and the single ESS case for the three typical driving cycles studied in this research. Meantime, the proposed strategy has the effect of prolonging the battery lifetime compared to the other two cases.

Abstract Heat pipes have been extensively applied in the domain of energy conversion and heat recovery application. A novel radial heat pipe with internally finned condenser has been proposed to recover waste heat from air conditioning units. Full mathematical description and computational fluid dynamics model were developed to predict the loop flow and thermal performance in a single radial heat pipe, respectively. The research focuses mainly on the effects of input power, filling ratio, pipe diameter, and velocity of the cooling water on the thermal performance and entropy generation rate of this novel heat pipe. Numerical results illustrate that average reductions in total thermal resistance with utilization of fins approximately achieved 8.69% and 14.78% for fins nf = 4 and nf = 8, respectively. Similarly, entropy generation rate shows an average decrease of 19.4% and 37.5% for identical operations. Additionally, the effect of internally finned condenser on the multiphase fluid flow has been comprehensively analyzed, including the break of the condensed film, generation of bubbles and nucleation boiling. The results further indicate that incorporation of internal fins has a significant influence on enhancing thermal homogenization and waste thermal recovery efficiency of a radial heat pipe. Case results agreed well with experimental data within average deviation being no more than 10%.

Abstract Hybrid nanofluids are a novel class of colloidal fluids which have drawn significant attention due to potential tailoring of their thermo-physical properties for heat transfer enhancement by a combination of more than one nano-additive to meet specific requirements of an application. In the present work, ceramic copper oxide/carbon (SiO2-CuO/C) nanoparticles in 80:20 (wt%) composition were prepared by ultrasonic-assisted wet mixing technique. The hybrid nanofluid was formulated by dispersing the nanoparticles into a base fluid mixture of 60:40 (% by mass) glycerol and ethylene glycol (G/EG) using the two-steps method. The influence of nanoparticles on the augmentation of specific heat, thermal conductivity and viscosity was examined in the volume concentration range of 0.5–2.0% in the temperature range of 303.15–353.15 K. The results demonstrate that the synthesized SiO2-CuO/C hybrid nanoparticles enhanced the thermo-physical properties of the base fluid mixture which is higher than using SiO2 alone. In the case of SiO2–G/EG nanofluid, the specific heat capacity decremented by a maximum value of 5.7% whereas the thermal conductivity and viscosity incremented by 6.9% and 1.33-times as compared with G/EG at maximum volume concentration of 2.0% at a temperature of 353.15 K. Comparatively, a reinforcement of 80% SiO2 with 20% CuO/C in G/EG mixture led to thermal conductivity and viscosity enhancement by 26.9% and 1.15-times, respectively with a significant reduction of specific heat by 21.1%. New empirical correlations were proposed based on the experimental data for evaluation of thermophysical properties.

Humidification dehumidification water desalination system using two cycles of air flow (open and closed-air cycles).

Abstract It is well known that Espinosa-parades et al. (Espinosa-Paredes et al., 2011) proposed a fractional neutron point kinetic (FNPK) model with multi-group of delayed neutrons to describe dynamic behavior in a nuclear reactor. In (Aboanber and Nahla, 2016b), Aboanber and Nahla presented an extension of Espinosa-parades et al. FNPK model. This new model is called as the corrected fractional neutron point kinetic (CFNPK) model. The present study is concerned with the numerical solution of the CFNPK model (Aboanber and Nahla, 2016b) with six groups of delayed neutron precursors. The fractional derivative is described in the sense of Grunwald-Letnikov. An implicit finite difference method (FDM) is constructed for the solution of CFNPK model. The stability analysis of the method is carried out. We analyze the results of neutron density for different values of anomalous diffusion order, reactivity function, relaxation time and time step size. In addition, we compare the results corresponding to CFNPK model (Aboanber and Nahla, 2016b) with the results corresponding to the FNPK model proposed by Espinosa-parades et al. (Espinosa-Paredes et al., 2011). It is shown that as anomalous diffusion order decreases or simulation time increases the difference between the values of neutron density increases. We have investigated the effects of each term of the CFNPK.

Abstract Emissions and efficiency of a pellet boiler (40 kW) at nominal load were compared with emissions and efficiency at reduced load, while fired with six biomass pellets. The pellets include reed canary grass ( Phalaris arundinacea ), pectin waste from citrus shells ( Citrus reticulata ), sunflower husk ( Helianthus annuus ), peat, wheat straw ( Triticum aestivum ) and wood pellets. The measurements of emissions comprised of carbon monoxide (CO), nitrogen oxides (NO x ), sulphur oxides (SO x ) and flue dust mass concentrations (using DIN plus and isokinetic sampling techniques). Emissions varied as a function of operational loads, for each type of pellets. The CO emissions were insignificant with reed canary grass (RCG), citrus pectin waste (CPW) and straw pellets at nominal load, however, at reduced load same pellets emitted 1.9, 4.0 and 7.4 times higher CO than wood pellets, respectively. Peat pellets emitted maximum CO at nominal load (4221.1 mgNm −3 , 12.6 times higher than wood pellets) however; at reduced load CO emission was insignificant. The highest NO x emissions were reported with CPW, which were 3.4 and 4.6 times higher than wood pellets at nominal load and reduced load, respectively. Dust emissions were highest with sunflower husk and lowest with RCG pellets, at both operational modes. The best performance was reported with wood pellets, followed by RCG and pectin pellets, however, wood pellets combustion emitted 1.7 and 2.0 times higher dust DIN plus than RCG at nominal and reduced loads, respectively. Not only fuel specific combustion optimization but also operational load specific optimization is essential for efficient use of agro-pellets in this type of boilers.

Abstract Phase-change materials (PCMs) can store and release great amount of thermal energy during the phase change and thus have broad application prospects in thermal energy management, waste heat recovery, building energy conservation, and other fields. However, the before and after state changes of PCMs are often accompanied by liquid leakage, thus severely limiting their application. Introducing supporting materials can solve this leakage problem but at the expense of phase-change enthalpy and service life. Herein, a novel comb-like structural phase-change composite with high latent heat was designed by using poly (ethylene glycol) (PEG) chain, which tightly intertwines with a comb-like structural phase-change supporting material under induced dipole force due to structural compatibility. This material can achieve shape stability and obtain high phase-change enthalpies (168.9 J/g–200.3 J/g). Furthermore, the composite does not show phase separation due to the good compatibility, and its phase-change temperatures and enthalpies can be adjusted by regulating the content and molecular weight of the loaded PEG.

In order to enhance the efficiency and benefits of the sludge anaerobic digestion process, K2FeO4 was added to a sludge anaerobic digestion system, and its effects on the system were comprehensively investigated. Results showed that sludge anaerobic digestion was greatly improved by adding 500 mg/L K2FeO4. Biogas and methane productions were increased by 26.6 and 28.4%, respectively. Sludge reduction, protein removal, and the conversion efficiency of dissolved organics were all improved. The mechanism revealed that the disintegration of sludge flocs, enhancement of protease activity, and decrease of soluble sulfide toxicity on microorganisms contributed to biogas production and sludge reduction. Biogas quality was improved, benefitting from the decreasing H2S content in biogas; as additionally, the cost of biogas desulfuration was reduced. In the biogas slurry treatment, the PO43--P concentrations were decreased by 39%, which also reduced the cost of the dephosphorization processes at certain extent.