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Abstract We screened several strains which exhibited significant photoorganotrophic growth on a chemically defined synthetic medium containing alcohols. Samples from sewage, rice fields, or other places were inoculated under anaerobic-light conditions at 30°C. After multiple enrichments, the growth characteristics of three fast growing isolates were investigated. We describe also the cloning of molecular chaperones from a purple non-sulfur bacterium.

Abstract Water fuel emulsion has been widely studied with the advantages of saving energy, enhancing engine torque, improving engine performance, and reducing the pollutant emissions. However, it has unfavorable disadvantages such as phase separation and long ignition delay. Water fuel microemulsion with rhamnolipid as the surfactant was formed in this study and characterized in comparison to water fuel emulsion. Water fuel microemulsion was thermodynamically stable without phase separation after 90 days vs. the milky-white emulsion fuel, separated within 2 days. In the thermogravimetric analysis, the TG and DTG curves were shifted to higher temperatures as the increment of heating rate. However, the shift for emulsion at 40 K min −1 was inconspicuous, which implies the reduction in heat transfer, mass transfer, and vaporization rates and further the lengthened ignition delay upon combustion in diesel engine. The activation energies ( E a ) predicted by Ozawa–Flynn–Wall (OFW), Kissinger–Akhira–Sunose (KAS), and Starink’s methods indicate that the formation of microemulsion could decrease the activation energy of the fuel by about 5 kJ mol −1 , while the formation of emulsion would increase by 15 kJ mol −1 . The lower activation energy of microemulsion fuel is an indication of easy ignition or shortened ignition delay. Thus, microemulsification may be a more competitive technique for fuel upgrading compared to emulsification.

Abstract A new kind of shape-stabilized phase change materials (PCMs) with positive temperature coefficient (PTC) effect was prepared in this paper. The materials were prepared by adding graphite powder (GP) to the paraffin/low density polyethylene (LDPE) composite and the PTC characteristic was found by adjusting the component ratio of the material. Then the physical structures and thermal properties of the materials were investigated and the effect of various GP mass fractions and paraffin/LDPE mass proportions on the PTC behavior of the materials was studied experimentally. The results showed that the switching temperature of the materials was about 25 °C (room temperature) which approached to the first phase change temperature of paraffin dispersed in the materials. The PTC behavior of the materials was the best when the GP mass fraction and the mass proportion of LDPE/paraffin were 40 wt% and 30:70, respectively. Furthermore, the negative temperature coefficient (NTC) effect of the materials could be eliminated effectively with heat treatment. This new kind of materials is different from the former PTC materials which the switching temperatures focus on high temperature ranges. It makes up for the defect of previous materials that the switching temperatures only range in high temperature rather than room temperature and provides a potential means for the thermal control of the electronic devices or other room temperature thermal control applications.

Abstract This paper presents an ultra-low frequency vibration energy harvester using a zigzag piezoelectric spring oscillator, which consists of two piezoelectric zigzag springs and a rolling metal ball. The metal ball rolls and drives the piezoelectric springs to deform to harvest energy when a slight vibration occurs in the external environment. The natural frequency of zigzag spring oscillator piezoelectric energy harvester (ZSO-PEH) is related to the length of the spring and the weight of the ball, correlation analysis is carried out by theoretical derivation and ANSYS simulation. It is found experimentally that the proposed device offers efficient energy output in ultra-low frequency excitation. A maximum output power of 5.68 mW is achieved under the best matching resistance of 5.1 k Ω at the excitation frequency of 3 Hz. The performance of energy harvester can be optimized by adjusting the length of the spring and the mass of the ball. The results show that the proposed piezoelectric energy harvester has the potential to power low-power electronic devices and wireless sensor nodes.

Abstract An attempt was made to develop multiple regression models for office buildings in the five major climates in China – severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89–97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.

Thermally integrated pumped-thermal electricity storage (TI-PTES) offers the opportunity to store electricity as thermal exergy at a large scale, and existing studies are primarily focused on TI-PTES systems based on high-temperature thermal energy storage. This paper presents a thermo-economic analysis of a “cold TI-PTES” system which converts electricity into cold energy using a vapor compression refrigeration (VCR) unit and stores it at sub-ambient temperatures during the charging process, and generates electricity by using an organic Rankine cycle (ORC) working between the sub-ambient temperature and an external low-grade heat source during the discharging process. The effects of key parameters, i.e., mass flowrate and temperature of the storage medium, ORC evaporation temperature, component efficiencies, and pinch-point temperature differences, on the system performance are evaluated based on a whole-system thermo-economic model. The results reveal that the roundtrip efficiency and levelized cost of storage (LCOS) of the system increases while the electrical energy storage capacity decreases as the temperatures of the two cold storage tanks approach each other. When the temperature of the cold storage tank 1 rises from 1 °C to 8 °C while the cold storage tank 2 remains as 13 °C, there is an increase of 25% and 20% in the roundtrip efficiency and LCOS respectively while the energy storage capacity decreases by 69%. A roundtrip efficiency of 0.74 and LCOS of 0.32 $/kWh are achieved with a heat source temperature of 85 °C, using a mass flowrate and temperature of the cold storage medium of 50 kg/s and 1 °C. Furthermore, any change in cold storage medium mass flowrate changes both electrical energy storage capacity and power output by the same proportions. With a continuous high-flowrate external heat source, the LCOS can be as low as 0.17 $/kWh. By providing sufficient heat from an external heat source, the proposed system possesses a high potential for medium-to-large scale energy storage with a unique hybrid ...

Abstract A new air-cooled gas-steam combined cycle cogeneration system with absorption heat pump for recovering waste heat from exhausted steam of the steam turbine to achieve double effects of waste heat recovery and water saving is proposed based on a conventional water-cooled gas-steam combined cycle cogeneration system in the paper. The property criteria variation is analyzed before and after modification. In addition, the exergy analyses of primary equipments are carried out based upon the exergy analysis theory. The results demonstrate that the net generating power is approximately increased by 11,082 kW, equivalent coal consumption is reduced by 2.71 g/kWh, the net overall thermal efficiency is improved by 0.91% with 334,245 kW heating load at 100% load of the gas turbine in the modified system. Besides, the overall exergy loss is decreased by 6448 kW and exergy efficiency is improved by 0.98%. The overall property of the whole system is improved. The results show that the property reduction caused by air-cooling modification can be made up by the property improvement due to waste heat recovery. Moreover, the cooling circulating water can be saved by 1196.34 kg/s. The presented measure can not only improve performance of the system but also simultaneously achieve energy and water saving on the premise of satisfying user needs, which has a wide application potential in the water-shortage regions.

Abstract Parameter estimation of photovoltaic cells is essential to establish reliable photovoltaic models, upon which studies on photovoltaic systems can be more effectively undertaken, such as performance evaluation, maximum output power harvest, optimal design, and so on. However, inherent high nonlinearity characteristics and insufficient current–voltage data provided by manufacturers make such problem extremely thorny for conventional optimization techniques. In particular, inadequate measured data might save computational resources, while numerous data is also lost which might significantly decrease simulation accuracy. To solve this problem, this paper aims to employ powerful data-processing tools, for instance, neural networks to enrich datasets of photovoltaic cells based on measured current–voltage data. Hence, a novel improved equilibrium optimizer is proposed in this paper to solve the parameters identification problems of three different photovoltaic cell models, namely, single diode model, double diode model, and three diode model. Compared with original equilibrium optimizer, improved equilibrium optimizer employs a back propagation neural network to predict more output data of photovoltaic cell, thus it can implement a more efficient optimization with a more reasonable fitness function. Besides, different equilibrium candidates of improved equilibrium optimizer are allocated by different selection probabilities according to their fitness values instead of a random selection by equilibrium optimizer, which can achieve a deeper exploitation. Comprehensive case studies and analysis indicate that improved equilibrium optimizer can achieve more desirable optimization performance, for example, it can achieve the minimum root mean square error under all three different diode models compare to equilibrium optimizer and several other advanced algorithms. In general, the proposed improved equilibrium optimizer can obtain a highly competitive performance compared with other state-of-the-state algorithms, which can efficiently improve both optimization precision and reliability for estimating photovoltaic cell parameters.

Abstract With rapid development of artificial intelligence, data-driven prediction models play an important role in energy prediction, fault detection, and diagnosis. This paper proposes an ensemble approach using random forest (RF) for hourly performance predictions of GSHP system. Two years of in situ data were collected in an educational building situated in severe cold area in China. Prediction models were established for performance indicators, and results indicate that the average error for COPs, COPu, EERs and EERu were all controlled within 5%. The model established by small amount of data can accurately predict long-term performance, thereby reducing time and difficulty of data collection. RF models, trained with different parameter settings were compared, results indicate that model accuracy was not very sensitive to variables numbers. The impact of input variables on prediction performance was analyzed, and importance ranking changed with period and performance indicators. By comparing the variable importance list, it was possible to establish which parameters were abnormal and lists of different periods can reflect whether the energy structure of building has changed. The overall superiority of RF was verified by comparing with back propagation neural network (BPNN) from robustness, interpretability, and efficiency. First, since GSHP system involving multiple indicators, the robustness, measured by average accuracy, was used to evaluate the accuracy level. According to CV-RMSE, robustness of RF is approximately 3.3% higher than that of BPNN. Second, RF is highly interpretive but BPNN is typical black box model. Finally, modeling complexity and training time of BPNN were much greater than RF.