• Energy Research
• 2016-2025close
• Closed Access close
• Open Source close
• CN close
• CA close
• DE close

Clostridium kluyveri is an anaerobic microorganism that is well-known for producing butyrate and hexanoate using ethanol and acetate. It is also an important bacterium in the production of Chinese strong flavour baijiu (SFB). To obtain a comprehensive understanding of its metabolism, a curated genome-scale metabolic model (GSMM) of C. kluyveri, including 708 genes, 994 reactions, and 804 metabolites, was constructed and named iCKL708. This model was used to simulate the growth of C. kluyveri on different carbon substrates and the results agreed well with the experimental data. The butyrate, pentanoate, and hexanoate biosynthesis pathways were also elucidated. Flux balance analysis indicated that the ratio of ethanol to acetate, as well as the uptake rate of carbon dioxide, affected hexanoate production. The GSMM iCKL708 described here provides a platform to further our understanding and exploration of the metabolic potential of C. kluyveri.

Abstract The accurate information of the thermal stresses and temperature in isotropic elastic solids is the key for many engineering applications. At present the classical linear coupled theory of thermoelasticity deduced with the assumptions of small temperature changes is widely used to solve the thermoelastic problems in engineering. In this paper, to describe the thermoelastic behavior in isotropic solids undergoing large temperature changes more accurately, the novel coupled models of thermoelasticity and the corresponding finite element models have been presented explicitly and validated by experimental measurement. The effect of large temperature changes on the solutions of thermoelastic problems is discussed. For the heat transfer process, if the isotropic elastic solids will expand when heated and contract when cooled and the condition d E E d T · σ i j E − δ i j 1 − 2 ν α 0 can be met in the context of small deformations, the effect of large temperature changes can be regarded as increasing the specific heat. The proposed models are applied to solve two thermoelastic problems. From the obtained numerical results, the effect of large temperature changes will increase with the amplitude of temperature change and may be considerably even when the temperature changes slowly.

Abstract This study investigates the non-linear relationship between urbanization paths and CO2 emissions in selected South, South-East, and East Asian countries over the period 1971–2014. Based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework, we applied the advanced and robust methods of dynamic seemingly unrelated regression (DSUR), dynamic OLS (DOLS), and fully modified OLS (FMOLS) to estimate the long-term effects. The empirical findings revealed the inverted U-shaped effects of urbanization and urban agglomeration and the U-shaped impact of the largest city ratio on CO2 emissions. Urbanization and urban agglomerations improve environmental quality in the long-run and support ecological modernization theory. However, excessive concentration in the largest cities have severely affected the environmental quality and violates the notion of compact-city efficiencies. Moreover, energy intensity and economic growth positively affect CO2 emissions, while trade openness negatively influences CO2 emissions. Our robustness analysis at the country-level applies the augmented mean group (AMG) panel ARDL technique, which further supports the non-linear effect of urbanization paths on CO2 emissions except for a few countries. The results of the panel Granger non-causality approach unveil bidirectional causality of energy efficiency, economic growth, urbanization, and largest city ratio with CO2 emissions. In contrast, unidirectional causality runs from urban agglomeration to CO2 emissions. Our findings have important policy implications as we suggest green urban infrastructures, eco-friendly dwellings, smart cities, country-specific trade policies, and renewable energy options to improve the environmental quality.

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Abstract Wellbore pressure gradient in gas wells is significant in designing deliquification technologies and optimizing production. At present, no model has yet to be established specifically for gas wells at a wide gas flowrate range. When calculating pressure gradient in a specific gas field, engineers must evaluate these widely used models and get the best performance model at a certain range. To establish a more comprehensive model in horizontal gas wells, an experimental study was conducted to investigate the flow behavior of liquid-gas two-phase flow at different gas and liquid velocities and inclined angles in a 50 mm visual pipe. The evaluation of these widely used models against the experimental data shows that no model can predict liquid holdup at different gas velocity ranges, and huge deviations due to several reasons can be observed. After conducting a comprehensive analysis, a new liquid holdup correlation was proposed based on the Mukherjee–Brill model by correlating from the experimental results, which have parametric ranges closer to the production of gas wells. This new model adopts a new dimensionless gas velocity number to characterize flow similarities and better scale up pressure from the experiment to the gas wells. By validating against experimental data and field data, the results indicate that the new two-phase flow model has stable performance and can accurately predict pressure gradients at different ranges of pressure and gas/liquid velocities.