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In this work, a hybrid method combining interparticle-potential multiphase LBM, finite difference method and characteristic-line wetting scheme, is developed for pore-scale simulation of superheated vapor displacing liquid, driven by gravity, in a porous geometry. The influence of injected vapor superheat, surface wettability and gravity on two-phase displacement and heat transfer processes is investigated. Results show that in thermal displacement, the vapor-liquid interface is unstable and trapped liquid blobs gradually evaporate into vapor. At low vapor superheat degrees, the displacement efficiency, as compared to isothermal displacement, is significantly improved, but it reduces as the vapor superheat degree rises due to the shift of displacement patterns. At all contact angles considered, the displacement always exhibits viscous fingering, although vapor flowpaths become wider with increasing surface wettability. The final vapor saturation is insensitive to surface wettability, but reducing contact angle leads to a less even temperature distribution inside vapor and a decrease in final average vapor temperature. In addition, increasing gravity is found to reduce vapor front temperature, but it almost has no effect on final average vapor temperature because the final average vapor temperature does not only depend on specific interfacial length but also on fingering number and inlet flow rate. • A hybrid method is developed for superheated vapor flooding in porous media. • The effect of vapor superheat degree, surface wettability and gravity is studied. • The displacement efficiency first rises and then falls with vapor superheat degree. • Decreasing contact angle leads to less even temperature distribution inside vapor. • The injection of superheated vapor is found to enhance interface instability. [ABSTRACT FROM AUTHOR]

• Three pehtze stages in sufu production were characterized by high enzyme activities. • Four aging stages in sufu production were characterized by the presence of diverse targeted volatile compounds. • Protease and esterase were strongly correlated with most of the targeted volatile compounds. • Esterase contributed to the formation of three high molecular weight esters. Dynamic changes and correlations between physicochemical properties, 14 targeted volatile compounds (TVCs) and six groups of enzyme activities during eight production stages of naturally fermented plain sufu were explored. Multiple factor analysis was used to discriminate between and group the samples into three clusters: cluster I comprised tofu and pehtze; cluster II comprised dried pehtze and salted pehtze; cluster III involved the aging stages. Clusters I and II were characterised by higher enzyme activities, while cluster III was characterised by the presence of diverse TVCs. Protease and esterase were strongly correlated with most of the TVCs. Esterase, in particular, contributed to the formation of three high molecular weight esters, namely, ethyl dodecanoate, ethyl (Z)-9-octadecenoate and ethyl (Z , Z)-9,12-octadecadienoate. The enzymes found contributed to the texture and flavour of naturally fermented sufu and will provide a good guide and control for using the enzymes directly to ripen sufu products. [ABSTRACT FROM AUTHOR]

Accurate prediction of renewable energy generation acts as a critical role which not only provides short-term power generation in the future, but also facilitates scheduling and pre-configuration of energy storage systems. More importantly, the power generation prediction is of great significance to the demand response management (DRM) of renewable energy to participate in the electricity spot market. Therefore, DRM helps improve the stability and reliability of renewable energy systems. This paper presents a novel prediction-smoothing based methodology to reduce and eliminate the influence caused by the uncertainty of renewable energy output. Firstly, the Whale Optimization Algorithm (WOA) is combined with Long Short-Term Memory (LSTM) to predict short-term wind power output. Then, the Hampel-Butterworth-SG filtering strategy with outlier regression and specific risk band elimination is introduced. After that, according to the short-term output forecast results, the scheduling and pre-configuration scheme of peak-filling type energy storage is developed. Finally, based on the predicted renewable energy output and electricity price, the dynamic changes of demand response (DR) are calculated based on Logistics function, optimistic response and pessimistic response factors. Through extensive case studies, it is demonstrated that the assessment deviations in different scenarios is less than 5%, which are 0.64% for Scenario 1 and 3.64% for Scenario 2, and no additional penalty is required at this time. In addition, the proposed Demand Response Load Adjustment Rate (DRLAR) help compare the differences between predicted and actual DR, which are DRLAR = 0.03% in Scenario 1 and DRLAR = 0.01% in Scenario 2. Users are able to adjust the DR dynamically according to the electricity price to realize the optimal scheduling of their renewable resources. The proposed methodology creates a special connection between DRM and renewable energy prediction, which provides a reliable reference for future work. [ABSTRACT FROM AUTHOR]