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The influence of calcium carbonate (CaCO3) with different polymorphs (calcite and aragonite) and morphologies (granular and rod-like) on mechanical and crystallization properties of polypropylene (PP) was investigated. Meanwhile, these CaCO3 fillers coated with oleic acid were added in different contents to PP. The results indicate that the tensile strength, flexural strength, modulus, and crystallization property of the filler-treated samples are improved, but the impact strength decreased. The crystallinity of the composites is higher than that of neat PP. Moreover, in the rod shape filler-treated sample, in both whisker species, the mechanical properties of composites are superior to the particles filled. Differential scanning calorimetry, X-ray diffraction, and mechanical tests display that calcite whisker-reinforced composite has higher crystallization enthalpy, melting enthalpy, degree of crystallinity, and mechanical properties than aragonite whiskers and calcite particles filled composites.

Although traditional fault diagnosis methods are proficient in extracting signal features, their diagnostic interpretability remains challenging. Consequently, this article proposes a conditionally interpretable generative adversarial network (C-InGAN) model for the interpretable feature fault diagnosis of bearings. Initially, the vibration signal is denoised and transformed into a frequency domain signal. The model consists of the two primary networks, each employing a convolutional layer and an attention module, generator (G) and discriminator (D), respectively. Latent code was incorporated into G to constrain the generated samples, and a discriminant layer was added to D to identify the interpretable features. During training, the two networks were alternately trained, and the feature mapping relationship of the pre-normalized encoder was learned by maximizing the information from the latent code and the discriminative result. The encoding that represents specific features in the vibration signal was extracted from the random noise. Ultimately, after completing adversarial learning, G is capable of generating a simulated signal of the specified feature, and D can assess the interpretable features in the vibration signal. The effectiveness of the model is validated through three typical experimental cases. This method effectively separates the discrete and continuous feature coding in the signal. [ABSTRACT FROM AUTHOR]

With the wide deployment of charging piles and the development of vehicle-to-grid technology, electric vehicles (EVs) will have more opportunities to participate in the operation and scheduling of electric power system through the EV aggregator (EVA), an intermediate agent between the power grid and EV users. Quantitatively evaluating the response capability (RC) of EVA, i.e., charging and discharging power ranges, is of fundamental importance for its trading with the operator of power grid. This article proposes a user-aware method to obtain a more accurate range of multitimescale RC considering the response willingness of EV users. First, a temporal RC evaluation model of a single EV considering charge-discharge state and state of charge (SOC) is established. Second, based on the user psychology model, which reflects the relationship between users' responsivity and incentive price, a multitimescale RC evaluation model of EVA considering users' willingness is built. The day-ahead RC of EVA is evaluated by the state prediction data of EVs. According to the control strategy which considers response time and SOC indicators comprehensively, the RC evaluation is updated intraday. Finally, using the statistical data from the EU MERGE project, the effectiveness of the proposed evaluation model is verified, and the impact of incentive price and scheduling time scale on the RC of EVA are analyzed. The results indicate that the proposed model can effectively track the scheduling goals of the system and realize the dynamic update of the RC of EVA. [ABSTRACT FROM AUTHOR]

• Developed a novel electric-thermal-hydrogen system for enhanced renewable energy use. • Used SAC algorithm to solve energy scheduling as a Markov decision process. • Validated the system's economy, effectiveness, stability, and generality. The increasing load demands and the extensive usage of renewable energy in integrated energy systems pose a challenge to the most efficient scheduling of integrated energy systems (IES) because of the unpredictability and volatility of both the load side and renewable energy.Integrating heat storage and hydrogen storage technologies into integrated energy systems can effectively alleviate the phenomena of wind and solar power desertion caused by the instability of renewable energy sources. This study introduces a real-time optimal scheduling method based on the Soft Actor-Critic (SAC) algorithm, aimed at reducing system operating costs and enhancing the consumption rate of renewable energy. First, the established mathematical model is converted into a Markov Decision Process (MDP), the objective function is converted by designing the action state space and the reward function, and the deep reinforcement learning framework is established. Finally, the decision-making outcomes of intelligence in various energy storage scenarios of renewable energy consumption and extreme cases are analyzed and compared, and the results show that the heat storage and hydrogen storage system significantly improve the rate of renewable energy consumption and the economy of the system. This method is also shown to significantly improve the rate of renewable energy consumption. [ABSTRACT FROM AUTHOR]