Peer Review Statementdoi: 10.1088/1742-6596/2938/1/011002pmid: N/A
All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.• Type of peer review: Double Anonymous• Conference submission management system: Morressier• Number of submissions received: 30• Number of submissions sent for review: 18• Number of submissions accepted: 10• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 33.3• Average number of reviews per paper: 2.7• Total number of reviewers involved: 62• Contact person for queries:Name: Heidi XueEmail: [email protected]: EMET2024 Committee
The removal of ammonia nitrogen from wastewater by using modified Zeolite particles under ultrasonic cavitationHe, Pengde; Liu, Fengbin; Yan, Hongjuan; Dou, Zhaoliang; Chen, Wenbin
doi: 10.1088/1742-6596/2938/1/012001pmid: N/A
The modified natural zeolite and artificial zeolite were used to treat ammonia nitrogen solution under the combination of aeration and ultrasonic cavitation. The adsorption process was analyzed by means of kinetic model, adsorption isothermal model and internal diffusion model. In addition, the surface microstructures of the modified zeolites before and after the experiment was characterized. The results showed that the kinetic processes of the two modified zeolites under aeration and ultrasonic processes can be better described with the pseudo-second-order kinetic model. And, the adsorption process conforms to the Freundlich isothermal equation better. The modified zeolite can easily adsorb ammonia nitrogen for its stronger affinity for NH4+ compared to the untreated zeolite. At the same time, the physical adsorption capacity and ion exchange capacity of the modified artificial zeolites under aeration and ultrasonic cavitation processes are stronger than that of the modified natural zeolites under the same conditions, which contribute to the higher removal fraction of the ammonia nitrogen. The different adsorption capacity and ion exchange capacity between the modified artificial zeolites and modified natural zeolites can be attributed to their different microstructures.
Enhancing in-situ air injection conversion: catalytic roles of metal oxides in kerogen-rich shale oxidationPu, Wanfen; Li, Tao; Jin, Xing; Liu, Renbao
doi: 10.1088/1742-6596/2938/1/012005pmid: N/A
In-situ air injection conversion technology represents one of the pivotal methods for developing kerogen-rich shale, wherein catalysts play a crucial role in enhancing the oxidation reaction rate and boosting the development efficiency. This paper systematically investigates the catalytic effects of several metal oxide catalysts (NiO, CoO, Fe2O3, CuO, and MoO3) in the oxidation reactions of kerogen-rich shale through rising temperature oxidation (RTO) experiment, along with the application of the Ozawa-Flynn-Wall (OFW) and Friedman conversion rate model methods. The study aims to provide a reference for the development of oxidation catalysts for kerogen-rich shale. The results indicate that during the rising temperature oxidation process, the shale undergoes three primary reaction stages: Low-temperature oxidation (LTO), Fuel deposition (FD), and High-temperature oxidation (HTO). The addition of NiO effectively reduces the activation energies of the LTO, FD, and HTO stages in shale to 83, 179, and 233 kJ/mol respectively, demonstrating a pronounced catalytic effect. In contrast, CoO and MoO3 decrease the activation energies to 149/170 kJ/mol and 171/229 kJ/mol respectively during the LTO/FD stages. However, introducing Fe2O3 and CuO catalysts significantly increases the oxidation activation energies across all stages, indicating that these two transition metal oxides exert an inhibitory effect on the shale oxidation process. This study preliminarily reveals the catalytic effects of various transition metal catalysts on the oxidation reactions of kerogen-rich shale, offering valuable insights and references for the development of oxidation catalysts for such reservoirs.
Preparation of CaSrSiOx: nCe4+ luminous material with rice husk as the silicon sourceLi, Haocheng
doi: 10.1088/1742-6596/2938/1/012003pmid: N/A
Rice husk ash is the product of rice husk combustion, and its output is quite large. Currently, its direct applications based on physical adsorption and high activity are widespread, but studies on using the main component of rice husk ash, silicon dioxide (SiO2), as a silicon source to prepare luminescent materials to enhance its value are relatively rare. Therefore, this paper utilizes the silica in rice husk ash to prepare luminescent materials, aiming to achieve high-value utilization of waste and environmental protection. In this experiment, rice husk ash was used as a silicon source to prepare CaSrSiOx (x=3,4): nCe4+ materials. The results show that the concentration of Ce ions has no significant effect on the crystal structure of the material. When the calcination temperature is 1000°C and the concentration of %Ce4+ is 1 mol%, the luminescent effect is optimal, and blue light is emitted under 375 nm light excitation. The concentration of Ce4+ and the calcination temperature have little impact on the CIE chromaticity coordinates of the phosphor.
CNN-BiLSTM combined with Bayesian optimization for short-term wind power predictionSong, Yahao; Wu, Yajun; Duan, Songtao; Dou, Chendan; Liu, Bei; Hou, Bingxue
doi: 10.1088/1742-6596/2938/1/012002pmid: N/A
Aiming at the difficulty of wind power prediction due to the volatility and uncertainty of wind power generation, this paper proposes a hybrid model based on Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM), and optimises the model hyper-parameters using Bayesian Optimization Algorithm (BO) in order to improve the prediction accuracy. Firstly, the input features that are highly correlated with wind power are screened using the Pearson Coefficient method (PCC). Then, CNN is used to extract features from the screened data. Next, the features extracted by CNN are further processed using BiLSTM to capture the long-term dependence and bi-directional information of the time series data. Finally, the hyperparameters of the model are adjusted by BO to obtain the best prediction performance. The experimental results show that the proposed BO-CNN-BiLSTM model reduces the RMSE by 35.3%, the MAE by 46.7%, and the R2 improves by 1.5% compared with the BiLSTM model.
PrefaceNing, Dezhi
doi: 10.1088/1742-6596/2938/1/011001pmid: N/A
The 4th International Conference on Energy Material and Energy Technology (EMET 2024) was successfully held in Haikou, China during November 18-20, 2024.The conference focused on cutting-edge research and achievements on materials and technologies used in all forms of energy harvesting, conversion and storage. The conference adopted a mixed mode of online and offline, with excellent academic presentations and exchanges, including keynote presentations, invited presentations, oral presentations, and poster presentations, with participants from universities, research institutes, and enterprises in different countries and regions.The EMET organizing committee extend their sincerest gratitude to all who have supported the conference in their ways, to the authors who have chosen this platform to publish their works and communicate with peers, to the participants who took an interest and attended the conference in person and/or online, to the chairs and committee members who have been indispensable in lending their professional expertise and judgment, to the speakers who generously shared their vision and passion, and to the reviewers who held up the faith of being a scholar and contributed their experience and honest opinions. It has been a pleasure and honor working alongside them, and we look forward to further cooperation with them at future EMET conferences to come.List of EMET 2024 Committee is available in this pdf.