China actively promotes CO2 capture, utilization and storage research to achieve carbon peak and carbon neutrality

Global climate change is a common challenge facing mankind, which has evolved from a scientifific issue into a global economic and political issue of universal concern to the international community. Temperature increase, sea level rise, extreme weather and climate events caused by the climate change are becoming more and more prominent. The scientifific understanding of climate change in the international community has been deepening. The Intergovernmental Panel on Climate Change (IPCC, 2014) further strengthened the scientific conclusion that human-induced climate change is more than 95% likely to be attributed to emissions of greenhouse gases from human activities.The United Nations Climate Change Summit (held in September 2014) pointed out that climate change threatens the hard-won peace, prosperity and opportunities of all mankind, and that no one and no country is immune to its impact. Controlling global warming within 2℃ is an urgent and severe challenge faced by mankind in dealing with climate change. The awareness of all countries on the issue of climate change is gradually increasing. The 26th Conference of the Parties (held in November 2021 in Glasgow, UK) urged all countries to achieve the net zero carbon emissions by around 2050, and step up efforts to reduce carbon emission before 2030. Therefore, taking active measures to cope with climate change becomes the common aspiration and urgent need of all countries.Mitigating greenhouse gas emissions (represented by CO2) has become the consensus of the world. In September 2020, Chinese President Xi Jinping pledged at the General Debate of the 75th Session of The United Nations General Assembly that China aims to peak its CO2 emissions before 2030 and achieve carbon neutrality before 2060 (i.e., dual carbon goals), which demonstrates the responsibility of a major country.CO2 Capture, Utilization, and Storage (CCUS) is considered as an effective technology directly achieving carbon emissions mitigation, and has attracted widespread attention of the international community (Metz et al., 2005). The implementation of CCUS projects began in the 1970s, and was mainly carried out in the United States, Canada and some European countries. Those projects mainly focused on CO2 enhanced oil recovery, whereas projects with the pure purpose of CO2 sequestration are relatively rare due to their poor economy.CCUS projects in China started relatively late, and most of them were gradually implemented after 2000 (Guo et al., 2014). The initial technical routes of these projects were similar to those of projects carried out in European and American countries, which began with the geological sequestration of CO2 and enhanced oil recovery. In the past decade, CCUS projects in China began to develop in a diversified way, and there emerged a variety of carbon dioxide capture, storage and utilization technologies, including pre-combustion capture of power plants, CO2 chemical and biological utilization, etc.The realization of the dual carbon goals not only requires revolutionary changes in industrial technology, but also largely depends on the formulation of relevant policies and capital investment. The National Natural Science Foundation of China launched a special research program “Major Basic Science Issues and Countermeasures for National Carbon neutrality” in 2021 to meet the needs of basic science research for the national carbon neutrality strategy. Focusing on the two core issues of “carbon emission mitigation” and “carbon sink increase”, the special program includes a total of 28 research projects, with an average funding of about 3 million RMB per project.This special research program aims to reveal the oceans and terrestrial carbon sinks, the process mechanism, evolution trend and its mutual feedback mechanism with the climate system, delineate the geological process of carbon sequestration and the effectivity of fixing carbon. The program also has goals to increase the potential of CO2 storage, to assess the technology risk and management mode, to analyze the economic transformation, the optimal pathway, climate control, international cooperation management and policy issues. Interdisciplinary integration research is needed to condense key basic science issues and solutions for serving the national carbon-neutral strategy.It is foreseeable that China will further increase investment in realizing a carbon emission peak and its carbon-neutral strategy in the future. This is also a great opportunity for the development of CCUS-related technologies. The contribution of CCUS technology in carbon emission mitigation is generally low today. For instance, even in Norway, which has the highest proportion of carbon emissions treated by CCUS, the value is less than 5% (Cai et al., 2020). However, as the guaranteed technology of carbon peak strategy, the contribution ratio of CCUS in carbon emission mitigation is expected to significantly increase in the future.Although the CCUS technology has been implemented for many years and many projects have been carried out, there are still many challenges to be solved, such as:(i) CCUS related technology development and cost control The CCUS technology includes capture, transportation, utilization and storage, all of which need to consume a lot of energy. At present, the cost of the CCUS projects is still high. It is estimated that the cost of the whole CCUS process will be 150-540 RMB per ton of CO2 by 2025, of which CO2 capture cost accounts for more than two thirds of the total cost, about 100-480 RMB/ton. In comparison, the cost of CO2 sequestration is 50-60 RMB/ton, while the cost of CO2 transportation is very low, less than 1 RMB/ton (Cai et al., 2021). Obviously, the wider promotion of CCUS projects in the future largely depends on the further development of CO2 capture technology and the rapid reduction of cost.(ii) Effect of long-term CO2-water-rock interaction on rock structure and mechanical properties In the process of CO2 geological storage and utilization, the injected CO2 will inevitably change the pH of formation water, breaking the original water-rock balance and inducing a new water-rock reaction. Thus, the rock structure and mechanical properties of the caprock are likely to be changed over time, which affects the safety of the storage reservoirs. The current studies mostly focus on the effect of CO2-water-rock interaction on the leakage channels (porosity and permeability) of the caprock (Credoz et al., 2009; Liu et al., 2020). However, the study on the change of rock mechanical properties caused by chemical reactions requires further research attention. A few previous studies only simply correlated the evolution of rock mechanical properties with porosity, but without considering the influence of changes in mineral composition induced by CO2-water-rock interaction on the rock mechanical properties (Agarwal, 2019). Therefore, it is necessary to further deepen the relevant investigation and build a comprehensive rock mechanical parameter evolution model considering the changes of porosity, mineral composition and content, and other factors (Tian et al., 2019).(iii) CO2 leakage monitoring and risk assessment methods The leakage risk of CO2 after injection has been one of the main concerns, which directly affects the safety and feasibility of CCUS technology (Bachu, 2008). At this point, the construction of a CO2 leakage monitoring system is particularly important. However, the CO2 leakage process is usually characterized by sudden occurrence and weak surface response. Therefore, a single monitoring method is difficult to ensure the reliability of monitoring. In the future, it is necessary to combine various monitoring methods with their respective advantages.For a long-term (more than 100 years) CO2 leakage risk assessment, the most commonly used method at present is to employ the reactive transport modelling. However, due to the large time scale, parameter uncertainty and the difficulty of validation, the predicted results have high uncertainty. Some natural CO2 gas reservoirs have existed for more than thousands of years (Jonathan et al., 2018). Taking natural CO2 gas reservoirs as a natural analogue of CO2 geological sequestration can solve the problem that long-term simulated results are difficult to verify, thereby improving the reliability of long-term risk assessment (Xu et al., 2019). AcknowledgementThis work was performed in support of the National Natural Science Foundation of China (Grant Nos. 42141013 and 41772247). Conflict of interest The authors declare no competing interest.Open Access This article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Cited as: Xu, T., Tian, H., Zhu, H., Cai, J. China actively promotes CO2 capture, utilization and storage research to achieve carbon peak and carbon neutrality. Advances in Geo-Energy Research, 2022, 6(1): 1-3. https://doi.org/10.46690/ager.2022.01.0

A deep search for C2H in the oxygen-rich post-AGB star OH 231.8+4.2

In carbon-rich environment, C2H emission is seen to be associated with HC3N, but whether it can trace the HC3N molecule in oxygen-rich environment is unknown. Here, we have searched for rotational emission from ethynyl radical (C2H) in the oxygen-rich circumstellar envelope (CSE) of the post-asymptotic giant branch (post-AGB) star OH 231.8+4.2, a renowned galactic proto-planetary nebula (PPN) known to display cyanoacetylene (HC3N) emission. Our observations were conducted using the James Clerk Maxwell Telescope (JCMT), and the total on-source time is 11.9 h. Base on local thermodynamic equilibrium (LTE) excitation analysis, we have calculated the column density and the abundance relative to H2 for C2H. The calculated column density for C2H is less than 1.4 × 1013 cm-2, which corresponds to a fractional abundance, f (C2H), less than 4.5 × 10–9. This inference suggests that the typical transformation pathway from C2H and C2H2 to HC3N may not play a significant role in O-rich environments. It indicates the presence of alternative, unidentified pathways that contribute to the formation of HC3N in O-rich circumstellar envelopes

High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection

Precision experimental determination of photon correlation requires the massive amounts of data and extensive measurement time. We present a technique to monitor second-order photon correlation $g^{(2)}(0)$ of amplified quantum noise based on wideband balanced homodyne detection and deep-learning acceleration. The quantum noise is effectively amplified by an injection of weak chaotic laser and the $g^{(2)}(0)$ of the amplified quantum noise is measured with a real-time sample rate of 1.4 GHz. We also exploit a photon correlation convolutional neural network accelerating correlation data using a few quadrature fluctuations to perform a parallel processing of the $g^{(2)}(0)$ for various chaos injection intensities and effective bandwidths. The deep-learning method accelerates the $g^{(2)}(0)$ experimental acquisition with a high accuracy, estimating 6107 sets of photon correlation data with a mean square error of 0.002 in 22 seconds and achieving a three orders of magnitude acceleration in data acquisition time. This technique contributes to a high-speed and precision coherence evaluation of entropy source in secure communication and quantum imaging.Comment: 6 pages, 6 figure

Transsion TSUP's speech recognition system for ASRU 2023 MADASR Challenge

This paper presents a speech recognition system developed by the Transsion Speech Understanding Processing Team (TSUP) for the ASRU 2023 MADASR Challenge. The system focuses on adapting ASR models for low-resource Indian languages and covers all four tracks of the challenge. For tracks 1 and 2, the acoustic model utilized a squeezeformer encoder and bidirectional transformer decoder with joint CTC-Attention training loss. Additionally, an external KenLM language model was used during TLG beam search decoding. For tracks 3 and 4, pretrained IndicWhisper models were employed and finetuned on both the challenge dataset and publicly available datasets. The whisper beam search decoding was also modified to support an external KenLM language model, which enabled better utilization of the additional text provided by the challenge. The proposed method achieved word error rates (WER) of 24.17%, 24.43%, 15.97%, and 15.97% for Bengali language in the four tracks, and WER of 19.61%, 19.54%, 15.48%, and 15.48% for Bhojpuri language in the four tracks. These results demonstrate the effectiveness of the proposed method

Bearing fault diagnosis method based on Hilbert envelope spectrum and deep belief network

When fault occurs in bearing, the frequency spectrum of vibration signal would change and it contains a considerable amount of fault information which can reflect the actual work condition and the fault type of bearing. Recently, the statistical features of the frequency spectrum have been widely used in bearing fault diagnosis. However, there are lots of statistical features with different sensitivity to fault identification. Selecting the most sensible statistical features for improving classification accuracy is often determined with experience, which will make great subjective influence on the fault diagnosis results. Deep belief network (DBN) is a deep neural network which can automatically find a latent hierarchical feature representation from the high dimension input data. In this study, a bearing fault diagnosis method based on Hilbert envelope spectrum and DBN is proposed. Firstly, the vibration signals under different test conditions are resampled. Secondly, the whole Hilbert envelope spectrum of the resampled signal is used directly as eigenvector to characterize the fault type of bearing. Finally, a DBN classifier model is established to recognize the fault type of bearing. DBN classifier model can be used as both an automatic feature extractor and a classifier for bearing fault diagnosis. Therefore, the process of fault diagnosis can be greatly simplified. The results of two different experiments demonstrate that the proposed method outperforms the competing methods and it can obtain a more excellent diagnostic performance

A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs

Entity alignment (EA) aims to find entities in different knowledge graphs (KGs) that refer to the same object in the real world. Recent studies incorporate temporal information to augment the representations of KGs. The existing methods for EA between temporal KGs (TKGs) utilize a time-aware attention mechanism to incorporate relational and temporal information into entity embeddings. The approaches outperform the previous methods by using temporal information. However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations. Therefore, we propose a simple graph neural network (GNN) model combined with a temporal information matching mechanism, which achieves better performance with less time and fewer parameters. Furthermore, since alignment seeds are difficult to label in real-world applications, we also propose a method to generate unsupervised alignment seeds via the temporal information of TKG. Extensive experiments on public datasets indicate that our supervised method significantly outperforms the previous methods and the unsupervised one has competitive performance.Comment: Accepted by COLING 202

User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations

Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminished user satisfaction and trust in the systems. Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability. In this paper, we present a user-controllable recommender system that seamlessly integrates explainability and controllability within a unified framework. By providing both retrospective and prospective explanations through counterfactual reasoning, users can customize their control over the system by interacting with these explanations. Furthermore, we introduce and assess two attributes of controllability in recommendation systems: the complexity of controllability and the accuracy of controllability. Experimental evaluations on MovieLens and Yelp datasets substantiate the effectiveness of our proposed framework. Additionally, our experiments demonstrate that offering users control options can potentially enhance recommendation accuracy in the future. Source code and data are available at \url{https://github.com/chrisjtan/ucr}.Comment: Accepted for presentation at 26th European Conference on Artificial Intelligence (ECAI2023

The effects of solvent extraction on nanoporosity of marine-continental coal and mudstone

Coal and organic-rich mudstone develop massive nanopores, which control the storage of adsorbed and free gas, as well as fluid flows. Generation and retention of bitumen and hydrocarbons of oil window reservoirs add more uncertainty to the nanoporosity. Solvent extraction is a traditional way to regain unobstructed pore networks but may cause additional effects due to interactions with rocks, such as solvent adsorbing on clay surfaces or absorbing in kerogens. Selected marine-continental coal and mudstone in Eastern Ordos Basin were studied to investigate how pore structures are affected by these in-situ-sorptive compounds (namely residual bitumen and hydrocarbons) and altered by solvent extractions. Solvent extraction was performed to obtain bitumen-free subsamples. Organic petrology, bulk geochemical analyses and gas chromatography were used to characterize the samples and the extracts. Low-pressure argon and carbon dioxide adsorptions were utilized to characterize the nanopore structures of the samples before and after extraction. The samples, both coal and mudstone, are in oil windows, with vitrinite reflectance ranging from 0.807 to 1.135%. The coals are strongly affected by marine organic input, except for the sample C-4; the mudstones are sourced by either marine or terrestrial organic input, or their mixture. As for the coals affected by marine organic input, residual bitumen and hydrocarbons occupying or blocking pores <10 nm becomes weak with thermal maturation. Bitumen derived from terrestrial organic matter mainly affects small pores, since coal asphaltene molecules are much smaller than petroleum asphaltene molecules. The mudstone M-2 with high extract production showed an increase of nanopores after extraction, due to the exposure of the filled or blocked pores. However, most transitional mudstones saw decreases of the pores because pore shrinkage caused by solvents adsorbing on and swelling clay minerals (mainly kaolinite and illite/smectite mixed layers) counteracts the released pore spaces. Solvent extractions on the coals significantly increased the micropores <0.6 nm, since the heat of sorption of alkanes reaches the peak in the pores within 0.4–0.5 nm. By contrast, solvent extractions on the mudstones decreased the micropores ∼0.35 nm, which is perhaps caused by evaporative drying of solvent displacing residual water in clay

The effect of smartphone dependence on learning burnout among undergraduates: the mediating effect of academic adaptability and the moderating effect of self-efficacy

IntroductionSmartphone dependence is closely related to the physical and mental health development of undergraduates and their learning. The purpose of this study was to explore the relationship between smartphone dependence, academic adaptability, self-efficacy and learning burnout among undergraduates and its underlying mechanisms.MethodsThe study was conducted on 2,110 undergraduates using the Smartphone Dependence Scale, the Undergraduates Learning Adjustment Scale, the Learning Burnout Undergraduates Scale and the Self-Efficacy Scale to develop a mediation model and a moderation model.ResultsThe findings of this study revealed that (1) smartphone dependence significantly negatively predicted academic adaptability; (2) academic adaptability significantly negatively predicted learning burnout; (3) smartphone dependence significantly positively predicted learning burnout; (4) academic adaptability partially mediated the effect of smartphone dependence on learning burnout; (5) self-efficacy played a moderating role in the effect of academic adaptability on learning burnout.ConclusionThese findings can help researchers and educators better understand the underlying mechanisms between smartphone dependence and learning burnout in undergraduates