Browsing by Author "Guo, Jianbin"
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Item Open Access Effects of nanobubble water on digestate soaking hydrolysis of rice straw(Elsevier, 2024-05-24) Wang, Enzhen; Xing, Fan; Chen, Penghui; Zheng, Yonghui; Lyu, Tao; Li, Xin; Xiong, Wei; Li, Gang; Dong, Renjie; Guo, JianbinThis study investigated the performance of combined nanobubble water (NW) and digestate in the soaking hydrolysis process. Two types of NW (CO2NW and O2NW) with digestate were used to soak rice straw for 1, 2, 3, 5, and 7 days. During soaking process, the volatile fatty acids (VFA) concentration in the treatment with O2NW and digestate for 3 days (O2NW-3 d) reached 7179.5 mg-HAc/L. Moreover, the highest specific methane yield (SMY) obtained in this treatment could reach 336.7 NmL/gVS. Although the addition of NW did not significantly increase SMY from digestate soaking, NW could accelerate the rate of methane production and reduce digestion time of T80. The enrichment of Enterobacter in the soaking process was observed when using CO2NW and O2NW as soaking solutions which played important roles in VFA production. This study provides a new insight into environment-friendly enhanced crop straw pretreatment, combining NW and digestate soaking hydrolysis.Item Embargo Process mechanisms of nanobubble technology enhanced hydrolytic acidification of anaerobic digestion of lignocellulosic biomass(Elsevier, 2023-12-21) Zhu, Yali; Lyu, Tao; Li, Daoyu; Zhang, Zongqin; Guo, Jianbin; Li, Xin; Xiong, Wei; Dong, Renjie; Wang, SiqiThis study explored the efficiency of CO2-, N2-, and H2- nanobubble treatment in anaerobic digestion (AD) of rice straw, with a focus on the processes and metabolic pathways of hydrolytic acidification, and revealed the underlying mechanisms. Mechanistic investigations revealed that nanobubbles, particularly CO2 nanobubbles, significantly increased the degradation of amorphous cellulose, resulting in higher levels of soluble carbohydrates (6.27 % – 11.13 %), VFAs (4.39 % – 24.50 %), and a remarkable cumulative H2 yield (74 – 94 times) Microbial community analysis indicated that the CO2 nanobubble promoted the growth of acidifying bacterial communities, such as Mobilitalea, unclassified_f_Lachnospiraceae, and Bacteroides. This indicates that the introduction of CO2 nanobubbles improved the total abundance of predicted functional enzymes were increased by 14 %, resulting in the production of more easily degradable intermediates. Based on the analysis of total methane production and kinetic analysis, it can be concluded that nanobubble addition enhanced methane production levels of 4.22 %−7.79 % with lower lag time (λ) (0.88–1.06 day) compared to the control group (1.09 day). The results also elucidated changes in relative enzymatic activities involved in the bioconversion of cellulose and hemicellulose during the hydrolysis stage with nanobubble treatment. This work is more beneficial for understanding the promoting effect and mechanism of nanobubbles on AD, facilitating the more precise application of nanobubble technology in the field of renewable energy.Item Open Access A rest time-based prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena(MDPI, 2016-11-01) Qin, Taichun; Zeng, Shengkui; Guo, Jianbin; Skaf, ZakwanState of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this frameworkItem Open Access State of health estimation of Li-ion batteries with regeneration phenomena: a similar rest time-based prognostic framework(MDPI, 2016-12-24) Qin, Taichun; Zeng, Shengkui; Guo, Jianbin; Skaf, ZakwanState of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework