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Interactions of phenol and benzaldehyde in electrocatalytic upgrading process

Chinese Chemical Letters 2024年第2期35卷 464-468页

作者： Lan Liang Chuanbin Wang Xukai Lu Yunan Sun Beibei Yan Ning Li Guanyi Chen Li'an Hou School of Environmental Science and engineering Tianjin Key Lab of Biomass/Wastes UtilizationTianjin UniversityTianjin 300350China School of Mechanical engineering Tianjin University of CommerceTianjin 300134China Georgia Tech Shenzhen Institute Tianjin UniversityShenzhen 518071China High Tech Inst Beijing Beijing 100085China

biomass pyrolysis oil can be improved effectively by electrocatalytic hydrogenation(ECH).However,the unclear interactions among different components lead to low bio-oil upgrading efficiency in the conversion ***,benzaldehyde and phenol,as common compounds in bio-oil,were chosen as model *** interactions between the two components were explored in the ECH process by combining experiments and theoretical *** showed that phenol could accelerate the conversion of benzaldehyde in the *** selectivity of benzyl alcohol was increased from 60.9%of unadded phenol to 99.1%with 30 mmol/L phenol concentration at 5 *** inhibited the ECH of *** the presence of benzaldehyde,the conversion rate of phenol was below 10.0%with no cyclohexanone and cyclohexanol formation at 5 *** density functional theory(DFT)calculations revealed that the phenol could promote the adsorption of benzaldehyde and facilitate the targeted conversion of benzaldehyde on the active site by lowering the reaction energy *** research on the interaction between phenol and benzaldehyde in the ECH provides a theoretical basis for the application of ECH in practical bio-oil upgrading.

关键词： Interaction bio-oil Electrocatalytic hydrogenation Phenol Benzaldehyde

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可生物降解塑料取代传统塑料--对碳排放的影响

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engineering 2024年第1期32卷 152-162页

作者： Guanyi Chen Jianyuan Li Yunan Sun Zhi Wang Gary ALeeke Christian Moretti Zhanjun Cheng Yuan Wang Ning Li Lan Mu Jinyu Li Junyu Tao Beibei Yan Li'an Hou Interdisciplinary Innovation lab for Environment and Energy School of Mechanical Engineering&School of Biotechnology and Food ScienceTianjin University of CommerceTianjin 300134China School of Environmental Science and engineering Tianjin UniversityTianjin 300350China School of Ecology and Environment Tibet UniversityLhasa 850012China tianjin key laboratory of biomass Wastes UtilizationTianjin Engineering Research Center for Organic Wastes Safe Disposal and Energy UtilizationTianjin 300072China School of Chemical engineering University of BirminghamBirmingham B152TTUK Department of Environmental Systems Science ETH ZürichZürich CH-8092Switzerland

In recent years,a great deal of attention has been focused on the environmental impact of plastics,includ-ing the carbon emissions related to plastics,which has promoted the application of biodegradable *** worldwide have shown high interest in replacing traditional plastics with biodegradable ***,no systematic comparison has been conducted on the carbon emissions of biodegrad-able versus traditional plastic *** study evaluates the carbon emissions of traditional and biodegradable plastic products(BPPs)over four stages and briefly discusses environmental and economic *** scenarios-namely,the traditional method,chemical recycling,industrial composting,and anaerobic digestion-are considered for the disposal of waste BPPs(WBPPs).The analysis takes China as a case *** results show that the carbon emissions of 1000traditional plastic products(plastic bags,lunch boxes,cups,etc.)were52.09-150.36 carbon emissions equivalent of per kilogram(kg CO_(2)eq),with the stage of plastic production contributing 50.71%-50.77%.In comparison,1000 similar BPPs topped out at 21.06-56.86 kg CO_(2)eq,approximately 13.53%-62.19%lower than traditional plastic *** difference was mainly at the stages of plastic production and waste disposal,and the BPPs showed significant carbon reduction potential at the raw material acquisition *** disposal plays an important role in environmental impact,and composting and anaerobic digestion are considered to be preferable disposal methods for ***,the high cost of biodegradable plastics is a challenge for their widespread *** study has important reference significance for the sustainable development of the biodegradableplastics industry.

关键词： Carbon emissions biodegradable plastics Different disposal scenarios Daily life consumption Environmental and economic discussion

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State-of-the-art applications of machine learning in the life cycle of solid waste management

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Frontiers of Environmental Science & engineering 2023年第4期17卷 53-69页

作者： Rui Liang Chao Chen Akash Kumar Junyu Tao Yan Kang Dong Han Xianjia Jiang Pei Tang Beibei Yan Guanyi Chen School of Environmental Science and engineering Tianjin UniversityTianjin 300350China School of Mechanical engineering Tianjin University of CommerceTianjin 300134China tianjin key laboratory of biomass wastes utilization/tianjin engineering research center of bio gas/oil technology Tianjin 300072China School of Science Tibet UniversityLhasa 850012China

Due to the superiority of machine learning(ML)data processing,it is widely used in research of solid waste(SW).This study analyzed the research and developmental progress of the applications of ML in the life cycle of *** analyses were undertaken on the literature published between 1985 and 2021 in the Science Citation Index Expanded and Social Sciences Citation Index to provide an overview of the *** on the articles considered,a rapid upward trend from 1985 to 2021 was found and international cooperatives were found to have *** three topics of ML,namely,SW categories,ML algorithms,and specific applications,as applied to the life cycle of SW were *** has been applied during the entire SW process,thereby affecting its life *** was used to predict the generation and characteristics of SW,optimize its collection and transportation,and model the processing of its energy ***,the current challenges of applying ML to SW and future perspectives were *** goal is to achieve high economic and environmental benefits and carbon reduction during the life cycle of *** plays an important role in the modernization and intellectualization of SW *** is hoped that this work would be helpful to provide a constructive overview towards the state-of-the-art development of SW disposal.

关键词： Machine learning(ML) Solid waste(SW) Bibliometrics SW management Energy utilization Life cycle

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Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste

Interpretable machine learning assisted spectroscopy for fas...

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2023年有机固废处理与资源化利用大会

作者： Liang Rui Chen Chao Tao Junyu Yan Beibei Chen Guanyi School of Environmental Science and engineering Tianjin University School of Mechanical engineering Tianjin University of Commerce tianjin key lab of biomass wastes utilization/tianjin engineering research center of bio gas/oil technology School of Science Tibet University

Background,Aims and *** characterization of biomass and waste(BW) is important to its downstream treatment and *** order to overcome the long duration and complex procedure problems of traditional BW characterization methods,the spectroscopy and machine learning based BW fast characterization method has been developed recently,and presented satisfactory ***,this fast characterization process is lack of interpretability towards its chemical insights,leading to less satisfactory recognition for its reliability and difficulty in expanding the further ***,this study proposed a hypothesis to the inner working mechanism of the spectroscopy and machine learning based BW fast characterization method:Infrared spectra have close qualitative and quantitative correlation with the functional groups' species and *** functional group has its specific element composition and heating value ***,the element composition and heating value results are obtained by processing the functional group information behind the infrared *** paper aimed to verify the hypothesis.A novel dimensionality reduction method with physicochemical meanings was proposed to explain the chemical insights of the machine learning *** high loading spectral peaks of BW,which contain the important information of the functional groups of raw materials,were selected as the results of dimensionality ***,combined with functional groups attribution of these spectral peaks,the machine learning model could provide clearer logic explanation of the input spectra and output characteristics,furthermore,provide support to the above *** paper discussed the results of the dimensionality reduction,evaluated the characterization performance between the newly dimensionality reduction method and PCA,moreover,explored the theoretical fundamentals of the input spectra on the output *** is hoped to provide

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