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Article ; Online: Data-driven models applying in household hazardous waste: Amount prediction and classification in Shanghai.

Lin, Kunsen / Zhao, Youcai / Kuo, Jia-Hong

2023 Volume 263, Page(s) 115249

Abstract: Precisely predicting the amount of household hazardous waste (HHW) and classifying it intelligently is crucial for effective city management. Although data-driven models have the potential to address these problems, there have been few studies utilizing ... ...

Abstract	Precisely predicting the amount of household hazardous waste (HHW) and classifying it intelligently is crucial for effective city management. Although data-driven models have the potential to address these problems, there have been few studies utilizing this approach for HHW prediction and classification due to the scarcity of available data. To address this, the current study employed the prophet model to forecast HHW quantities based on the Integration of Two Networks systems in Shanghai. HHW classification was performed using HVGGNet structures, which were based on VGG and transfer learning. To expedite the process of finding the optimal global learning rate, the method of cyclical learning rate was adopted, thus avoiding the need for repeated testing. Results showed that the average rate of HHW generation was 0.1 g/person/day, with the most significant waste categories being fluorescent lamps (30.6 %), paint barrels (26.1 %), medicine (26.2 %), battery (15.8 %), thermometer (0.03 %), and others (1.22 %). Recovering rare earth element (18.85 kg), Cd (3064.10 kg), Hg (15643.43 kg), Zn (14239.07 kg), Ag (11805.81 kg), Ni (4956.64 kg) and Li (1081.45 kg) from HHW can help avoid groundwater pollution, soil contamination and air pollution. HVGGNet-11 demonstrated 90.5 % precision and was deemed most suitable for HHW sorting. Furthermore, the prophet model predicted that HHW in Shanghai would increase from 794.43 t in 2020 to 2049.67 t in 2025.
MeSH term(s)	Humans ; Refuse Disposal/methods ; Hazardous Waste/analysis ; Household Products ; China ; Environmental Pollution/analysis ; Waste Management/methods
Chemical Substances	Hazardous Waste
Language	English
Publishing date	2023-07-11
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	436536-7
ISSN	1090-2414 ; 0147-6513
ISSN (online)	1090-2414
ISSN	0147-6513
DOI	10.1016/j.ecoenv.2023.115249
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Article ; Online: Hi-MGT: A hybrid molecule graph transformer for toxicity identification.

Tan, Zhichao / Zhao, Youcai / Zhou, Tao / Lin, Kunsen

Journal of hazardous materials

2023 Volume 457, Page(s) 131808

Abstract: Conventional toxicity testing methods that rely on animal experimentation are resource-intensive, time-consuming, and ethically controversial. Therefore, the development of alternative non-animal testing approaches is crucial. This study proposes a novel ...

Abstract	Conventional toxicity testing methods that rely on animal experimentation are resource-intensive, time-consuming, and ethically controversial. Therefore, the development of alternative non-animal testing approaches is crucial. This study proposes a novel hybrid graph transformer architecture, termed Hi-MGT, for the toxicity identification. An innovative aggregation strategy, referred to as GNN-GT combination, enables Hi-MGT to simultaneously and comprehensively aggregate local and global structural information of molecules, thus elucidating more informative toxicity information hidden in molecule graphs. The results show that the state-of-the-art model outperforms current baseline CML and DL models on a diverse range of toxicity endpoints and is even comparable to large-scale pretrained GNNs with geometry enhancement. Additionally, the impact of hyperparameters on model performance is investigated, and a systematic ablation study is conducted to demonstrate the effectiveness of the GNN-GT combination. Moreover, this study provides valuable insights into the learning process on molecules and proposes a novel similarity-based method for toxic site detection, which could potentially facilitate toxicity identification and analysis. Overall, the Hi-MGT model represents a significant advancement in the development of alternative non-animal testing approaches for toxicity identification, with promising implications for enhancing human safety in the use of chemical compounds.
MeSH term(s)	Humans ; Electric Power Supplies ; Learning
Language	English
Publishing date	2023-06-08
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1491302-1
ISSN	1873-3336 ; 0304-3894
ISSN (online)	1873-3336
ISSN	0304-3894
DOI	10.1016/j.jhazmat.2023.131808
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Article ; Online: Hi-MGT: A hybrid molecule graph transformer for toxicity identification

Tan, Zhichao / Zhao, Youcai / Zhou, Tao / Lin, Kunsen

Journal of Hazardous Materials. 2023 Sept., v. 457, p. 131808

2023 , Page(s) 131808

Abstract	Conventional toxicity testing methods that rely on animal experimentation are resource-intensive, time-consuming, and ethically controversial. Therefore, the development of alternative non-animal testing approaches is crucial. This study proposes a novel hybrid graph transformer architecture, termed Hi-MGT, for the toxicity identification. An innovative aggregation strategy, referred to as GNN-GT combination, enables Hi-MGT to simultaneously and comprehensively aggregate local and global structural information of molecules, thus elucidating more informative toxicity information hidden in molecule graphs. The results show that the state-of-the-art model outperforms current baseline CML and DL models on a diverse range of toxicity endpoints and is even comparable to large-scale pretrained GNNs with geometry enhancement. Additionally, the impact of hyperparameters on model performance is investigated, and a systematic ablation study is conducted to demonstrate the effectiveness of the GNN-GT combination. Moreover, this study provides valuable insights into the learning process on molecules and proposes a novel similarity-based method for toxic site detection, which could potentially facilitate toxicity identification and analysis. Overall, the Hi-MGT model represents a significant advancement in the development of alternative non-animal testing approaches for toxicity identification, with promising implications for enhancing human safety in the use of chemical compounds.
Keywords	animal experimentation ; geometry ; humans ; model validation ; models ; toxicity ; Toxicity identification ; Machine learning ; Graph transformer ; Toxic site detection
Language	English
Dates of publication	2023-09
Size	p. 131808
Publishing place	Elsevier B.V.
Document type	Article ; Online
ZDB-ID	1491302-1
ISSN	1873-3336 ; 0304-3894
ISSN (online)	1873-3336
ISSN	0304-3894
DOI	10.1016/j.jhazmat.2023.131808
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Article ; Online: Machine learning models for predicting thermal desorption remediation of soils contaminated with polycyclic aromatic hydrocarbons.

Chen, Haojia / Cao, Yudong / Qin, Wei / Lin, Kunsen / Yang, Yan / Liu, Changqing / Ji, Hongbing

The Science of the total environment

2024 Volume 927, Page(s) 172173

Abstract: Among various remediation methods for organic-contaminated soil, thermal desorption stands out due to its broad treatment range and high efficiency. Nonetheless, analyzing the contribution of factors in complex soil remediation systems and deducing the ... ...

Abstract	Among various remediation methods for organic-contaminated soil, thermal desorption stands out due to its broad treatment range and high efficiency. Nonetheless, analyzing the contribution of factors in complex soil remediation systems and deducing the results under multiple conditions are challenging, given the complexities arising from diverse soil properties, heating conditions, and contaminant types. Machine learning (ML) methods serve as a powerful analytical tool that can extract meaningful insights from datasets and reveal hidden relationships. Due to insufficient research on soil thermal desorption for remediation of organic sites using ML methods, this study took organic pollutants represented by polycyclic aromatic hydrocarbons (PAHs) as the research object and sorted out a comprehensive data set containing >700 data points on the thermal desorption of soil contaminated with PAHs from published literature. Several ML models, including artificial neural network (ANN), random forest (RF), and support vector regression (SVR), were applied. Model optimization and regression fitting centered on soil remediation efficiency, with feature importance analysis conducted on soil and contaminant properties and heating conditions. This approach enabled the quantitative evaluation and prediction of thermal desorption remediation effects on soil contaminated with PAHs. Results indicated that ML models, particularly the RF model (R
Language	English
Publishing date	2024-04-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2024.172173
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Article ; Online: Toward a comprehensive understanding of alicyclic compounds: Bio-effects perspective and deep learning approach.

Shi, Wenjie / Lin, Kunsen / Zhao, Youcai / Li, Zongsheng / Zhou, Tao

The Science of the total environment

2023 Volume 912, Page(s) 168927

Abstract: The escalating use of alicyclic compounds in modern industrial production has led to a rapid increase of these substances in the environment, posing significant health hazards. Addressing this challenge necessitates a comprehensive understanding of these ...

Abstract	The escalating use of alicyclic compounds in modern industrial production has led to a rapid increase of these substances in the environment, posing significant health hazards. Addressing this challenge necessitates a comprehensive understanding of these compounds, which can be achieved through the deep learning approach. Graph neural networks (GNN) known for its' extraordinary ability to process graph data with rich relationships, have been employed in various molecular prediction tasks. In this study, alicyclic molecules screened from PCBA, Toxcast and Tox21 are made as general bioactivity and biological targets' activity prediction datasets. GNN-based models are trained on the two datasets, while the Attentive FP and PAGTN achieve best performance individually. In addition, alicyclic carbon atoms make the greatest contribution to biological activity, which indicate that the alicycle structures have significant impact on the carbon atoms' contribution. Moreover, there are terrific number of active molecules in other public datasets, indicates that alicyclic compounds deserve more attention in POPs control. This study uncovered deeper structural-activity relationships within these compounds, offering new perspectives and methodologies for academic research in the field.
MeSH term(s)	Deep Learning ; Carbon ; Industry ; Neural Networks, Computer ; Organic Chemicals
Chemical Substances	Carbon (7440-44-0) ; Organic Chemicals
Language	English
Publishing date	2023-11-30
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2023.168927
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Article ; Online: Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai.

Lin, Kunsen / Zhao, Youcai / Kuo, Jia-Hong

Chemosphere

2022 Volume 307, Issue Pt 4, Page(s) 136119

Abstract: It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools ... ...

Abstract	It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools with potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that attention is crucial for decoding the encoding information, which would improve performance between predicted and known MSW amount (R
MeSH term(s)	China ; Cities ; Deep Learning ; Humans ; Refuse Disposal/methods ; Solid Waste/analysis ; Waste Management/methods
Chemical Substances	Solid Waste
Language	English
Publishing date	2022-08-20
Publishing country	England
Document type	Journal Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2022.136119
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Article: Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai

Lin, Kunsen / Zhao, Youcai / Kuo, Jia-Hong

Chemosphere. 2022 Nov., v. 307

2022

Abstract	It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools with potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that attention is crucial for decoding the encoding information, which would improve performance between predicted and known MSW amount (R² in A-L-C, L-A-C, L-C-A was 89.45%, 90.77%, and 95.31%, respectively.). CNN modules appear to be positioned similarly across the MSW predicted system. Finally, R² in L-A-C, A-L-C, and A-C-L was 85.44%, 91.61%, and 89.45%, which suggested that LSTM as an intermediary between CNN and Attention modules seems a wise measure to predict the MSW amount based on the correlation efficiency. In addition, some socioeconomic factors including the average number of people in households and budget revenue may be chosen for the decision-making of MSW management in Shanghai city in the future, according to the weight of neurons in fully connected layers by the visual technology.
Keywords	case studies ; decision making ; income ; municipal solid waste ; neural networks ; socioeconomics ; China
Language	English
Dates of publication	2022-11
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2022.136119
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

Lin, Kunsen / Zhao, Youcai / Wang, Lina / Shi, Wenjie / Cui, Feifei / Zhou, Tao

Front. Environ. Sci. Eng.. 2023 June, v. 17, no. 6 p.77-77

2023

Abstract: An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material ...

Abstract	An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.
Keywords	artificial intelligence ; chemical reactions ; materials ; models ; municipal solid waste ; screening ; virus transmission
Language	English
Dates of publication	2023-06
Size	p. 77.
Publishing place	Higher Education Press
Document type	Article ; Online
ZDB-ID	2662203-8
ISSN	2095-221X ; 2095-2201
ISSN (online)	2095-221X
ISSN	2095-2201
DOI	10.1007/s11783-023-1677-1
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Transformer-based enhanced model for accurate prediction and comprehensive analysis of hazardous waste generation in Shanghai: Implications for sustainable waste management strategies

Shi, Wenjie / Zhao, Youcai / Li, Zongsheng / Zhang, Wenxiao / Zhou, Tao / Lin, Kunsen

Chemosphere. 2023 Oct., v. 338 p.139579-

2023

Abstract: The escalating generation of hazardous waste (HW) has become a pressing concern worldwide, straining waste management systems and posing significant health hazards. Addressing this challenge necessitates an accurate understanding of HW generation, which ... ...

Abstract	The escalating generation of hazardous waste (HW) has become a pressing concern worldwide, straining waste management systems and posing significant health hazards. Addressing this challenge necessitates an accurate understanding of HW generation, which can be achieved through the application of advanced models. The Transformer model, known for its ability to capture complex nonlinear processes, proves invaluable in extracting essential features and making precise HW generation predictions. To enhance comprehension of the key factors influencing HW generation, visualization techniques such as SHapley Additive exPlanations (SHAP) provide insightful explanations. In this study, a novel approach combining classical deep learning algorithms with the Transformer model is proposed, yielding impressive results with an R² value of 0.953 and an RMSE of 7.284 for HW prediction. Notably, among the five key fields considered—demographics, socio-economics, industrial production, environmental governance, and medical health—industrial production emerges as the primary contributor, accounting for over 50% of HW generation. Moreover, a high rate of industrial development is anticipated to further accelerate this process.
Keywords	environmental governance ; hazardous waste ; industrialization ; models ; prediction ; socioeconomics ; waste management ; China ; Deep learning ; Transformer
Language	English
Dates of publication	2023-10
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Pre-press version
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2023.139579
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Agglomeration-influenced transformation of heavy metals in gas-solid phases during simulated sewage sludge co-incineration: Effects of phosphorus and operating temperature.

Lin, Kunsen / Zhao, Youcai / Kuo, Jia-Hong / Lin, Chiou-Liang

The Science of the total environment

2022 Volume 858, Issue Pt 1, Page(s) 159759

Abstract: Phosphorus and operating temperature not only affect the agglomeration behavior but also the transformation and migration of heavy metals. Accordingly, this study examined the effect of temperature and phosphorus in a fluidized bed combustion process to ... ...

Abstract	Phosphorus and operating temperature not only affect the agglomeration behavior but also the transformation and migration of heavy metals. Accordingly, this study examined the effect of temperature and phosphorus in a fluidized bed combustion process to understand the emission and distribution of heavy metals by both experimental and thermodynamic calculations. The experimental results indicated that the sodium-phosphate reactions occur before the sodium-silicate reaction in the solid phase when the ratio of P/Na was 1/2. A low-melting-point sodium phosphate component, such as NaPO
MeSH term(s)	Incineration ; Sewage ; Temperature ; Phosphorus ; Cadmium ; Lead ; Silicon Dioxide ; Metals, Heavy ; Sodium
Chemical Substances	Sewage ; Phosphorus (27YLU75U4W) ; Cadmium (00BH33GNGH) ; Lead (2P299V784P) ; Silicon Dioxide (7631-86-9) ; Metals, Heavy ; Sodium (9NEZ333N27)
Language	English
Publishing date	2022-10-27
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2022.159759
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