Article
Engineering, Environmental
Dooguen Song, Kwangho Lee, Chuntak Phark, Seungho Jung
Summary: This study introduces an encoding-prediction neural network to predict gas leak dispersion by learning the relationship between gas dispersion, velocity field, and facility-layouts. By observing similar data at different time points, the network achieves satisfactory predictive results for gas dispersion.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Computer Science, Information Systems
Chenhuzhe Shao, Yue Liu, Zhedian Zhang, Fulin Lei, Jinglun Fu, Josep L. Rossello
Summary: This article proposes a method for designing gas turbine combustion chambers that combines artificial neural networks and computational fluid dynamics, which can greatly improve design speed and efficiency, and have the potential to achieve digital twinning.
Article
Environmental Sciences
Xiao-ting Ren, Xiao-ling Ma, Jiang-zheng Liu, Rui Liu, Chen-qian Zhao, Hao Wu, Zhao Wang, Chun-xu Hai, Xiao-di Zhang
Summary: This study investigates the impact of terrain factors on chlorine gas diffusion processes using the SLAB model. The study simulates the changing wind speed with altitude by using actual terrain data and integrates the influence of terrain on wind speed using various algorithms. The results show noticeable differences in the endpoint distance and area of chlorine gas dispersion between real terrain conditions and ideal conditions, indicating the importance of considering terrain factors in modeling gas diffusion.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Engineering, Multidisciplinary
Ibham Veza, Asif Afzal, M. A. Mujtaba, Anh Tuan Hoang, Dhinesh Balasubramanian, Manigandan Sekar, I. M. R. Fattah, M. E. M. Soudagar, Ahmed EL-Seesy, D. W. Djamari, A. L. Hananto, N. R. Putra, Noreffendy Tamaldin
Summary: Artificial Neural Network (ANN) is considered as a beneficial prediction tool in automotive applications, especially when the system is complicated and costly to model using simulation programs. However, further examination and improvement are required for the use of ANN in engine applications.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Thermodynamics
Ali Sohani, Siamak Hoseinzadeh, Saman Samiezadeh, Ivan Verhaert
Summary: An enhanced design for a solar still desalination system was employed to develop artificial neural network (ANN) models, with FF and RBF types identified as the best structures for predicting distillate production and water temperature. Error analysis on data not used for ANN model development showed varying errors in different months.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2022)
Article
Multidisciplinary Sciences
Tiankuang Zhou, Wei Wu, Jinzhi Zhang, Shaoliang Yu, Lu Fang
Summary: We propose a spatiotemporal photonic computing architecture to achieve dynamic processing, matching highly parallel spatial computing with high-speed temporal computing. A unified training framework is devised to optimize the physical system and the network model. The proposed architecture paves the way for ultrafast advanced machine vision and will find applications in unmanned systems, autonomous driving, ultrafast science, etc.
Article
Materials Science, Multidisciplinary
Junaid Siddiqui, M. Jamal Deen
Summary: In this paper, a solution-based fabrication process is presented for a biodegradable electrochemical free chlorine sensor using asparagine that is functionalized onto graphene oxide (GO). The collected data is used to train an artificial neural network to characterize the factors affecting the sensor's performance and model its degradation.
ADVANCED MATERIALS TECHNOLOGIES
(2023)
Article
Multidisciplinary Sciences
Xiangyu Zheng, Rong Jia, Linling Gong, Aisikaer, Xiping Ma, Jian Dang, Zhihan Lv
Summary: This paper discusses the weaknesses of traditional relay protection technology and proposes the concept of relay protection based on artificial intelligence. Through simulation experiments, it is validated that the system has good performance and high reliability.
Article
Energy & Fuels
Yang Tian, Xianglei Liu, Li Zhang, Qinyang Luo, Qiao Xu, Haichen Yao, Fengyi Yang, Jianguo Wang, Chunzhuo Dang, Yiming Xuan
Summary: A prediction model based on BP artificial neural network and bio-inspired algorithm was proposed to accurately predict the thermophysical properties of eutectic salts. The BP-PBO algorithm demonstrated the lowest average prediction error for melting temperature and phase change enthalpy of chlorine eutectic salts, with significant improvements compared to BP algorithm and BP-GA algorithm. The study provides accurate and fast prediction methods for the thermophysical properties of chlorine eutectic salts.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Chemistry, Multidisciplinary
Mustapha Mukhtar, Ariyo Oluwasanmi, Nasser Yimen, Zhang Qinxiu, Chiagoziem C. Ukwuoma, Benjamin Ezurike, Olusola Bamisile
Summary: This study develops two novel hybrid neural network models for accurate prediction of global solar radiation. Compared with traditional artificial neural network models, the hybrid models show better performance in different countries across Africa. The results of this study are of great significance for finding more accurate methods of solar radiation estimation.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Environmental
Di Chen, Chengqing Wu, Jun Li, Kexi Liao
Summary: A numerical model of a progressive vented gas explosion is presented, which utilizes a CFD tool, correlation analysis, and an artificial neural network (ANN). The model is validated using experimental results and shows differences between fixed and progressive vented gas explosions. The study also demonstrates the significant impact of obstacles inside the tunnel on the flow field.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2022)
Article
Engineering, Chemical
Joao Pedro Souza de Oliveira, Joao Victor Barbosa Alves, Joao Neuenschwander Escosteguy Carneiro, Ricardo de Andrade Medronho, Luiz Fernando Lopes Rodrigues Silva
Summary: The present work demonstrates the application of Computational Fluid Dynamics (CFD) and Artificial Neural Network (ANN) algorithm in atmospheric dispersion problems. The combination of CFD and ANN allows for digital twin design and optimization. The local approach reduces the computational cost by treating the network as a transition rule. Several case studies were conducted to validate the effectiveness of the method, including methane cloud displacement, plume formation, and wind influence.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2022)
Article
Chemistry, Analytical
Jingya Dong, Bin Song, Fei He, Yingying Xu, Qiang Wang, Wanjun Li, Peng Zhang
Summary: In this paper, a Comprehensive Diagram Method (CDM) based on a Multi-Layer Perceptron Neuron Network (MLPNN) is proposed for natural gas energy metering using temperature, pressure, and the speed of sound from an ultrasonic flowmeter. The MLPNN model is trained and tested using real data points of compression factors (Z-factors) and calorific values of natural gas in Sichuan province, China. Experimental results show that the selected MLP 3-5-1 network for Z-factor metering and the new CDM method for calorific value metering have high accuracy and adaptability.
Article
Construction & Building Technology
Zhi-jiao Zhang, Zhong-xian Liu, Hai Zhang, Si-bo Meng, Ji-hao Shi, Jia-wei Zhao, Cheng-qing Wu
Summary: This study systematically investigates critical parameters that affect gas explosion loads in the utility tunnel and establishes an explosion load model using the artificial neural network. The results show that the methane volume and cross-sectional size of the utility tunnel have the most significant impact on natural gas explosion. The ANN-predicted model is efficient and quick to estimate the gas explosion loads for structural dynamics analysis.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2023)
Article
Energy & Fuels
Satuk Bugra Akdas, Abdullah Fisne
Summary: This study presents a new data-driven approach to analyze the total desorbed gas content of coal seams using machine learning models. The models were trained using core samples and coal properties data, and three different algorithms were employed for prediction. Sensitivity analysis was conducted to investigate the influence of coal properties on gas content. The findings provide insights into unconventional reservoir analysis and show the potential of machine learning in the field of geology.
Article
Engineering, Environmental
Shun-Shun Chen, Xu-Xiu Chen, Tian-Yu Yang, Li Chen, Zheng Guo, Xing-Jiu Huang
Summary: A temperature-modulated sensing strategy was proposed to identify and determine BTEX compounds. Highly effective identification of BTEX was achieved using linear discrimination and convolutional neural network analyses. Additionally, quantitative analysis of concentration was accomplished by establishing the relationship between concentration and response.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Myungkyung Noh, Jeong Yeon Sim, Jisung Kim, Jee Hwan Ahn, Hye-Young Min, Jong-Uk Lee, Jong-Sook Park, Ji Yun Jeong, Jae Young Lee, Shin Yup Lee, Hyo-Jong Lee, Choon-Sik Park, Ho-Young Lee
Summary: This study reveals that chronic exposure to PM induces chronic inflammation and development of COPD by dysregulating NAD+ metabolism and subsequent SIRT1 deficiency in pulmonary macrophages. Activation of SIRT1 by resveratrol effectively mitigates PM-induced inflammation and COPD development. Targeting metabolic and epigenetic reprogramming in macrophages induced by PM is a promising strategy for COPD treatment.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Yu Liu, Linlin Qin, Yiming Qin, Tong Yang, Haoran Lu, Yulong Liu, Qiqi Zhang, Wenyan Liang
Summary: Co/NC/PAC electrode was prepared by compounding ZIF-67 with powder-activated carbon for the electrocatalytic treatment of nitrogen-containing heterocyclic compounds. The degradation efficiency of the four compounds reached 90.2-93.7% under optimal conditions, and the degradation order was pyridazine < pyrimidine < pyrazine < pyridine.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Julien Couturier, Pierre Tamba Oulare, Blanche Collin, Claire Lallemand, Isabelle Kieffer, Julien Longerey, Perrine Chaurand, Jerome Rose, Daniel Borschneck, Bernard Angeletti, Steven Criquet, Renaud Podor, Hamed Pourkhorsandi, Guilhem Arrachart, Clement Levard
Summary: This study analyzes the properties of bauxite residue samples and explores the influence of bauxite ore origin, storage conditions, and storage time. The results show that the speciation of yttrium is related to the origin of bauxite ore, while no significant variation was observed with storage conditions or aging of the residues.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Sakthinarenderan Saikumar, Ravi Mani, Mirunalini Ganesan, Inbakandan Dhinakarasamy, Thavamani Palanisami, Dharani Gopal
Summary: Microplastic contamination in marine ecosystems poses a growing concern due to its trophic transfer and negative effects on marine organisms. This study investigates the transfer and impacts of polystyrene microplastics in an estuarine food chain. The results show that microplastics can be transferred through the food chain, although the transfer rates are low. The exposed organisms exhibit stress responses, suggesting the potential risk of microplastics reaching humans through the food chain.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Review
Engineering, Environmental
Yan-Jiao Li, Ying Yuan, Wen-Bing Tan, Bei-Dou Xi, Hui Wang, Kun-Long Hui, Jia-Bao Chen, Yi-Fan Zhang, Lian-Feng Wang, Ren-Fei Li
Summary: This review investigated and analyzed the distribution, composition, and abundance of heavy metals and antibiotic resistance genes (ARGs) in landfill. The results showed that heavy metals have lasting effects on ARGs, and complexes of heavy metals and organic matter are common in landfill. This study provides a new basis to better understand the horizontal gene transfer (HGT) of ARGs in landfill.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Jessy Joseph, Ari Vaisanen, Ajay B. Patil, Manu Lahtinen
Summary: Efficient and environmentally friendly porous hybrid adsorbent beads have been developed for the removal of arsenic from drinking water. The structural tuning of the adsorbents has been shown to have a significant impact on their adsorption performance, with high crystallinity leading to increased adsorption capacity and selectivity towards As5+.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Yangyang Liu, Minhua Xiao, Kaiqin Huang, Juntao Cui, Hongli Liu, Yingxin Yu, Shengtao Ma, Xihong Liu, Meiqing Lin
Summary: This study measured the levels of phthalate metabolites in breast milk collected from mothers in southern China. The results showed that phthalates are still prevalent in the region, and breastfeeding contributes to phthalate intake in infants. However, the levels detected do not pose significant health risks to infants based on dietary exposure. The increasing exposure to certain phthalates calls for further research into their sources and potential risks.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Zhiqiang Wu, Jianxing Sun, Liting Xu, Hongbo Zhou, Haina Cheng, Zhu Chen, Yuguang Wang, Jichao Yang
Summary: Ocean depth affects microbial diversity, composition, and co-occurrence patterns of microplastic microbial communities. Deterministic processes dominate the assembly of mesopelagic plastisphere microbial communities, while stochastic processes shape the assembly of bathypelagic microbial communities. The relationships between microorganisms in the mesopelagic layer are more complex and stable, with Proteobacteria and Actinobacteriota playing important roles.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Tingting Xiao, Renjie Chen, Chen Cai, Shijie Yuan, Xiaohu Dai, Bin Dong, Zuxin Xu
Summary: Based on the efficiency of catalytic ozonation techniques in enhancing sludge dewaterability, this study investigated its effectiveness in simultaneous reduction of antibiotics and antibiotic resistance genes. The results showed that catalytic ozonation conditioning changed the distribution of antibiotics and achieved high degradation rates. It also significantly reduced the abundance of ARGs, inhibited horizontal gene transfer, and decreased the signal transduction of typical ARGs host bacteria.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Yang Deng, Xiaohong Guan
Summary: This article discusses two different development approaches for ferrate(VI) technology in water treatment, arguing that process integration is a promising method that can drive technological innovation and revolution in water treatment, achieving higher treatment efficiency, reduced costs and energy consumption, and a smaller physical footprint.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Zhe Zhang, Lu Zhang, Zhihao Huang, Yuxin Xu, Qingqing Zhao, Hongju Wang, Meiqing Shi, Xiangnan Li, Kai Jiang, Dapeng Wu
Summary: In this study, a floating catalytic foam was designed and prepared to enhance the mass transfer in immobilized photocatalysts for wastewater treatment. The floating catalytic foam could float on the water surface and establish a temperature gradient, effectively promoting the diffusion and adsorption of target molecules during the photocatalytic process.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Muhammad Nafees, Adiba Khan Sehrish, Sarah Owdah Alomrani, Linlin Qiu, Aasim Saeed, Shoaib Ahmad, Shafaqat Ali, Hongyan Guo
Summary: The accumulation of cadmium and antibiotics in edible plants and fertile soil is a worldwide problem. This study investigated the potential of zinc oxide nanoparticles to alleviate the toxicity of both cadmium and antibiotics and promote spinach growth.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Lurui Wan, Kai Wang, Yuan Chen, Zhiyong Xu, Wenbo Zhao
Summary: In this study, a low viscosity and high thermal stability SO2 absorbent with dual interacting sites was successfully synthesized. The absorbent showed the highest absorption enthalpy change and entropy change values among reported SO2 absorbents, and exhibited lower viscosity and comparable thermal stability to ILs.
JOURNAL OF HAZARDOUS MATERIALS
(2024)
Article
Engineering, Environmental
Zhengwei Zhou, Guojie Ye, Yang Zong, Zhenyu Zhao, Deli Wu
Summary: This study utilized Mo powder and STPP to enhance the performance of the sodium percarbonate system in pollutant degradation. The presence of Mo and STPP resulted in a higher degradation rate of the model pollutant SMX, with low oxidant consumption. The system generated multiple active species through a series of chain reactions at different pH values, exhibiting excellent performance towards electron-rich pollutants. Furthermore, Mo demonstrated excellent stability and reusability.
JOURNAL OF HAZARDOUS MATERIALS
(2024)