It is particularly important to comprehensively assess the biotoxicity variation of industrial wastewater along the treatment process for ensuring the water environment security.However,intensive studies on the biotox...It is particularly important to comprehensively assess the biotoxicity variation of industrial wastewater along the treatment process for ensuring the water environment security.However,intensive studies on the biotoxicity reduction of industrial wastewater are still limited.In this study,the toxic organics removal and biotoxicity reduction of coal chemical wastewater(CCW)along a novel full-scale treatment process based on the pretreatment process-anaerobic process-biological enhanced(BE)process-anoxic/oxic(A/O)process-advanced treatment process was evaluated.This process performed great removal efficiency of COD,total phenol,NH_(4)^(+)-N and total nitrogen.And the biotoxicity variation along the treatment units was analyzed from the perspective of acute biotoxicity,genotixicity and oxidative damage.The results indicated that the effluent of pretreatment process presented relatively high acute biotoxicity to Tetrahymena thermophila.But the acute biotoxicity was significantly reduced in BE-A/O process.And the genotoxicity and oxidative damage to Tetrahymena thermophila were significantly decreased after advanced treatment.The polar organics in CCW were identified as the main biotoxicity contributors.Phenols were positively correlated with acute biotoxicity,while the nitrogenous heterocyclic compounds and polycyclic aromatic hydrocarbons were positively correlated with genotoxicity.Although the biotoxicity was effectively reduced in the novel full-scale treatment process,the effluent still performed potential biotoxicity,which need to be further explored in order to reduce environmental risk.展开更多
The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this...The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming loop.The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices.The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios.To achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process.The approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical examples.The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data.Overall,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally intensive.This approach also shows potential for broader applications in structural damage detection.展开更多
基金supported by the Natural Science Foundation of Shandong Province,China(No.ZR2021QE227)the Natural Science Foundation of Shandong Province,China(No.ZR2021QE272)+1 种基金the Open Project of State Key Laboratory of Urban Water Resource and Environment,Harbin Institute of Technology(No.ES202120)the Taishan Scholars Program of Shandong Province,China(No.tsqn201812091)。
文摘It is particularly important to comprehensively assess the biotoxicity variation of industrial wastewater along the treatment process for ensuring the water environment security.However,intensive studies on the biotoxicity reduction of industrial wastewater are still limited.In this study,the toxic organics removal and biotoxicity reduction of coal chemical wastewater(CCW)along a novel full-scale treatment process based on the pretreatment process-anaerobic process-biological enhanced(BE)process-anoxic/oxic(A/O)process-advanced treatment process was evaluated.This process performed great removal efficiency of COD,total phenol,NH_(4)^(+)-N and total nitrogen.And the biotoxicity variation along the treatment units was analyzed from the perspective of acute biotoxicity,genotixicity and oxidative damage.The results indicated that the effluent of pretreatment process presented relatively high acute biotoxicity to Tetrahymena thermophila.But the acute biotoxicity was significantly reduced in BE-A/O process.And the genotoxicity and oxidative damage to Tetrahymena thermophila were significantly decreased after advanced treatment.The polar organics in CCW were identified as the main biotoxicity contributors.Phenols were positively correlated with acute biotoxicity,while the nitrogenous heterocyclic compounds and polycyclic aromatic hydrocarbons were positively correlated with genotoxicity.Although the biotoxicity was effectively reduced in the novel full-scale treatment process,the effluent still performed potential biotoxicity,which need to be further explored in order to reduce environmental risk.
基金This study was supported by Bualuang ASEAN Chair Professor Fund.
文摘The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming loop.The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices.The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios.To achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process.The approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical examples.The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data.Overall,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally intensive.This approach also shows potential for broader applications in structural damage detection.
