Complex Chemical Processes Research Articles

Dynamic-scale graph neural network for fault detection

Traditional graph-based dynamic fault detection methods describe the dynamic characteristic through constructing a single neighborhood graph at the current sample with some history samples. However, they ignore the diversity of dynamic properties of the variables in complex chemical processes. To overcome this problem, a novel neural network structure combining multiscale subgraphs is proposed, named dynamic-scale graph neural network (DSGNN), which divides variables into multiple groups according to their dynamic properties. DSGNN constructs a subgraph in each group. In traditional graph-based methods, the scale of the graph is usually manually designed. In DSGNN, the scale of each subgraph is decided by the dynamic properties of the variables in this subgraph. To aggregate the dynamic information, DSGNN utilizes convolution operations. The weights assigned to the neighbors in each subgraph are determined according to the similarity between the current data and its neighbors. Low-dimensional features are extracted through the back-propagation technique from the updated high-dimensional features produced by convolution operations. Two case studies on a multivariate dynamic process and the Tennessee Eastman process are conducted to show the superiority of DSGNN.

A Novel Spatiotemporal Process Feature Learning Method Based On the Pseudo-Siamese Network for Complex Chemical Process Concurrent Condition Monitoring.

The deep learning-based process monitoring method has attracted great attention due to its ability to deal with nonlinear correlation. However, the further modeling of learned deep features from process data to better depict typical process features to obtain more precise monitoring results remains a challenge. In this paper, a novel nonlinear spatiotemporal process feature learning method is proposed to extract high-value slow-varying spatiotemporal process features, with an explicit temporal relationship model for the concurrent monitoring of the static deviation and the dynamic anomaly of complex chemical processes. Different from directly mixed spatiotemporal information methods, the pseudo-Siamese autoencoder network is designed with two different subencoders to separately describe the nonlinear spatial and temporal relationships of the nonlinear dynamic input data. Correspondingly, a cost function including three losses and one orthogonal constraint is proposed to make sure that the extracted spatiotemporal process features change as slowly as possible and contain the most nonlinear dynamic information on the input data. With the explicit spatial and temporal relationship submodel, predictions are utilized to shrink the variability of the nonlinear temporal correlated data and focus on the unpredictable variabilities to improve process monitoring performance. Meanwhile, the linear dynamic information is further extracted in the reconstructed residual space by the general slow feature analysis (SFA) method to provide a more detailed analysis of the process characteristics and improve the monitoring results. The case study monitoring results demonstrate the effectiveness and superiority of the proposed method over other compared methods for concurrent process monitoring.

HYDROLYSIS OF PLANT BIOMASS USING RECOMBINANT ALPHA-AMYLASE FROM BACILLUS LICHENIFORMIS AND XYLANASE FROM BACILLUS SONORENSIS

With the reduction of fossil hydrocarbon sources and the increasing need for energy, chemical resources and food, the use of biomass as a renewable source is of great importance. This, in turn, necessitates efficient enzymes for bioconversion of biomass. Enzymes are important in industry because they are biocatalysts for complex chemical processes. The conversion of plant biomass into sugars requires a complex of enzymes whose composition must be adapted to the type of biomass and а pretreatment method. The efficiency of enzymatic hydrolysis can be increased by optimizing the composition of the enzymatic complex, increasing the catalytic activity and stability of the enzymes included in it. A large number of microbial amylases are now commercially available, and they have almost completely replaced chemical starch hydrolysis in the processing industry. The use of bacterial xylanases is a key step in the conversion of lignocellulosic polysaccharides into fermentable sugars for the production of biofuels and value-added products. Enzyme complexes containing both α-amylases and xylanases have found applications in the pulp and paper industry and feed production.
 Recombinant α-amylase and xylanase from Bacillus licheniformis and Bacillus sonorensis, respectively, were obtained using recombinant DNA technology. These enzymes were purified by metal affinity chromatography from the lysate of induced cultures of recombinant strains. Enzymatic hydrolysis of potato starch and birch xylan with these recombinant enzymes was performed. The hydrolysis products of these polysaccharides were studied by thin-layer chromatography. It was found that the products of hydrolysis of potato starch using recombinant α-amylase were maltose and maltooligosaccharides and a small amount of glucose. Xylan was isolated from birch sawdust and hydrolyzed by recombinant xylanase. Detection of the hydrolysis products of xylan showed that xylan is hydrolyzed to oligoxylans with a degree of polymerization of at least 2 monomers. No xylose was detected among the hydrolysis products. The results indicate that α-amylase from Bacillus licheniformis and xylanase from Bacillus sonorensis are endoenzymes.

Economic Oriented Dynamic Matrix Control of Wastewater Treatment Plants

Wastewater Treatment Plants (WWTPs) are industrial facilities, which are important for the protection of the environment, because they remove pollutants from wastewater, before it reaches natural bodies of water. WWTPs consist of complex physical, chemical, and biological energy-intensive processes, which are subject to significant disturbances and uncertainties, due to large variations in the load and quality of the influent. Rising energy prices and increasingly stringent effluent requirements have amplified the need of developing more efficient control schemes for WWTPs. In this paper a novel Economic Dynamic Matrix Control (EDMC) configuration is proposed for WWTPs, where the objective is to minimize the plant’s operating costs in terms of energy savings, while maintaining the effluent quality within acceptable regulatory limits. The novelty of the proposed scheme lies in the combination of the standard Dynamic Matrix Control (DMC) methodology, with economic oriented control strategies. The EDMC predictive models are derived from the application of step tests on the COST/IWA Benchmark Simulation Model No. 1 (BSM1). Based on the BSM1 model, the proposed method is compared to standard Multiple Input–Multiple Output (MIMO) DMC controllers, to the default BSM1 control strategy and to other economic control methods, which have been proposed in the literature. The results illustrate that the proposed EDMC scheme is superior to alternative control strategies in terms of minimizing the energy consumption while, the effluent quality of the plant is maintained at acceptable levels.

Uncovering the Nature of Active Sites during Electrocatalytic Reactions by In Situ Synchrotron-Based Spectroscopic Techniques.

Catalysts can effectively accelerate the reaction kinetics process and are recognized as the core to realize the conversion and supply of carbon-free energy. However, the active sites of catalysts, especially nanocatalysts, usually undergo dynamic structural evolution under realistic working conditions, which may be induced by various reaction effects such as the applied voltages, electrolytes, or adsorbed intermediates. Therefore, in-depth and systemic insights into the nature of the active sites involved under the working conditions are prerequisites for correlating structure-performance relationships. However, uncovering and identifying active sites under operation conditions are still formidable scientific and technical challenges, which are severely hindered by the complex physical and chemical processes occurring on the active sites. Meanwhile, complementary and important information could be missed by conducting only the conventionally employed ex situ microscopic and spectroscopic measurements. Accordingly, it is highly desirable for us to develop the ever-increasing in situ synchrotron-based techniques to identify the nature of active sites, which renders the rational design of functional catalysts achievable.In this Account, we elaborately highlight the substantial achievements in cutting-edge in situ X-ray spectroscopy (XAS) techniques by presenting several representative carbon-neutral electrocatalytic examples performed in our group to broadcast the principles and virtues of identifying the active sites and tracing intermediate species during electrocatalytic water splitting and electrocatalytic CO2 reduction (ECR). Specifically, we believe that the interactions between the active sites and the support as well as the adsorption behaviors of intermediates are considered to be the important factors that govern the performance in the water splitting reaction. Meanwhile, the structural rearrangement of alloy catalysts driven by the cathodic potential significantly governs the activity and selectivity toward ECR. More importantly, the directions and suggestions for addressing the current limitations and pitfalls that we may encounter in the course of executing in situ experiments are also provided. Accordingly, it is necessary to use multiple in situ synchrotron-based techniques to obtain the comprehensive details. Furthermore, bridging the gap between the real energy devices and half-reactions could help us to approach the realistic mechanism. Beyond that, developing the rapid time resolution of in situ XAS will overcome the challenge of timescale mismatch to capture the faster structural kinetics of catalysts. Therefore, this Account is aimed to increase the awareness and appreciation of conducting in situ investigations on energy conversion reactions, which would be a guideline for us to explore catalytic scopes that remain challenging.

Extending machine learning beyond interatomic potentials for predicting molecular properties.

Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its chemical properties. This Review highlights developments in the use of ML to evaluate chemical properties such as partial atomic charges, dipole moments, spin and electron densities, and chemical bonding, as well as to obtain a reduced quantum-mechanical description. We overview several modern neural network architectures, their predictive capabilities, generality and transferability, and illustrate their applicability to various chemical properties. We emphasize that learned molecular representations resemble quantum-mechanical analogues, demonstrating the ability of the models to capture the underlying physics. We also discuss how ML models can describe non-local quantum effects. Finally, we conclude by compiling a list of available ML toolboxes, summarizing the unresolved challenges and presenting an outlook for future development. The observed trends demonstrate that this field is evolving towards physics-based models augmented by ML, which is accompanied by the development of new methods and the rapid growth of user-friendly ML frameworks for chemistry.

A soft sensor regression model for complex chemical process based on generative adversarial nets and vine copula

BackgroundIn the increasingly complex industrial process, it is extremely important to measure the key variables that directly affect the timely operation of the entire process. However, some key variables are challenging to be measured by traditional methods, so it is meaningful to use relevant variables to establish a soft sensor regression model to predict them. Usually, a large number of labeled samples are needed for modeling accurately. However, in some complex chemical industries, only a small number of labeled samples can be used to build a regression model and the model's description of the process is therefore inaccurate. MethodsThis paper proposes a soft sensor method based on generative adversarial nets and vine copula regression (GAN-VCR) to address this problem. This method uses generative adversarial nets (GAN) to generate a large number of samples with a distribution similar to the labeled samples. For more reasonable sample augment, a sample selection strategy based on the generalized local probability (GLP) index is used to select augmented samples from a large number of generated samples to augment the training samples. Then, the augmented training samples are used to build a vine copula regression model and predict the key variables. Finally, numerical and industrial examples are used to prove the effectiveness and practicability of the method. Significant findingsBased on the developed soft sensor, sample augmentation can be effectively performed and the performance can be improved compared to traditional soft sensor methods, and the product quality in practical can be effectively improved.

Mechanisms and Pathways for Coordinated Control of Fine Particulate Matter and Ozone.

Purpose of ReviewFine particulate matter (PM2.5) and ground-level ozone (O3) pose a significant risk to human health. The World Health Organization (WHO) has recently revised healthy thresholds for both pollutants. The formation and evolution of PM2.5 and O3 are however governed by complex physical and multiphase chemical processes, and therefore, it is extremely challenging to mitigate both pollutants simultaneously. Here, we review mechanisms and discuss the science-informed pathways for effective and simultaneous mitigation of PM2.5 and O3.Recent FindingsGlobal warming has led to a general increase in biogenic emissions, which can enhance the formation of O3 and secondary organic aerosols. Reductions in anthropogenic emissions during the COVID-19 lockdown reduced PM2.5; however, O3 was enhanced in several polluted regions. This was attributed to more intense sunlight due to low aerosol loading and non-linear response of O3 to NOx. Such contrasting physical and chemical interactions hinder the formulation of a clear roadmap for clean air over such regions.SummaryAtmospheric chemistry including the role of biogenic emissions, aerosol-radiation interactions, boundary layer, and regional-scale transport are the key aspects that need to be carefully considered in the formulation of mitigation pathways. Therefore, a thorough understanding of the chemical effects of the emission reductions, changes in photolytic rates and boundary layer due to perturbation of solar radiation, and the effect of meteorological/seasonal changes are needed on a regional basis. Statistical emulators and machine learning approaches can aid the cumbersome process of multi-sector multi-species source attribution.

A unified just-in-time learning paradigm and its application to adaptive soft sensing for nonlinear and time-varying chemical process

In traditional just-in-time learning (JITL)-based adaptive soft sensors for complex chemical process, relevant sample selection and local model construction are carried out separately, resulting in that the developed predictive models are usually lack of optimality and validity. This has long been a tough issue for the JITL-based soft sensors. Therefore, this paper proposes a novel unified JITL paradigm based on representation learning and ridge regression. In the UniJITL, the tasks of relevant sample selection and local expert construction are integrated into a unified framework, where the two tasks are cooperatively considered and designed into one optimization problem. Moreover, an efficient solution to the optimization problem with guaranteed convergence is developed. The performance of the UniJITL is evaluated using two real-life chemical processes, and is compared with the state-of-the-art JITL methods, which effectively demonstrate both the predictive advantage and computational feasibility of the UniJITL.

Machine learning aided multi-objective optimization and multi-criteria decision making: Framework and two applications in chemical engineering

To accelerate data-driven studies for various optimization applications in chemical engineering, a comprehensive machine learning aided multi-objective optimization and multi-criteria decision making (abbreviated as ML aided MOO-MCDM) framework is proposed in the present paper. The framework comprises a total of seven steps; firstly, study the application and its input-output datasets to identify objectives, constraints and required ML models; secondly, select ML model(s) for some or all objectives and constraints; thirdly, train the chosen ML model(s), including finding optimal hyperparameter values in each of them using an advanced/global optimization algorithm; fourthly, formulate the MOO problem for the application; fifthly, select a MOO method and develop/test the program; sixthly, solve the formulated MOO problem with the developed/tested MOO program many times and review the Pareto-optimal solutions obtained; lastly, perform MCDM using several methods and choose one Pareto-optimal solution for implementation. The proposed ML aided MOO-MCDM framework is useful for process design and operation of chemical and related processes. It is shown to be beneficial for the optimization of two complex chemical processes, which are supercritical water gasification process aiming for H2-rich syngas with lower greenhouse gas emissions, and combustion process in a power plant targeting for higher energy output and lower pollution of the environment.

Effect of surface curvature and wettability on nucleation of methane hydrate

AbstractNatural gas hydrate nucleation is a complex physical and chemical process that is not well understood presently. In this article, an improved thermodynamic model is proposed to analyze the effects of surface curvature and wettability on methane hydrate nucleation for the first time. The results indicate that methane hydrate nucleation is more difficult on hydrophilic curvature surfaces under the same conditions, with a larger critical nucleation radius and required energy barrier than on hydrophobic surfaces. Furthermore, a convex surface is more favorable for forming methane hydrate under the same conditions than a concave surface. The model's results are critical in elucidating the microscopic mechanism of methane hydrate nucleation and providing a theoretical foundation for developing technologies for strengthening and inhibiting hydrate formation.

In situ feature analysis for large-scale multiphase flow simulations

The study of multiphase flow is essential for designing chemical reactors such as fluidized bed reactors (FBR), as a detailed understanding of hydrodynamics is critical for optimizing reactor performance and stability. An FBR allows scientists to conduct different types of chemical reactions involving multiphase materials, especially interaction between gas and solids. During such complex chemical processes, the formation of void regions in the reactor, generally termed as bubbles, is an important phenomenon. The study of these bubbles has a deep implication in predicting the reactor’s overall efficiency. But physical experiments needed to understand bubble dynamics are costly and non-trivial due to the technical difficulties involved and harsh working conditions of the reactors. Therefore, to study such chemical processes and bubble dynamics, a state-of-the-art computational simulation MFIX-Exa is being developed. Despite the proven accuracy of MFIX-Exa in modeling bubbling phenomena, the large-scale output data prohibits the use of traditional post hoc analysis capabilities in both storage and I/O time. To address these issues and allow the application scientists to explore the bubble dynamics in an efficient and timely manner, we have developed an end-to-end analytics pipeline that enables in situ detection of bubbles, followed by a flexible post hoc visual exploration methodology of bubble dynamics. The proposed method enables interactive analysis of bubbles, along with quantification of several bubble characteristics, enabling experts to understand the bubble interactions in detail. Positive feedback from the experts has indicated the efficacy of the proposed approach for exploring bubble dynamics in very-large-scale multiphase flow simulations.

Stochastic processes shape the aggregation of free‐living and particle‐attached bacterial communities in the Yangtze River Estuary, China

An estuary plays an important role in material and energy exchange between the land and sea, where complex physical, chemical, and biological processes occur. Here, we investigated the assembly processes of free-living (FL) and particle-associated (PA) bacterial communities in two seawater layers at five stations in the Yangtze River Estuary (YRE) by using 16S rRNA sequencing methods. The results indicated that Proteobacteria was the most abundant phylum in the YRE. The α-diversity of PA community was significantly higher than FL community, and analysis of similarity showed significantly different (Global R = 0.2809, p < 0.005). RDA revealed that phosphate (PO4 3- ) was significantly correlated with PA bacterial community abundance (p < 0.05). An ecological null model showed that both PA and FL bacterial communities were mainly influenced by stochastic processes (PA: 100%, FL: 70%), which PA attached to nutrient particles and are less affected by environmental filtration. Dispersal limitation (50%) was the main assembly process of the PA community, while homogeneous selection (30%) and drift (30%) were important processes in the FL community assembly. The available substrate for colonization limits the transformation from FL to PA bacteria. This study would improve our understanding of FL and PA bacterial community structure and factors affecting assembly process in estuarine environments.

Chemical weathering intensity and geochemical characteristics of Cretaceous terrigenous clastic rock‐purple soil profiles in the Pushi area, Xichang

The Pushi area of South‐west China features a large area of purple soil that formed by the weathering of Cretaceous terrigenous clastic rocks. In this study, four vertical profiles of the purple soil (P1, P2, P3, and P4) derived from different pedogenic parent rocks were investigated. Specifically, the chemical weathering intensity and element migration, enrichment, and differentiation characteristics were analysed and the influencing factors of chemical weathering were explored. According to the discrimination map of major and trace elements and content characteristics of rare earth elements, the soil profiles are controlled by complex chemical weathering processes. Various geochemical parameters and indicators exhibited good correlation, and the chemical weathering intensity of the profiles followed the order P1 (mudstone) &gt; P3 (silty mudstone) &gt; P4 (argillaceous siltstone) &gt; P2 (sandstone). The overall chemical weathering intensity of argillaceous rocks was slightly higher than that of sandstone, and the four profiles generally reflected medium to strong chemical weathering. The migration, enrichment, and differentiation characteristics of geochemical elements were found to be mainly affected by different chemical weathering intensities and stages, followed by leaching deposition. The chemical weathering characteristics varied for the four profiles with different pedogenic parent rocks, which could be mainly attributed to internal factors such as the lithology of the pedogenic parent rock and external topographic factors such as altitude.

Electrodeposition of nanostructured Bi2MoO6@Bi2MoO6–x homojunction films for the enhanced visible-light-driven photocatalytic degradation of antibiotics

Complex tunable visible-light-driven Aurivillius-phase-based micro- and nanostructured photocatalysts were fabricated following a novel, scalable, easily implemented sequential process based on electrochemical deposition and thermal treatment. Regarding its novelty, electrochemistry was the primary tool for synthesis, one that constitutes a viable alternative to other complex chemical and physical processes. The parameters of electrodeposition and thermal conditions promoted different nanostructured Bi2MoO6 and Bi2MoO6@Bi2MoO6–x films. The nanostructured homojunction Bi2MoO6@Bi2MoO6–x films obtained after 3 h of calcination showing micro- and nanowire morphology emerged as the most effective photocatalyst for degrading and mineralizing the mono- and multiantibiotic solutions (i.e., tetracycline, ciprofloxacin, and/or levofloxacin), which achieved near-total degradation and exceptionally high mineralization values (>95%) after 180 min of radiation. The materials’ proven reusability, low photocorrosion activity, and excellent photocatalytic performance in mineralizing antibiotics can support the implementation of Bi2MoO6@Bi2MoO6–x homojunctions as efficient visible-light photocatalysts under solar radiation.

A Detailed Temperature Map of the Archetypal Protostellar Shocks in L1157

We present sensitive NH3 (1,1)–(7,7) line images from the Karl G. Jansky Very Large Array toward successive shocks, which are associated with the blueshifted outflow lobe driven by the compact protobinary system L1157. Within a projection distance of 0.1 pc, our observations not only trace the quiescent and cold gas in the flattened envelope, but also illustrate the complex physical and chemical processes that take place where the high-velocity jet impinges on its surrounding medium. Specifically, the NH3 ortho-to-para ratio is enhanced by a factor of 2–2.5 along the jet path, where the velocity offset between the line peak and the blueshifted wing reaches values as high as 10 km s−1; it also shows a strong spatial correlation with the NH3 column density, which is enhanced to >1016 cm−2 toward the shock cavities. At a linear resolution of 1500 au, our refined temperature map from the seven NH3 lines shows a gradient from the warm B0 eastern cavity wall (>120 K) to the cool cavity B1 and the earlier shock B2 (<80 K), indicating shock heating.

An addressable electrowetting valve for centrifugal microfluidics

Centrifugal microfluidic platforms aim to integrate and perform complex biological and chemical processes within disc-like substrates and valving through centrifugal forces and the Coriolis effect. Although the integration of active valves within such system can be used to enhance fluid control, the ability to fabricate and integrate addressable valves within centrifugal platforms remains a significant challenge. To address this issue, herein, we present and test an addressable electrowetting centrifugal (EWC) valve. The EWC valve comprises a hydrophobic burst valve, integrated side electrodes and a dielectric layer between the microfluidic channel and electrode structures. The burst valve is made from a hydrophobic material able hold a specified pressure. Specifically, application of an external electric field is used to alter the valve wettability, making the valve more hydrophilic, decreasing the threshold pressure and triggering the opening of the valve. To assess valve performance, we investigate the influence of electric properties and channel geometry on valve response and then demonstrate multiple operations on different fluid samples. Finally, and to highlight the utility of EWC valves, we demonstrate the detection and analysis of CD4 human immune cells.

Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm.

In order to realize the real-time and accurate prediction of dissolved oxygen concentration in the sewage treatment process, a prediction model of dissolved oxygen concentration in the sewage treatment process based on a data identification algorithm was proposed. Combined with the data characteristics of the sewage treatment process, a new sample similarity measure is defined to extract more representative modeling data. In the improved algorithm, in order to improve the quality of the initial members of the basic fireworks algorithm, the chaos algorithm is integrated. The search mechanism of the basic fireworks algorithm is improved, and the optimization process is divided into two stages based on the set criteria, and two groups are used simultaneously. The results show that compared with the basic FWA algorithm, the CFWA algorithm makes better use of the chaotic search mechanism. On the one hand, it avoids the excessive random or blind selection of the initial weight threshold of the neural network in the initial stage; on the other hand, in the optimization process of the weight threshold, two types of search mechanisms, FWA and COA, are used to give full play to their respective strengths and to continuously conduct information exchange and mutual cooperation between groups and individuals. The number of times is better than the basic FWA algorithm, and the training error and generalization error of the CFWA model in the simulation results of the soft sensor model are also better than those of the FWA model, which fully verifies the effectiveness of the CFWA algorithm. It is proved that the data recognition algorithm can effectively predict sewage treatment. It is proved that the data recognition algorithm can effectively predict the dissolved oxygen concentration in wastewater treatment process. It provides a new measurement method for some key process variables that cannot be measured or are difficult to measure in complex chemical processes.

A fault diagnosis method for complex chemical process based on multi-model fusion

Deep learning methods have become the mainstream research direction in the field of chemical process fault detection and diagnosis, which have great application and research value. However, the existing deep fault diagnosis methods are faced with challenges such as missing data, high-dimensional redundancy and difficulty in fault feature mining, which limits their application in industry. Based on this, a fault diagnosis method for complex chemical process based on multi-model fusion is proposed. This approach avoids over-fitting the model due to excessive redundant data by introducing a FunkSVD matrix decomposition model to augment the missing data without changing the data distribution and then inputting an extreme gradient boosting tree model to learn key features. Finally, the model memory and generalization capability are improved by training a very deep factor decomposer diagnostic model to extract and fuse linear, low-order interaction and high-order interaction features in an all-round way to adaptively establish the correlation between fault features and fault conditions. To validate the model effectiveness, extensive experiments were conducted on the Tennessee Eastman Process dataset and Fluidized Catalytic Cracker fractionation unit dataset, and the results showed that the proposed method has significant performance advantages over existing diagnostic methods in terms of precision and recall metrics.

Exploring the influence mechanism of dissolved organic matter on the bioavailability and thyroid hormone disrupting effect of zinc: A case study of effluents from galvanizing plants

The effect of dissolved organic matter (DOM) on metal bioavailability and toxicity is a complex process. Effluents from galvanizing plants containing large amounts of DOM and Zn were selected to investigate the potential influence and mechanism of DOM on Zn bioavailability and its role in inducing thyroid hormone disrupting effects. Thyroid hormone disrupting effects were evaluated using a recombinant thyroid hormone receptor β gene yeast assay. The results suggest that Zn could be the main metal contributor to the toxic effects. Then, Zn-binding characteristics with different fluorescent components of DOM were analyzed using three-dimensional excitation emission matrix fluorescence spectroscopy (3DEEM) and revealed that Zn was more susceptible to interactions with fulvic-like materials. Furthermore, DOM altered the cellular biouptake and compartmentalization processes of Zn by downregulating Zn transmembrane transport-related genes (ZRT1, ZRT2 and ZAP1) and upregulating detoxification-related genes (COT1 and ZRC1), thus altering thyroid toxicity. These results provide comprehensive insights into the influence and mechanism of DOM on bioavailability and thyroid toxicity of Zn and suggest that the influence is associated with complex physical, chemical and biological processes, indicating that more refined medium constraints along with subtle biological reactions should be considered when predicting the bioavailability and toxicity of Zn in environmental water samples.