Chemical Industrial Park Research Articles

A conceptual framework integrating numerical simulation with system theory based method for quantitative explosion process hazard analysis

Explosion is a significant threat to chemical process safety, which often occurs suddenly with a wide range of impact, resulting in catastrophic disasters. Predictions of explosion hazards are required for regional safety assessment and disaster resilience enhancement of chemical industrial parks (CIPs). This paper, therefore, aims to develop a conceptual framework for systematically analyzing explosion evolutions and quantifying the potential hazards by combining system-theoretic process analysis (STPA) method with numerical simulation. The proposed framework consists of 5 steps: (i) establishment of hierarchical safe control structures (SCSs) of important chemical processing zones in the CIP, (ii) computational fluid dynamics (CFD) modeling for potential explosion evolutions in each zone by changing the examined parameters randomly, (iii) development of a convolutional neural network (CNN) prediction model through constant self-learning of CFD pressure field data, (iv) comprehensive assessment of blast damage by incorporating the outputs of the above numerical models into existing evaluation methods, (v) identification of unsafety control actions and causes, and safety constraints for the improvement of SCSs. Provided with monitoring data, the developed analysis architecture can predict explosion process hazards and recommend appropriate safety strategies in real time. This would service the multi-level requirements for explosion prevention and protection, supporting better-informed decision-making. The paper describes the concepts and implementation process of the method as a first step.

Multi-plant heat integration with shell-and-tube heat exchangers for multi-period operations

Multi-period heat exchanger network (HEN) can provide various system configurations for seasonal changes in demands, supplies, and different operation conditions of process streams. Current multi-period heat integration has mainly focused on the single-plant recovery, while a chemical industrial park usually consists of several plants that can be linked together through streams. In this study, multi-period HEN has been extended to a multi-plant scale to recover more waste heat. However, as the drawback of a multi-plant HEN, if an unplanned shutdown happens in one plant, other plants will be influenced due to the connected invalid heat exchangers and resort to extra utilities to reach target temperatures. To overcome this, a heat exchanger network with safety redundancy (HEN-SR) model is established for guaranteeing the continuous operation of remaining plants during an unplanned shutdown. Besides, to meet the industrial application, parameters of the real exchanger design are included, including temperature difference correction factor and the number of shell passes. The optimization results of two cases indicate that the mode switching of multi-period HEN-SR model can avoid huge economic losses caused by plant shutdowns, and the one-step synthesis method using a bi-level optimization strategy can find the HEN configuration with a lower total cost.

An event-driven probabilistic methodology for modeling the spatial-temporal evolution of natural hazard-induced domino chain in chemical industrial parks

Natural hazards may rapidly lead to a massive domino chain in chemical industrial parks (CIPs). This work develops a high-efficiency and systematic analytical framework that is applicable to a broad range of uncertain and time-varying factors related to the evolution process of natural hazard-induced domino chain (NHDC). Specifically, the evolution mechanism of NHDC is revealed from a macro-systemic perspective. An event-driven disaster chain evolution system is developed, of which the system state transition is formulated by a Markov decision process and a temporal-difference learning algorithm. A system dynamic risk model is proposed to analyze the dynamic risk associated with NHDC. An earthquake-induced Na-tech scenario is adopted to demonstrate the methodology. Computational results indicate that the proposed methodology is competitive in simulating large-scale system state transition spaces. The involvement of natural hazards would lead to a more complex and severe evolution pattern. Five distinctive stages of the whole NHDC were identified. We found that the value of system dynamic risk is likely to surge in the deterioration stage. Our methodology can dynamically identify the critical system temporal intervals and units at each evolution stage, which has the potential to support the prevention and mitigation of such catastrophic chain events.

A supplementary assessment system of AQI-V for comprehensive management and control of air quality in chemical industrial parks

Volatile organic compounds (VOCs) are the dominant pollutants in industrial parks. However, they are not generally considered as part of the air quality index (AQI) system, which leads to a biased assessment of pollution in industrial parks. In this study, a supplementary assessment system of AQI-V was established by analyzing VOCs characteristics with vehicle-mounted PTR-TOFMS instrument, correlation analysis and the standards analysis. Three hourly and daily scenarios were considered, and the hierarchical parameter setting was further optimized by field application. The hourly and daily assessments revealed the evaluation factors for the discriminability of different air quality levels, practiced value for regional air quality improvement, and the reservation of general dominant pollutants. Finally, the universality testing in ZPIP successfully recognized most of the peaks, with 54.76%, 38.39% and 6.85% for O3, VOCs and NO2 as the dominant pollutant, and reflected the daily ambient air quality condition, together with the dominant pollutant. The AQI-V system with VOCs sub-index is essential for air quality evaluation in industrial parks, which can further provide scientific support to control the pollution of VOCs and the secondary pollutant, therefore significantly improve the air quality in local industrial parks.

Composition profiles of halogenated flame-retardants in the surface soils and in-situ cypress leaves from two chemical industrial parks

There is limited information available regarding the investigation on typical organic pollutants between the soil and in-situ grown plant leaves. This study is to reveal whether the pollution characteristics of soil and leaves can reflect the long-term and short-term pollution situation, and to find the differences between halogenated flame-retardants in the surface soils and in-situ cypress leaves. Polybrominated diphenyl ethers (PBDEs), dechlorane plus (DP), and decabromodiphenyl ethane (DBDPE) in were investigated in two different industrial parks, which were located at the largest brominated flame-retardant-manufacturing center in Weifang, China. These chemicals were frequently detected with high median concentrations of PBDEs (1.22 × 103 ng/g) and DBDPE (227 ng/g) in the soil samples, and DBDPE (881 ng/g) and PBDEs (461 ng/g) in the in-situ cypress leaves. The DP concentration was 1–4 orders of magnitude lower than the other two chemicals in both the matrices. Different composition profiles of the chemicals in soil and cypress leaves were observed. The PBDEs and DBDPE were found to be the predominant species in soils and cypress leaves, respectively. In comparison, the LG industrial parks had higher concentrations of PBDEs and DBDPE in both the soils and cypress leaves. No significant correlations were observed for these chemicals between the soil and leaf samples, although significant correlations (p < 0.05) were observed for several PBDE congeners among all samples from the industrial parks and a separate industrial park. The results indicated that the soil was not the important source of these chemicals in leaves. A large proportion of DBDPE was preferentially present in cypress leaves, which revealed the situation of recent pollution. The results deepen the understanding of chemical distribution characteristics among different environmental matrices in soils and leaves.

Hybrid Gas Sensor Array to Identify and Quantify Low-Concentration VOCs Mixtures Commonly Found in Chemical Industrial Parks

The chemical industrial park is an important source of volatile organic compounds (VOCs) emissions which is difficult to monitor due to its diversity and low concentration. In this work, a hybrid sensor array consisting of six gas sensors (four semiconductor sensors, one electrochemical sensor, and one photo ionization detector) was constructed to measure VOCs. Three most frequently detected VOCs in a Chinese fine chemical industrial park, namely toluene, dichloromethane, ethyl acetate, and their mixtures, were tested all below 5 ppm (even below 1 ppm in many cases). Support vector machine (SVM) and back-propagation artificial neural network (BP-ANN) based on different input features, including steady-state responses (SS), the first four components preprocessed by principal component analysis (PCA) and linear discriminant analysis (LDA), were trained and evaluated. The 10-fold cross validation results indicate that SS-SVM and SS-ANN are two best models with accuracy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{R}^{{2}}$ </tex-math></inline-formula> of about 94% and 0.95 for the validation set, respectively. Compared with LDA, PCA is more suitable for dimension reduction in this study, since the performance of models with the first 4 PCs are closer to that of models with SS. The results demonstrate that the proposed hybrid sensor array system with SVM and ANN models is able to identify and quantify low-concentration VOCs mixtures. This method is promising to be used for low-cost and online monitoring of VOCs emissions in chemical industrial parks.

Vertical profiles of O3, NO2 and PM in a major fine chemical industry park in the Yangtze River Delta of China detected by a sensor package on an unmanned aerial vehicle

The vertical profiles and diurnal variations of air pollutants at different heights in the fine chemical industry park (FCIP) were systematically studied in this study. Air pollutants in a major FCIP in the Yangtze River Delta of China within 500 m above ground level (AGL) detected by a sensor package on an unmanned aerial vehicle (UAV). The air pollutants including ozone (O3), nitrogen dioxide (NO2), particulate matter (PM), total volatile organic compounds (TVOCs) and carbon monoxide (CO), respectively, had been measured through more than one hundred times of vertical flights from Aug. 2020 to Jul. 2021. The concentrations of NO2 and CO generally decreased with the height while the concentrations of O3 increased with the height within 500 m AGL. The photochemical reaction resulted in a strong inverse relationship between the vertical profiles of O3 and that of NO2. The concentrations of PM2.5 and TVOCs generally decreased with the height below 100 m AGL and were fully mixed above 100 m AGL. The vertical profiles of different particle sizes were well consistent with the R2 value of 0.97 between PM1 and PM2.5 and 0.93 between PM2.5 and PM10. The NO2 and PM2.5 concentrations sometimes increased with height maybe due to the influence of temperature inversion layer or long-distance transportation from northern China. The diurnal variations of NO2, O3, TVOCs and CO concentrations at different heights within 500 m AGL were basically consistent. The diurnal variations range of PM2.5 concentrations below 100 m AGL was large and different from other heights, which should be greatly influenced by the local emissions. The unstable atmospheric stability was accompanied by strong photochemical reactions and convective activities, resulting in low concentrations of NO2 and PM2.5, while high concentrations of O3.

Three-dimensional reconstruction of a leaking gas cloud based on two scanning FTIR remote-sensing imaging systems.

Remote sensing imaging technology is one of the most powerful tools for gas leak monitoring in chemical industrial parks. In the case of leaks, it is necessary to quickly and accurately obtain detailed information of the gas cloud (volume, distribution, diffusion situation and location). This paper proposes a 3-D quantitative reconstruction method for gas clouds. Two scanning Fourier transform infrared (FTIR) remote-sensing imaging systems were used to perform telemetry experiments in a monitored space with a total volume of 314.9 m3, and the released gases were SF6 and CH4. One scanning FTIR remote-sensing imaging system can only measure a 2-D concentration-path-length product (CL) image of a 3-D gas cloud, where each pixel has attitude information of elevation and azimuth. Geometric methods are applied to locate the monitored space and construct a 3-D grid (longitude, latitude, altitude). The optical path length (OPL) sparse matrix of each layer is generated, and the concentration distribution of each layer is reconstructed by the simultaneous algebraic reconstruction technique (SART). The reconstructed results of each layer are stacked into a 3-D gas cloud and displayed on the 3-D Earth software at a set threshold. Three-dimensional leaking gas clouds (CH4, SF6) with geometric information and concentration distribution has been generated through the above processes from measurement, localization to reconstruction and display. On the premise that the gas cloud is completely covered by the field of view of each scanning system, the localization and quantification of the gas cloud is available. Then weighted concentration centers can be calculated from these gas clouds to approximate the leak source. The proposed method effectively extends the online leak monitoring application of the scanning FTIR remote-sensing imaging system.

Peripheric sensors-based leaking source tracking in a chemical industrial park with complex obstacles

Hazardous gas leakage can cause irreversible damage to the environment and human health. When it happens, it's necessary to find the accurate position of the leaking source efficiently and take effective measures to reduce or prevent more irreversible losses. However, source tracking in the scenario with complex obstacles faces the challenge caused by turbulent wind flow. In this paper, ethane leak scenarios with different leaking sources and environmental conditions are simulated using the Flame acceleration simulator (FLACS). Considering that sensors are often deployed at the boundaries of industrial parks for the detection of hazardous gas leakage, the concentration information of these peripheric sensors is mapped to images, which serve as inputs to a convolutional neural network (CNN) to determine the location of the leaking source and wind direction in a chemical industrial park with complex obstacles. The results show the effectiveness of the proposed method. In addition, fixed failure rates of the sensor along with additional meteorological conditions are considered to evaluate the performance of generalization.

Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks.

Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network (AQMN) in the CIP. Traditionally, APE can be detected by determining whether the contaminant concentration at any ambient monitoring station exceeds the national environmental standard. However, the environmental standards used are unified in various ambient monitoring stations, which ignores the source–receptor relationship in the CIP and challenges the effective detection of excessive emissions in some scenarios. In this paper, an approach based on a multivariate statistical analysis (MSA) method is proposed to detect the APEs caused by excessive emissions from CIPs. Using principal component analysis (PCA), the spatial relationships hidden among the historical environmental monitoring data are extracted, and the high-dimensional data are projected into only two subspaces. Then, two monitoring indices, and Q, which represent the variability in these subspaces, are utilized to monitor the pollution status and detect the potential APEs in the CIP. In addition, the concept of APE detectability is also defined, and the condition for APE detectability is derived, which explains when the APEs can be detectable. A simulated case for a CIP in Zhejiang province of China is studied to evaluate the performance of this approach. The study indicates that the method can have an almost 100% APE detection rate. The real-world measurements of Total Volatile Organic Compounds (TVOC) at a 10-min time interval from 3 December 2020∼12 December 2020 are also analyzed, and 64 APEs caused by excessive TVOC emissions are detected in a total of 1440 time points.

Real-Time Synchronous 3-D Detection of Air Pollution and Wind Using a Solo Coherent Doppler Wind Lidar

The monitoring and tracking of urban air pollution is a challenging environmental issue. The approach of synchronous 3-D detection of wind and pollution using a solo coherent Doppler wind lidar (CDWL) is developed and demonstrated. The 3-D distribution of pollutant is depicted by the backscatter coefficient based on signal intensity of CDWL. Then, a high-resolution wind field is derived to track the local air pollution source with its diffusion and to analyze transboundary air pollution episodes. The approach is experimentally implemented in a chemical industry park. Smoke plumes caused by point source pollutions are captured well using plan position indicator (PPI) scanning with low elevation. A typical source of pollution is located, combining the trajectory of the smoke plume and the horizontal wind vector. In addition, transboundary air pollution caused by the transport of dust storms is detected in a vertical profile scanning pattern, which is consistent with the results of national monitoring stations and backward trajectory models. Our present work provides a significant 3-D detection approach to air pollution monitoring with its sources, paths, and heights by using a solo-CDWL system.

Water Pollution Control and Comprehensive Utilization Technology of Water Resources in Chemical Industrial Parks

Water Pollution Control and Comprehensive Utilization Technology of Water Resources in Chemical Industrial Parks

Path planning of patrol robot based on improved discrete electrostatic discharge algorithm

Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots.

Research on Spatial Planning of Petrochemical Industrial Parks from the Perspective of Symbiosis: Example of Yueyang Green Chemical Industry Park

As a practical exploration of industry ecologicalization, ecoindustrial parks (EIP) serve as an effective approach to sustainable development. Different from western industrialized countries, China is accelerating its industrialization, and the philosophy of symbiosis is embodied more in the requirements of economy and environmental protection in the production process than in the long-term social and environmental mutual construction. EIPs are a regional system consisting of nature, industry, and society, and the key to achieve industrial symbiosis is systematically allocating resources for industry, city, and people through planning. Based on engineering practice, the authors selected the special space of China Yueyang Petrochemical Industrial Park as the object of discussion, addressing the main problems and challenges facing its development, and focusing on the relationship between industrial symbiosis and spatial symbiosis. From the analysis of the current situation, the circular symbiotic industrial chain network, and the layout of the symbiotic park are included in the spatial planning of the industrial park.

Accurate Identification of Pollution Sources in a Chemical Enterprise Based on a Distributed Multi-channel VOCs Online Monitoring Mass Spectrometry System

A monitoring and traceability management system for unorganized emissions of volatile organic compounds (VOCs) in industrial parks has been established. This system uses distributed multi-channel mass spectrometry to continuously monitor multiple online monitoring points within the enterprise and the factory boundary to initially identify the source of unorganized emissions of VOCs. Based on online monitoring data, the system combined a positive matrix factorization (PMF) model and conditional bivariate probability function (CBPF) methods to accurately identify pollution sources of different scales in the park. The system was successfully applied to a pharmaceutical chemical factory in Taizhou Chemical Industry Park, and VOCs online monitoring was carried out for more than three months at 10 monitoring sites in the factory area. The sources of VOCs were analyzed using the PMF model, and the geographical location information of each pollution source factor was identified by the CBPF method. During the monitoring period, the contribution of chlorobenzene emissions and the number of early warnings were much higher than those of other species. Compared with that of other species, the frequency of abnormal emissions of butene was higher, and the frequency of abnormal emissions of toluene was lower. Among the top ten species, only chlorobenzene had significant concentration changes in different monitoring sites. Ambient VOCs in the plant mainly came from six sources:butene emission, toluene emission, chlorobenzene emission, solvent use, sulfur-related processes, and industrial production. Based on the CBPF results of each pollution source factor relative to the ten monitoring stations, the local pollution source and external pollution source in the plant were distinguished, and the specific location of the local pollution source and the transmission direction of the external pollution source were identified.

Multi-hazard coupling vulnerability analysis for buckling failure of vertical storage tank: Floods and hurricanes

As one of the typical multi-hazard natural disasters, floods and hurricanes have caused destructive damage to the process equipment, especially vertical storage tanks, leading to a large number of severe technological accidents in chemical industrial parks. In the present study, aiming at the buckling behavior under the coupling effect of floods and hurricanes, the wind load, flood load, and wave load are analyzed, and the limit state equation of storage tank buckling failure under the coupling effect of floods and hurricanes is established. Then, the load distribution on the tank wall is verified by FLUENT software and the rationality of FLUENT simulation is shown by laboratory experiments of vertical storage tanks. The fragility curves and surfaces are plotted by Monte Carlo Simulation under different wind speeds, considering the effects of flood velocity, flood inundation height, and liquid filling level. The results show that with the increase of wind speed, the influence of flood inundation height on the vulnerability of storage tanks gradually increases, while the influence of flood velocity and filling level on the vulnerability of storage tanks gradually decreases. Flood inundation height is the main disaster parameter affecting the vulnerability of storage tanks. Compared with the case of floods or hurricanes alone, the buckling failure probability of storage tanks under the coupling effects of floods and hurricanes increases by 17.67% and 80.50%, respectively. Moreover, the damaging effect of the coupling of floods and hurricanes is greater than that of the direct superposition effect, and the failure probability increases by 17.57%. The research aims to analyze the failure mechanism of vertical storage tanks, accident prevention, and control under the coupling effects of multiple hazards.

Pollution characteristics and vertical cutoff wall optimization at an industrial contaminated site in China

Vertical cutoff walls have been widely used in the remediation of contaminated sites. However, determining the best method for evaluating the long-term barrier performance of vertical cutoff walls presents a major difficulty in actual projects. Here, a case study is presented for a typical electroplating, medical, and chemical industrial park in China. Based on the analysis of groundwater pollution characteristics at the site, pollutants included metals (Ni, Al), ammonia nitrogen, and 1,2-dichloroethane. Finite element model simulations of Ni transport at the site showed that a vertical cutoff wall with a thickness of 60 cm and a hydraulic conductivity of 1.0 × 10−8 cm/s could significantly attenuate pollutant transport in the horizontal direction. Compared with other methods such as reducing the hydraulic conductivity or increasing the adsorption retardation factor of the vertical cutoff wall, increasing the thickness was more effective in controlling pollutant transport at the study site. Doubling the thickness would cause the Ni leakage concentration to decrease by more than 98% and the breakthrough time to increase by more than 47 years. It is recommended that the thickness of cutoff walls be maximized to optimize their effects on pollutant transport.

A dynamic risk-based routing approach for multi-source and multi-sink evacuation problem in chemical industrial parks

Chemical industrial park (CIP) is a safety-critical system composed of various hazardous materials related units. Once a chemical accident occurs, evacuation is a typical strategy employed to mitigate the casualties. Currently, the emergency evacuation plans in CIPs are lack of quantitative basis risk. Meanwhile, most related studies ignored the temporal-spatial evolution of chemical accidents. The present work raised a multi-source and multi-sink evacuation model (MMEM), which is driven by the dynamic risk assessment. The human injury model and the evacuation behavior model are adopted to quantify the dynamic evacuation risk. Moreover, the spatial-temporal intersection is considered to avoid the path conflicts caused by multiple evacuation crowds, An efficient prior knowledge-based Dijkstra algorithm (PKDA) is proposed for model solving, and the proposed method is demonstrated by a case study. Simulation results show that PKDA is superior to the traditional Dijkstra algorithm (DA) and the Ant Colony algorithm (ACO), which is competitive in both effectiveness and efficiency. The proposed MMEM model is beneficial to the evacuees select a reasonable evacuation path according to the dynamic evolution characteristics of chemical accidents, which effectively improves the efficiency and safety of emergency evacuation.

Mobile monitoring of VOCs and source identification using two direct-inlet MSs in a large fine and petroleum chemical industrial park

Mobile monitoring with direct-inlet MS (DI-MS), one of the most direct and effective ways to track emission sources, can effectively serve air quality management in chemical industrial parks (CIPs). Mobile monitoring using a high mass-resolution proton-transfer-reaction time-of-flight MS (HMR-PTR-TOFMS) and single-photon ionization time-of-flight MS (SPI-TOFMS) was conducted in a large fine and petroleum CIP in eastern China for three days. The high mixing ratios of aliphatic hydrocarbons (AHs), aromatics, oxygenated VOCs (OVOCs), and nitrogenous VOCs (NVOCs) were found in the northeast, middle, north, and northeast of the fine chemical industrial zone (FCIZ), respectively. OVOCs were the most abundant VOC group in this area. Abnormal emissions of aromatics were universal throughout the CIP. We discovered 38 characteristic VOCs by the HMR-PTR-TOFMS, mainly including C6-C10 aromatics, C2-C6 carbonyls, C2-C3 organic acids, and some NVOCs. The time series and spatial distribution of the TVOCs obtained by the two DI-MSs are generally consistent. A comparison of the speciated VOCs at the TVOC peak points illustrates that the characteristic VOCs obtained by different instruments differed significantly: PTR-TOFMS showed an advantage in measuring aromatics and OVOCs; SPI-TOFMS showed an advantage in measuring aromatics and some Ahs; offline GC–MS showed an advantage in measuring AHs, aromatics, some OVOCs, and halohydrocarbons. Similarities were compared between five positive matrix factorization (PMF) model-based fingerprints of VOCs in a previous study and observed profiles of VOCs from mobile monitoring. The emission sources of the five fingerprints were identified and validated: two were widely distributed, one was a chemical reagent production factory, one was an acrylic fiber production plant, and one was a pesticide factory. This study demonstrated methods for analyzing mobile monitoring data, characterizing the VOCs in the fine and petroleum CIP, correlating the results of stationary observation and mobile monitoring, and integrating the source tracing system with DI-MSs.

An emergency supplies scheduling for chemical industry park: based on super network theory.

In a concentrated area of chemical industry parks (CIPs), emergency relief efficiency is not only affected by the rescue capability of themselves, but also their coordination relationships with other CIPs. Previous studies focus on the location of resource warehouse and the scheduling of logistics transportation, in the relief process after unexpected events, but rarely integrate them ideally in practice. This paper utilizes the super network theory to propose a regional emergency scheduling model to improve collaboration efficiency among primary relief centers (PRC), local relief centers (LRC), and CIPs. So, the proposed super network model fills the research gap of only considering emergency logistic supply chain and provides decision scheme regarding the emergency material dispatch plan. We developed a modified projection algorithm to solve the scheduling problem by turning it to a variational inequality and compare the performance under several disaster scenarios. The practicability of the model is proved by the result of the numerical example given.