Chemical production activities in industrial districts pose great threats to the surroundingatmospheric environment and human health. Therefore, developing appropriate and intelligentpollution controlling strategies for the management team to monitor chemical production processesis significantly essential in a chemical industrial district. The literature shows that playing a chemicalplant environmental protection (CPEP) game can force the chemical plants to be more compliantwith environmental protection authorities and reduce the potential risks of hazardous gas dispersionaccidents. However, results of the current literature strictly rely on several perfect assumptions whichrarely hold in real-world domains, especially when dealing with human adversaries. To addressbounded rationality and limited observability in human cognition, the CPEP game is extended togenerate robust schedules of inspection resources for inspection agencies. The present paper isinnovative on the following contributions: (i) The CPEP model is extended by taking observationfrequency and observation cost of adversaries into account, and thus better reflects the industrialreality; (ii) Uncertainties such as attackers with bounded rationality, attackers with limited observationand incomplete information (i.e., the attacker’s parameters) are integrated into the extended CPEPmodel; (iii) Learning curve theory is employed to determine the attacker’s observability in the gamesolver. Results in the case study imply that this work improves the decision-making process forenvironmental protection authorities in practical fields by bringing more rewards to the inspectionagencies and by acquiring more compliance from chemical plants.@en

Locating and quantifying the emission source plays a significant role in the emergency management of hazardous gas leak accidents. Due to the lack of a desirable atmospheric dispersion model, current source estimation algorithms cannot meet the requirements of both accuracy and efficiency. In addition, the original optimization algorithm can hardly estimate the source accurately, because of the difficulty in balancing the local searching with the global searching. To deal with these problems, in this paper, a source estimation method is proposed using an artificial neural network (ANN), particle swarm optimization (PSO), and a simulated annealing algorithm (SA). This novel method uses numerous pre-determined scenarios to train the ANN, so that the ANN can predict dispersion accurately and efficiently. Further, the SA is applied in the PSO to improve the global searching ability. The proposed method is firstly tested by a numerical case study based on process hazard analysis software (PHAST), with analysis of receptor configuration and measurement noise. Then, the Indianapolis field case study is applied to verify the effectiveness of the proposed method in practice. Results demonstrate that the hybrid SAPSO algorithm coupled with the ANN prediction model has better performances than conventional methods in both numerical and field cases.

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The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

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Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and efficiency in terms of prediction, especially in field cases. However, selection of model type and the inputs of the ML model are still essential problems. To address this issue, two ML models (i.e., the back propagation (BP) network and support vector regression (SVR) with different input selections (i.e., original monitoring parameters and integrated Gaussian parameters) are proposed in this paper. To compare the performances of presented ML models in field cases, these models are evaluated using the Prairie Grass and Indianapolis field data sets. The influence of the training set scale on the performances of ML models is analyzed as well. Results demonstrate that the integrated Gaussian parameters indeed improve the prediction accuracy in the Prairie Grass case. However, they do not make much difference in the Indianapolis case due to their inadaptability to the complex terrain conditions. In addition, it can be summarized that the SVR shows better generalization ability with relatively small training sets, but tends to under-fit the training data. In contrast, the BP network has a stronger fitting ability, but sometimes suffers from an over-fitting problem. As a result, the model and input selection presented in this paper will be of great help to environmental and public health protection in real applications.

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Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF₆ release source. The results demonstrate the effectiveness of the method.

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Online social networks can detailedly and accurately record the activities of human beings and the trajectories of information dissemination over time, which provides us an opportunity to understand the information diffusion process from a renewed, more realistic, data driven modeling dimensionality. In consideration of two fundamental behaviors (viewing and sharing) involved in information diffusion, we propose a stochastic, heterogeneous, continuous-time delay Unknown-View-Share-Removed (UVSR) model to characterize the information diffusion process. The UVSR model introduces four parameters to describe the diffusion probability and speed: viewing/sharing probability/delay. These parameters are subject to some sort of distributions from the actual data, or based on empirical assumptions. To validate the model, we collect and analyze large number of information cascades (tree structure) diffused in WeChat network. We find that the viewing delay and sharing delay are approximately subject to log-normal and power-law distributions respectively, and the sharing probability follows a Gaussian distribution. Driven by these empirical findings and a constant viewing probability assumption, our model can reproduce numerous key features of information diffusion process in both topology and temporal dynamics, such as cascade size distribution, structural virality, life span distribution and relative propagation speed. Our work contributes to a better understanding of the topological features and temporal dynamics of information diffusion from a continuous time, stochastic modeling view.

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