1. Introduction
As the use of chemicals continues to increase, leaks of hazardous chemicals [
1] also occur frequently. Once such accidents occur, they can easily lead to serious consequences, such as property damage and casualties. Hydrogen sulfide [
2] is often found in natural gas, which is a very important chemical raw material and is used in various fields, and there are many accidents caused by hydrogen sulfide leakage. On 12 July 2021, a hydrogen sulfide leak occurred at the Maidokawa Nuclear Power Plant in Miyagi Prefecture, Japan. The gas leaked from a storage tank in the waste treatment room of Unit 1 of the nuclear power plant and entered the adjacent Unit 2 control room through an air duct. Seven staff members in the control room suffered from varying degrees of poisoning after inhaling hydrogen sulfide. On 25 January 2021, a serious safety incident occurred at Sorik Marapi Geothermal Energy Ltd. in Indonesia during the commissioning of a power plant. When the geothermal well was opened, hydrogen sulfide gas [
3] was released into the air, resulting in the death of five villagers from a nearby village and the hospitalization of nearly 20 villagers.
The accident analysis shows that incidents and accidents caused by poisoning by toxic and harmful gases of hydrogen sulfide have occurred repeatedly. Hydrogen sulfide is very dangerous in the production process, storage process, and transportation process and is prone to leakage accidents, and the lack of scientific research and evaluation of the spread of hydrogen sulfide leakage and the blind rescue of the rescuers at the scene will cause further expansion of the accident. It will also cause great difficulties for rescuers to organize a rescue. If the scope of hydrogen sulfide leak diffusion cannot be roughly calculated in advance, the personnel involved in the rescue will also be harmed, and once a leakage accident with serious consequences and great impact occurs, it will cause serious harm to people’s lives, properties, and the atmospheric environment. If the diffusion concentration of hydrogen sulfide leakage can be predicted in real-time, it can effectively help the emergency rescue team to determine the alert area and adjust the scope of the alert area in time to provide a technical guarantee for the emergency rescue work in the process of emergency rescue.
For the study of the method of toxic heavy gas leak diffusion prediction, mainly from the four aspects of experimental, theoretical, simulation, and statistical start, since the 1960s, some scholars have conducted many large-scale heavy gas release experiments.
Table 1 lists a few typical experiments [
4,
5,
6,
7,
8,
9], with most of these experiments in the field open space because the heavy gas itself has a certain degree of danger. In addition, the field experiments have an experimental preparation workload and field experiments have the disadvantages of large experimental preparation, poor experimental reproducibility, and unpredictable experimental results.
To simplify the field experiments and make the operation of the experiments easier to control, researchers have found that the simulation of heavy gas diffusion can be carried out using laboratory simulation studies, such as wind tunnel tests and water tank experiments. As a result of its advantages of convenient field observation [
10] and relatively simple operation, many experts have carried out wind tunnel simulation tests [
11] with rich results, and European experts in heavy gas research have also established a set of wind tunnel test databases for researchers to analyze and study. However, the method cannot fully simulate the real leakage scenario, and the setting of the surrounding atmosphere and other conditions cannot be fully restored, so there is still a certain degree of error, and the reliability of the experimental results needs to be proven.
At the beginning of gas diffusion research, researchers believed that the gas followed a Gaussian distribution in the diffusion process, so the Gaussian diffusion model has been used for simulations of the diffusion of heavy gas [
12]. However, with further scientific studies, researchers found that the Gaussian diffusion model [
13] was more accurate in simulating the passive diffusion process of neutral and light gas clouds, and the results obtained in predicting the diffusion of simulated heavy gases differed greatly from the actual situation. Later, after decades of development and improvement, researchers established mathematical models applicable to the study of heavy gas diffusion simulations. In the 1970s, Blackmore and Wheatly [
14,
15] conducted a review and analysis of most of the heavy gas [
16,
17,
18,
19] diffusion models established at that time. The ones that are the most applied currently include the BM model [
20], box model [
21], shallow model [
22], and hydrodynamic approach [
23], but there are great limitations in the use of these models.
With the development of computer technology, numerical simulation and analysis methods have gradually become the main way to analyze the diffusion process of heavy gas. Hanna [
24] used CFD models to numerically simulate the diffusion process of chlorine gas leakage in different scenarios, and their simulation results proved that CFD models simulate the local effects of building wake, slight terrain differences, and some mitigation measures (e.g., fencing). Pontiggia [
25] used CFD tools to simulate the consequences of LPG railcar rupture in an urban area and found that obstacles have a significant impact on LPG dispersion in urban areas and that the real observed damaged areas are very similar to those predicted by CFD models. Dong [
26] conducted a comprehensive field test of heavy gas leakage from a large building and simultaneously used four commonly used turbulence models for tests, which are important for the emergency response in the event of a leak in a large indoor space. Pan [
27] used Fluent software to simulate the Thorney Island 026 experiment to observe the effect of obstacles on heavy gas diffusion and demonstrated that CFD simulates the heavy gas diffusion process in complex terrain. Gao [
28] used Fluent software to simulate a natural gas pipeline leak to discuss the effect of wind speed on gas diffusion under different working conditions. This work can help emergency managers delineate the danger zone and evacuate people in time to ensure the smooth operation of emergency rescue work. However, to more realistically restore the situation when the gas leaks, the model grid needs to be finely divided, which makes the computer’s solution speed greatly reduced, and sometimes a large simulation project will take several days to receive the results of the simulation solution calculation, so it cannot track the prediction of heavy gas leakage accidents in real-time.
The advent of artificial intelligence has led to the integration of big data statistical methods into various disciplines, and this data-driven approach has led to the development of cross-disciplinary approaches and the rapid development of related theories that have brought unprecedented opportunities for the study of heavy gas leak prediction models. Some researchers have used artificial neural networks [
29] or support vector machines [
30] to predict the concentration change after a gas leak [
31,
32,
33,
34,
35]. Kolehmainen [
36] used an artificial neural network algorithm to computationally predict the urban air quality in Stockholm, and the results showed that the algorithm obtained fairly good predictions. Wang et al. [
37] developed a fast method for predicting gas dispersion that collects data through gas detectors and then imports the data into an artificial neural network model for simulation, which allows bypassing difficult-to-obtain input parameters and using easily available parameters to predict gas dispersion concentrations for certain scenarios. Qiu et al. [
38] proposed a new method for locating emission sources using artificial neural networks, particle swarms, and expectation maximization. A field study was conducted in Indianapolis to demonstrate that the method is feasible in practice, and the results of the field study show that the proposed method can estimate emission sources with acceptable accuracy and efficiency.
In this study, by analyzing the research history of heavy gas leak diffusion mentioned above, we found that researchers have conducted experiments and simulations for the situation after the accident. The existing gas diffusion concentration prediction models cannot combine accuracy and real-time, so there is a relative gap in the research on real-time prediction models for heavy gas leak diffusion concentrations. We consider the use of machine learning algorithms to develop a real-time prediction model for hydrogen sulfide leak dispersion concentration. Firstly, the CFD simulation software is used to simulate the hydrogen sulfide leakage dispersion process, and the data results obtained from the simulation are exported. After that, the data are processed and used as input samples for the machine algorithm model. Finally, the prediction models trained by machine learning are compared and evaluated to verify the accuracy and reliability of the models. The study will not only improve the safety level of the whole chemical park but also provide a richer, more intuitive, and more accurate decision-making basis for emergency rescue operations in case of accidents.
5. Conclusions
Once the toxic heavy gas leaks, it will not only cause casualties and property losses but also the air pollution it causes seriously restricts the sustainable development of the environment. In this paper, based on a systematic review of heavy gas leak diffusion concentration research, a real-time prediction model of hydrogen sulfide leak diffusion concentration based on a machine learning algorithm is proposed, and the method is validated by taking a chemical park as an example. We use Fluent simulation software to study the influencing factors affecting hydrogen sulfide leak dispersion and train the simulation results as the sample data for the prediction model. The accuracy of the computational results and the excellent model performance were compared between traditional neural networks, integrated learning, and deep learning, and the superiority of the performance of the support vector machine model optimized by the sparrow search algorithm was demonstrated. The experimental results show that our model can be used for real-time prediction of diffusion concentration of hydrogen sulfide gas leakage in chemical parks, and the accuracy of its prediction results can meet the requirements of practical engineering applications. It not only has a certain guiding significance for the emergency rescue operations after the leakage of hydrogen sulfide gas in chemical parks but also has a certain reference significance for the planning and design of safety development in chemical parks. In addition, in terms of prediction time, the prediction of hydrogen sulfide leak diffusion concentration using the machine algorithm model is less than 1 s. It can be seen that the machine learning algorithm greatly reduces the computational time cost while ensuring the accuracy of the prediction results.
Related studies have demonstrated that heavy gas leak dispersion can be simulated using computational fluid dynamics simulation software, and heavy gas leak dispersion can be predicted using artificial intelligence algorithms. This study not only demonstrates the feasibility of these methods but also proposes a more novel approach to predict the diffusion concentration of hydrogen sulfide leaks in real-time. Despite the innovative nature of the methods used in this study, there are some shortcomings (1) for the applicability of the model. Although the model can be extended to other cases of heavy gas leakage, it is only applicable to point source leakage of the gas in a particular scenario. If it is applied to other scenarios, it is still necessary to re-collect data and organize the data samples to retrain the model. (2) The amount of sample data required for training machine learning algorithms is huge, which plays a key role in the success of the prediction model. However, existing data on heavy gas leaks such as hydrogen sulfide are scarce and difficult to obtain, so this paper uses CFD software to perform accurate numerical simulations and uses the data results obtained from the simulations as samples for modeling. Although the prediction time is significantly saved, the cost of collecting data upfront is too large in order to reproduce the real situation of leakage.