Abstract
Chemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemical gas leakage accidents, source tracking technology of chemical gas leakage has been proposed and evolved. This paper proposes a novel method, Outlier Mutation Optimization (OMO) algorithm, aimed to quickly and accurately track the source of chemical gas leakage. The OMO algorithm introduces a random walk exploration mode and, based on Swarm Intelligence (SI), increases the probability of individual mutation. Compared with other optimization algorithms, the OMO algorithm has the advantages of a wider exploration range and more convergence modes. In the algorithm test session, a series of chemical gas leakage accident application examples with random parameters are first assumed based on the Gaussian plume model; next, the qualitative experiments and analysis of the OMO algorithm are conducted, based on the application example. The test results show that the OMO algorithm with default parameters has superior comprehensive performance, including the extremely high average calculation accuracy: the optimal value, which represents the error between the final objective function value obtained by the optimization algorithm and the ideal value, reaches 2.464e-15 when the number of sensors is 16; 2.356e-13 when the number of sensors is 9; and 5.694e-23 when the number of sensors is 4. There is a satisfactory calculation time: 12.743 s/50 times when the number of sensors is 16; 10.304 s/50 times when the number of sensors is 9; and 8.644 s/50 times when the number of sensors is 4. The analysis of the OMO algorithm’s characteristic parameters proves the flexibility and robustness of this method. In addition, compared with other algorithms, the OMO algorithm can obtain an excellent leakage source tracing result in the application examples of 16, 9 and 4 sensors, and the accuracy exceeds the direct search algorithm, evolutionary algorithm, and other swarm intelligence algorithms.
Highlights
The world’s rapid economic development, urbanization, and industrialization make people increasingly dependent on the chemical industry, machinery industry, and electronics industry, etc
High-precision OMO algorithm results are often accompanied by suitable exploration steps, and low-precision OMO algorithm results are often accompanied by too-large exploration steps
The reason why some SH are too large but can achieve accuracy of the algorithm is mainly due to the random allocation of super boundary data, but in the chemical gas leakage accident model established by this paper, the best value of SH is 1–60
Summary
The world’s rapid economic development, urbanization, and industrialization make people increasingly dependent on the chemical industry, machinery industry, and electronics industry, etc. The storage of raw materials, the production of spare parts, the assembly of machines, and the testing of technology often need to be carried out in specific places. Among the above industrial processes, the chemical industrial park plays an important role in the storage of raw materials and chemical reaction tests [1,2,3,4]. There are a large number of chemicals, many of which are flammable, explosive, and hazardous gases [5]. Poisonous gas leakage accidents are challenges for various industries like the petrochemical and gas industries, which will result in financial 4.0/). Large-scale poisoning disasters or internal domino chain explosion effects may even occur [7]
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