Mathematical model of chemical process prediction for industrial safety risk assessment

Artamonov, Alexey; Bogdanova, Leila M.; Nagibin, Sergey Ya.; Loskutov, Dmitry I.; Chemakin, Alexander S.

doi:10.1016/j.procs.2021.06.013

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…In this case, the boundaries of the subdomains are blurred, which means that several laws can be fulfilled at once, but with different degrees. The degree of ownership of the input vector 𝑋 * = ( 𝑥 1 * , … , 𝑥 𝑛 * ) to values 𝑑 𝑗 = 𝑏 𝑗0 + ∑ 𝑏 𝑖𝑗 𝑥 𝑖 𝑖= 1,𝑛 ̅̅̅̅̅ is calculated as follows (10):…”

Section: Input Vectormentioning

confidence: 99%

“…In addition, motion planning for several pieces of equipment simultaneously is not considered in classical graph theory. The methods of Levenberg-Marquardt, gradient descent, as well as the methods of nonlinear regression analysis used to calculate the schedule, make it possible to bring the calculated data closer to the experimental ones, but at the same time lead to a number of problems: firstly, they give significant errors when trying to predict the behavior of the "priority function "; secondly, such methods for the most part work with functions of a given type, while in this case the function is generally unknown [9], [10]. The purpose of this article is to increase the availability of resources and capacity of a transport railway graph with a complex topology to ensure business continuity of an industrial enterprise by developing a mathematical dispatching model that includes a mathematical model for optimizing the schedule for using transport resources and monitoring their execution.…”

Section: Introductionmentioning

confidence: 99%

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Neuro-fuzzy-based mathematical model of dispatching of an industrial railway junction

Bogdanova

Nagibin²,

Loskutov³

et al. 2023

Bulletin EEI

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In any transport system, especially at industrial railway junctions, it is fundamentally important to build an effective timetable (traffic schedule) to regulate traffic flows. The task is complicated by the high dimensionality of the railway network of the node, the large number of variable parameters associated with scheduling the use of a traction resource (locomotives) during operation for sorting wagons and transporting payloads (ore, fuel, finished products and empty wagons). The problem is that most plotting problems are NP-hard, i.e. the algorithms for solving them, used to automate the process, may require an unacceptably long execution time by traditional methods of solving this problem (sequential, using reference information; method of thread laying). The article deals with the issues of building a mathematical model for dispatching an industrial railway junction to minimize the time of using locomotives in order to increase the efficiency of its operation. The mathematical model uses the technique of neuro-fuzzy computing to determine the parameters for identifying fuzzy systems and calculating the priorities of operations in the framework of creating a flexible schedule for the decision support system of the dispatching service. The results of modeling and recommendations on the use of the developed methodology are presented.

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Section: Input Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy-based mathematical model of dispatching of an industrial railway junction

Bogdanova

Nagibin²,

Loskutov³

et al. 2023

Bulletin EEI

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show abstract

“…A mathematical model of the styrene production process function was established by using neural network technology. Based on the prediction results, some suggestions are put forward for the industrial safety assessment of special hazardous production processes [18]. Bozzano M. et al…”

Section: Introductionmentioning

confidence: 99%

Application of Extension Engineering in Safety Evaluation of Chemical Enterprises

Han

Liu

2022

Applied Sciences

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To effectively analyze the safety risk of chemical enterprises and ensure the safety of production and management of enterprises, the contradiction problems in the process of index selection and risk early warning model in practical application are addressed. In this paper, extension engineering is introduced into the safety-security field of chemical enterprises to extract hidden useful information from the production environment and outdoor environment data and provide decision support for the managers of chemical enterprises. First, based on data preprocessing and extension analysis, the safety-security data of chemical enterprises that meet the quality requirements and can be efficiently mined are searched. Then, the outdoor environment is combined in the paper to conduct the mining of these data in two aspects: (1) comprehensive analysis and evaluation of data quality; (2) key factors affecting factory safety mining, realizing the safety-security evaluation of intelligent factories in chemical enterprises. Based on the proposed chemical factory safety extension prerisk model, the risk assessment of the safety status of a chemical enterprise in Hebei Province is carried out. The research results of this paper provide a theoretical basis for the safety production analysis of such chemical enterprises and put forward practical suggestions for preventing possible accidents in the production process.

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Risk Assessment and Early Warning Model for Water Conservancy Projects Based on IoT and Big Data

Yang,

Zhao

2024

Lecture Notes in Electrical Engineering

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Mathematical model of chemical process prediction for industrial safety risk assessment

Cited by 4 publications

References 4 publications

Neuro-fuzzy-based mathematical model of dispatching of an industrial railway junction

Neuro-fuzzy-based mathematical model of dispatching of an industrial railway junction

Application of Extension Engineering in Safety Evaluation of Chemical Enterprises

Risk Assessment and Early Warning Model for Water Conservancy Projects Based on IoT and Big Data

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