Ключевые слова

2411-3336

2541-9404

Записки Горного института

Journal of Mining Institute

Санкт-Петербургский горный университет императрицы Екатерины ΙΙ

Empress Catherine II Saint Petersburg Mining University

OOZSQX

pmi-16568

https://pmi.spmi.ru/pmi/article/view/16568

Геотехнология и инженерная геология

Geotechnical Engineering and Engineering Geology

Development and integration of an underground mining enterprise ventilation process simulations into the demand response

Разработка и интеграция имитационной модели процесса проветривания подземного горно-добывающего предприятия в сервис управления спросом на электроэнергию

Nikolaev

Aleksandr V.

Николаев

А. В.

Nikolaev

Aleksandr V.

nikolaev0811@mail.ru

0000-0002-4601-5780

Пермский национальный исследовательский политехнический университет (Пермь, Россия) Perm National Research Polytechnic University (Perm, Russia)

Kychkin

Aleksei V.

Кычкин

А. В.

Kychkin

Aleksei V.

avkychkin@hse.ru

0000-0003-0626-5803

Национальный исследовательский университет «Высшая школа экономики» – Пермь (Пермь, Россия) National Research University Higher School of Economics – Perm (Perm, Russia)

24 09 2025

2025

275 18 29 09 08 2024 02 07 2025 31 10 2025

2025

А. В. Николаев, А. В. Кычкин

Aleksandr V. Nikolaev, Aleksei V. Kychkin

Эта статья доступна по лицензии Creative Commons Attribution 4.0 International (CC BY 4.0)

This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0)

https://pmi.spmi.ru/pmi/article/view/16568

Управление процессом проветривания подземных горно-добывающих предприятий (ПГДП), характеризующимся высокой инерционностью и множеством влияющих внешних факторов, по текущим показаниям с датчиков, которые расположены в горных выработках и на поверхности, с высоким уровнем точности регулировки подачи воздуха главной вентиляторной установкой (ГВУ) возможно только в условиях заранее предопределенной траектории управляющих воздействий. Такую задачу можно отнести к задачам аппроксимированного динамического программирования (ADP), что подразумевает синтез субоптимальной функции управления работой ГВУ в режиме предиктивного моделирования процесса воздухораспределения при известном пространстве возможных состояний и выборе наилучшей стратегии управления, обеспечивающей заданный критерий. Приведена имитационная модель подсистемы цифрового двойника процесса управления проветриванием на примере двух типов ПГДП (калийных рудников и нефтяных шахт), которую можно использовать для решения задач ADP. Для предиктивного моделирования воздухораспределения и определения значения критерия энергоэффективности работы ГВУ, потребляющей до половины всей электроэнергии ПГДП, цифровой двойник подключается к внешним данным, с учетом которых оценивается энергопотребление при сохранении подачи требуемого объема подаваемого воздуха. Подобный способ управления позволит не только безопасно и энергоэффективно управлять процессом проветривания, но и участвовать в планировании и реализации мероприятий ценозависимого управления спросом на электроэнергию.

Controlling the ventilation in underground mining enterprises (UME), characterized by high inertia and numerous influencing external factors, based on real-time sensor data located in mine workings and on the surface, with a high level of accuracy in regulating air supply by the main ventilation unit (MVU), is feasible only under conditions of a pre-defined sequence of control actions. This task can be classified as an approximate dynamic programming (ADP) problem, which involves synthesizing a suboptimal control function for MVU operation in a predictive modeling mode of air distribution, given a known space of possible states and the selection of the optimal control strategy that meets a specified criterion. A simulation model of a digital twin subsystem for ventilation process control is presented, using the example of two types of UME (potash mines and oil shafts), which can be used to solve ADP tasks. For predictive modeling of air distribution and determining the energy efficiency criterion of the MVU, which consumes up to half of the total electricity of the UME, the digital twin is integrated with external data, based on which energy consumption is evaluated while maintaining the required volume of supplied air. This control approach enables not only safe and energy-efficient management of the ventilation process but also participation in the planning and implementation of measures for price-dependent electricity demand management.

Ключевые слова управление спросом на электроэнергию кибернетический подход платформа Интернета вещей подземное горно-добывающее предприятие краткосрочное прогнозирование нагрузки цифровой двойник

Keywords electricity demand management cybernetic approach Internet of Things platform underground mining enterprise short-term load forecasting digital twin

Исследования выполнены при поддержке Министерства науки и высшего образования Российской Федерации (проект № FSNM-2024-0005).

The research was funded by the Ministry of Science and Higher Education of the Russian Federation (Project N FSNM-2024-0005).

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