An introduction to cybernetics.

/pdf/introduction-to-cybernetics-state-of-the-art-50yldo5448.pdf

Introduction [to: Cybernetics: state of the art]

This paper investigates the application of process mining methodology on the processes of a mobile asset in mining operations as a means of identifying opportunities to improve the operational efficiency of such. Industry 4.0 concepts with related extensive digitalization of industrial processes enable the acquisition of a huge amount of data that can and should be used for improving processes and decision-making. Utilizing this data requires appropriate data processing and data analysis schemes. In the processing and analysis stage, most often, a broad spectrum of data mining algorithms is applied. These are data-oriented methods and they are incapable of mapping the cause-effect relationships between process activities. However, in this scope, the importance of process-oriented analytical methods is increasingly emphasized, namely process mining (PM). PM techniques are a relatively new approach, which enable the construction of process models and their analytics based on data from enterprise IT systems (data are provided in the form of so-called event logs). The specific working environment and a multitude of sensors relevant for the working process causes the complexity of mining processes, especially in underground operations. Hence, an individual approach for event log preparation and gathering contextual information to be utilized in process analysis and improvement is mandatory. This paper describes the first application of the concept of PM to investigate the normal working process of a roof bolter, operating in an underground mine. By applying PM, the irregularities of the operational scheme of this mobile asset have been identified. Some irregularities were categorized as inefficiencies that are caused by either failure of machinery or suboptimal utilization of the same. In both cases, the results achieved by applying PM to the activity log of the mobile asset are relevant for identifying the potential for improving the efficiency of the overall working process.

https://www.mdpi.com/2079-9276/9/2/17/pdf

Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case

The Internet of Things (IoT) market is currently one of the most dynamically developing branches of technology. The increase in the number of devices and their capabilities make them present in every area of life—from construction to medicine. Due to flourishing industrial automation and wireless connectivity, all devices in buildings can be networked, which significantly improves comfort, energy efficiency, and thus significantly affects the promotion of the idea of sustainable development. This article proposes to build a system to monitor an energy management system by collecting information about temperature, humidity and pressure. The concept of the system is based on the Message Queue Telemetry Transport (MQTT) communication protocol, a wireless network, an ESP8266 microcontroller and a BME280 sensor. Sensors and actuators are connected to the ESP8266, and a Mosquitto-based MQTT broker is placed on the RPi and set up for remote monitoring and control. An innovative approach to the use of database systems dedicated to time series on the example of InfluxDB and data visualization in the Grafana environment was also presented. The proposed system was built in laboratory conditions in 2022 and used to monitor and control energy consumption in real time, depending on Polish weather conditions.

/pdf/the-use-of-the-mqtt-protocol-in-measurement-monitoring-and-3b6g0h06.pdf

The Use of the MQTT Protocol in Measurement, Monitoring and Control Systems as Part of the Implementation of Energy Management Systems

Abstract. Methane is a powerful greenhouse gas responsible for around 20 % of
radiative forcing (relative to the pre-industrial era) caused by all
long-lived greenhouse gases (WMO, 2021). About 60 % of the global emissions
are from anthropogenic sources, and coal mining is one of the largest
contributors. Emissions are either estimated by bottom-up approaches (based
on inventories) or top-down approaches (based on atmospheric measurements).
Combining those with an accurate error estimation allows us to better
characterise model errors e.g. caused by transport mechanisms. Here we provide a detailed description of factors influencing the coal mine
methane emission variability. We use high-frequency (up to hourly) temporal
data from seven coal mines in the Upper Silesian Coal Basin during the Carbon dioxide and Methane (CoMet 1.0) mission from 14 May to 13 June 2018. Knowledge of these factors for the individual ventilation shaft is essential
for linking the observations achieved during the CoMet 1.0 mission with models, as
most publicly available data in the bottom-up worldwide inventories provide
annual emissions only. The methane concentrations in examined shafts ranged from 0.10 % to 0.55 %±0.1 % during the study period. Due to the changing scope of mining
works performed underground, they were subjected to a significant variation
on a day-to-day basis. The yearly methane average emission rate calculated
based on 1 month's set of temporal data of the analysed subset of mines was
of the order of 142.68 kt yr−1 (σ=18.63 kt yr−1), an
estimate 27 % lower than the officially published State Mining
Authority (WUG) data and 36 % lower than reported to the European Pollutant
Release and Transfer Register (E-PRTR). We also found that emissions from individual
coal mine facilities were over- and underestimated by between 4 % to 60 %, compared to the E-PRTR, when short-term records were analysed. We show
that the observed discrepancies between annual emissions based on temporal
data and public inventories result from (1) the incorrect assumption that the
methane emissions are time-invariant, (2) the methodology of
measurements, and lastly, (3) the frequency and timing of measurements. From the emission monitoring perspective, we recommend using a standardised
emission measurement system for all coal mines, similar to the
Methane Fire Teletransmission Monitoring System (SMP-NT/A). Legal safety requirements
require all coal mines to implement this system. After an adaptation, the
system could allow for gas flow quantification, necessary for accurate and
precise estimations of methane emissions at a high temporal resolution.
Using this system will also reduce the emission uncertainty due to factors
like frequency and timing of measurements. In addition, it would be
beneficial to separately identify the emissions from individual ventilation
shafts and methane drainage stations. That would bridge the gap between
bottom-up and top-down approaches for coal mine emissions. The intermittent
releases of unutilised methane from the drainage stations are currently not
considered when constructing regional methane budgets.


https://acp.copernicus.org/articles/22/16031/2022/acp-22-16031-2022.pdf

Factors influencing the temporal variability of atmospheric methane emissions from Upper Silesia coal mines: a case study from the CoMet mission

/pdf/systemy-monitoringu-w-kopalni-podziemnej-56cfd1boen.pdf

Systemy monitoringu w kopalni podziemnej

Procesy wzbogacania mają decydujący wplyw na zbyt produktow handlowych. Waznym zagad-nieniem jest uzyskiwanie maksymalnej wartości produkcji z wegla surowego przy roznych kontrak-tach handlowych. Istotne jest wiec poszukiwanie nowych sposobow zwiekszenia wartości produkcji.

/pdf/ocena-oplacalnosci-wzbogacania-wegla-w-ukladach-osadzarek-251aitt29a.pdf

Ocena Opłacalności Wzbogacania Wêgla W Układach Osadzarek

Dołowe sieci telekomunikacyjne z kablami miedzianymi w kopalniach JSW SA

Nowe elementy systemu telekomunikacyjnego HETMAN

Wykorzystanie przewodu promieniującego w systemie lokalizacji pojazdów w kopalniach

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