Toward Open-Source Hardware and Software for the Mining Industry: a Case Study of Low-Cost Environmental Monitoring System for Non-Metallic Underground Mines

Mardonova, Mokhinabonu; Choi, Yosoon

doi:10.1007/s42461-019-0093-3

Cited by 14 publications

(5 citation statements)

References 40 publications

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“…The formation of the problem was undertaken subsequent to the finalization of the predetermined set of criteria and securing sensors. As revealed in Figure 2 , Accuracy (C1) [ 15 ], Reliability (C2) [ 16 ], Durability (C3) [ 17 ], Cost (C4) [ 11 ], Ease of Installation (C5) [ 18 ], Power Consumption (C6) [ 19 ], Response Time (C7) [ 20 ], Interoperability (C8) [ 21 ], Maintenance Requirements (C9) [ 22 ], Data Integration (C10) [ 23 ], Environmental Impact (C11) [ 24 ], Scalability (C12) [ 25 ], Regulatory Compliance (C13) [ 26 ], Adaptability to Varied Conditions (C14) [ 27 ], and User-Friendliness (C15) [ 28 ] are determined the most important criteria. Figure 2 illustrates the entirety of the decision system components examined in this current study.…”

Section: Methodsmentioning

confidence: 99%

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Wang,

Cheng,

et al. 2024

Sensors

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This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of “Ease of Installation” as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model’s performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.

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Section: Methodsmentioning

confidence: 99%

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Wang,

Cheng,

et al. 2024

Sensors

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“…Thus, AM can be directly used to achieve low-cost, customer-based and flexible sensors specific to an industrial application and could help companies, e.g., SMEs, with financial inadequacies. As an example of the assistive use of AM, Mardonova and Choi [55] designed a low-cost underground mine monitoring system by using open-source hardware and software. Arduino microcontroller was used to process the environmental data acquired by temperature, gas, humidity and dust sensors and to merge and visualize the gathered data within the MIT App Inventor software.…”

Section: Cost-effective Solutionsmentioning

confidence: 99%

A Systematic Literature Review: Industry 4.0 Based Monitoring and Control Systems in Additive Manufacturing

2023

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Three-dimensional printing, also referred to as additive manufacturing, offers a wide range of product diversity, design flexibility, and competitive product costs, making it a key technology in the Industry 4.0 era. With a growing demand for customer-oriented manufacturing strategies in the industry, 3D printing holds the potential to revolutionize traditional manufacturing systems by enabling the production of high-value-added complex products at reduced costs. This systematic literature review paper aims to analyze the ongoing research on Industry 4.0-based digital solutions in the field of monitoring and control to facilitate the adoption of 3D technologies. The study utilizes a systematic literature review method to provide detailed analyses. Specific keywords and a comprehensive database are employed for this study. Furthermore, the paper surveys the existing advancements in 3D printing machinery, focusing on process monitoring and control methods, as well as their impact on sustainability. The discussion section evaluates the literature review results for potential implementation in small and medium-sized enterprises.

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“…There is also an increase in usage of Open Source Hardware (OSH) in the manufacturing industries [22]. Devices such as Arduino or Raspberry Pi can be often found on the shop floor, particularly in an Internet of Things context.…”

Section: Introductionmentioning

confidence: 99%

Open Source IIoT Solution for Gas Waste Monitoring in Smart Factory

Waters

Waszczuk

Ayre³

et al. 2022

Sensors

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Rapid development of smart manufacturing techniques in recent years is influencing production facilities. Factories must both keep up with smart technologies as well as upskill their workforce to remain competitive. One of the recent concerns is how businesses can contribute to environmental sustainability and how to reduce operating costs. In this article authors present a method of measuring gas waste using Industrial Internet of Things (IIoT) sensors and open-source solutions utilised on a brownfield production asset. The article provides a result of an applied research initiative in a live manufacturing facility. The design followed the Reference Architectural Model for Industry 4.0 (RAMI 4.0) model to provide a coherent smart factory system. The presented solution’s goal is to provide factory supervisors with information about gas waste which is generated during the production process. To achieve this an operational technology (OT) network was installed and Key Performance Indicators (KPIs) dashboards were designed. Based on the information provided by the system, the business can be more aware of the production environment and can improve its efficiency.

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Toward Open-Source Hardware and Software for the Mining Industry: a Case Study of Low-Cost Environmental Monitoring System for Non-Metallic Underground Mines

Cited by 14 publications

References 40 publications

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

A Systematic Literature Review: Industry 4.0 Based Monitoring and Control Systems in Additive Manufacturing

Open Source IIoT Solution for Gas Waste Monitoring in Smart Factory

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