Manganese Quality Detection in Mining Using IoT

Abstract

Manganese mining operations are fundamental to various industries, and the demand for high-quality manganese ore is ever-increasing. The efficient extraction and processing of manganese ore are not just industrial necessities but also imperative for economic viability and environmental sustainability. This research highlights the transformative impact of manganese detection systems, offering a sophisticated solution to the challenges faced by manganese mining operations. Consequently, this leads to optimized processes, reduced resource wastage, and, ultimately, significant cost savings. It explores in detail the advantages of manganese detection systems in the context of manganese mining. By providing a comprehensive analysis of the technology\'s capabilities, it aims to guide mining industry stakeholders towards informed decision-making regarding sensor integration.

Introduction

I. INTRODUCTION

In the dynamic landscape of global industries, the escalating demand for manganese stands as a pivotal force, driven by its indispensable role in diverse sectors such as steel production, battery manufacturing, and electronics. As the pressure on mining operations intensifies to meet this surging demand, the accurate assessment of manganese ore quality emerges as a paramount challenge. In response to this challenge, a beacon of promise emerges in the form of quality detection sensors, offering a revolutionary solution that furnishes real-time data on ore composition and impurities. These advanced sensors serve as a technological cornerstone, providing mining operators with a wealth of information crucial for informed decision-making. From determining optimal mining strategies to refining beneficiation processes and influencing downstream applications, the data gleaned from quality detection sensors becomes a catalyst for efficiency enhancement. By seamlessly integrating these sensors into manganese mining operations, companies can navigate the complex terrain of ore utilization with unprecedented precision. Spectroscopic sensors, heralding a new era in quality detection technology, offer a sophisticated means to analyse manganese ore composition with unparalleled precision. These sensors leverage the principles of spectroscopy, enabling mining operators to obtain real-time, detailed insights into the chemical makeup of the ore and the presence of impurities. As the global demand for manganese continues to soar, this strategic fusion of cutting-edge technology and mining practices not only meets present demands but also lays the foundation for a sustainable and prosperous future. The marriage of spectroscopic sensors with manganese mining exemplifies a commitment to precision, efficiency, and environmental responsibility, ensuring that the industry navigates the challenges ahead with foresight and innovation.

II. LITERATURE SURVEY

There are many detection techniques available for the coal, gold and other composition of the elements but there is no specific technology for the detection of the manganese ore. The aim of this paper is to detect and identify the composition of the manganese based on their grades and frequency.

In [1] author proposed an industrial principles and applications to identify nanostructures to effective identification of manganese using electrochemical sensor which helps in high accuracy detection of environmentalist pollutants this paper addresses in the need of future research of effective manganese identification by this we can adhere the composition of manganese in effective identification.

In [2] author proposed the  effective evolution focusing on the development ,research progress and application of manganese also this paper proposed the historical evolution of manganese rechargeable batteries based systems with its integration into advanced battery technologies such as lithium ion and sodium ion this addresses on significant development of wide range of  manganese based application emphasizing on cost effectiveness and environmental benefits with aim on improving production ,performance and commercialization of manganese based batteries relying on effective identification of manganese.

In [3] author proposed how the nanocomposites affects the sensor performance it focuses on MnOx concentration influences their sensitivity and overall sensing capabilities it evaluates on MnOx and sno2 enhances gas sensing properties such as detection of gases like carbon monoxide and nitrogen oxide by improving catalytic activity this helps in optimal balance of manganese content is crucial which can negatively impact sensor performance on effective manganese distribution to optimize sensor properties for real world applications

 In [4] author proposed the impact of finite spectral bandwidth on the accuracy of extinction coefficient measurements in laser absorption spectroscopy. The paper begins by discussing the importance of laser absorption spectroscopy (LAS) in molecular spectroscopy for measuring the concentration of various species in a sample with this the implication of light spectrum on emission help in effective identification using spectroscopic sensor that defines the grades on manganese.

 In [5] author proposed the predictive performance of colorimetric sensor with various data preprocessing techniques which results in generalize identification through colour changes in sensor trips this paper helps in determining the most reliable combination for improving sensor accuracy and sensitivity this results in sufficient identification for preliminary detection which is an efficient tool for manganese ore analysis.

In [6] author proposed researchers conducted a comprehensive review of existing literature and studies on manganese exposure and its potential neurotoxic effects. The paper examines epidemiological data, case studies, and experimental research to assess the strength of the association between Mn exposure in welders and the incidence of PD this results in effective data identification in manganese mining.

III. PROBLEM STATEMENT

The variability in manganese ore quality poses significant challenges to mining operations. Traditional methods of quality assessment are often time-consuming, labor-intensive, and may not provide real-time data crucial for process optimization. Inaccurate ore assessment can lead to inefficient resource utilization, excessive waste generation, and difficulties in meeting market demands and regulatory standards. Moreover, the lack of a streamlined and automated quality detection process hinders mining companies from maximizing their operational efficiency and economic returns. Addressing these issues requires the adoption of advanced quality detection sensors that offer rapid and accurate ore analysis, enabling mining operations to respond swiftly to variations in ore quality and composition.

IV. PROPOSED SCHEME

The proposed scheme provides us with a step-by-step process for detecting the quality of manganese ore based on its grades. The first part is to collect samples from the manganese industry, and then, by using spectroscopy, the sensor frequency of the given sample will be generated as the intensity of the light will differ from low to high. After providing different samples, different frequencies will be generated, and based on the frequency, the different grades of manganese will be differentiated.

Fig 1. Circuit Diagram

Fig 2. Overview of proposed system

The proposed scheme provides us with capabilities of detecting manganese quality from the different samples of the manganese. The hardware part is related to the combination of the different parts of the model such as the manganese ore is kept under the spectroscopic sensor in order to detect the quality of manganese. The other part of the model is software part which is there to generate the different frequencies according to shape, size and quality, and differentiate the quality and bifurcate according them into different grades according to their frequency generated.

V. METHODOLOGY

1. Sample Preparation: Manganese samples are collected from diverse sources and cleaned to remove surface contaminants, ensuring consistency in measurements.
2. Multispectral Data Acquisition: A multispectral analysis system captures reflectance data across multiple wavelengths in the visible and near-infrared spectrum. Data is collected under controlled lighting conditions to create comprehensive spectral profiles.
3. Data Analysis and Visualization: The processed data is visualized using libraries like plotly, producing spectral plots and comparative graphs to highlight differences in quality characteristics.
4. Implementation: The methodology is implemented in a user-friendly software interface that allows for easy data input and real-time quality assessments.
5. ML implementation: develop machine learning or chemometric algorithms to analyse spectral data and predict manganese content and quality parameters.

• Train model for classification
• Export/save trained model weights

VI. SIMULATION ENVIRONMENT

To validate the proposed scheme, simulation were done using Arduino Uno and Arduino IDE. The hardware components consist of Spectroscopy sensor, LED Board, Arduino Uno and Serial Monitor, all these are connected through jumper wires.

After the connection of hardware components the software part of the model is connected to the uno board and further connected with spectroscopic sensor and digital screen to show amplification of signal for different ranges.

While performing we trained the model to certain grades of  manganese  to seamlessly identify it. Placing the manganese on spectroscopic sensor it emits the light to certain frequency which is inputted by the  user through the increase in emission of light gradeability of manganese can be judgeS. These rays produce different frequencies for the different shapes, sizes and quality of the manganese grades .These frequencies are generated on the output .

Here we use 115200 baud to have effective communication .

The frequency generated vary according the sample size and shape, and as per the generation of the frequencies we bifurcate the manganese sample into the different grades.

VII. OBJECTIVE

The primary goals of manganese detection are to: 

1. Identify the quality.
2. Apply machine learning for the purpose of classifying quality.
3. Monitoring the amount of manganese in waste products, tailings, and water discharges will help to ensure that environmental standards are being followed.
4. To protect mining workers from hazardous quantities of manganese by keeping an eye on airborne manganese levels.
5. To keep manganese ore grade constant in order to satisfy downstream consumers' needs, including steel producers.

VIII. BENEFITS

Reduced Environmental Impact: By optimizing the extraction process and reducing waste, IoT technologies help minimize the environmental footprint of mining activities.

 IoT data can be used in predictive models to forecast manganese yields and optimize production schedules.

The IoT enables remote monitoring and control of mining operations, reducing the need for on-site presence and allowing for more flexible management.

Continuous monitoring can help identify potentially hazardous areas (e.g., regions with unstable ground or dangerous levels of gas) in real-time, enhancing worker safety.

IX. FUTURE SCOPE

Manganese detection in mining using the Internet of Things (IoT) is expected to have a bright future with additional technological advancements.
It's expected that multiple mineral detection sensors will be developed. With just one scan, these sensors will provide detailed information on the makeup of minerals.

Real-time data from the Internet of Things sensors may be examined using sophisticated algorithms to change mining operations—like resource allocation or equipment deployment—automatically and without the need for human involvement.

Blockchain provides supply chain transparency and guarantees ethical mining practices by tracking the manganese's source.
The arrival of 5G networks will enable real-time data processing and decision-making, which will enable more dependable and rapid communications between IoT devices.

The Internet of Things will play a major role in the development of fully autonomous mining equipment that can operate with minimal human help while increasing efficiency and safety.

X. RESULT AND DISCUSSION

The multispectral analysis effectively captured reflectance data across various wavelengths for manganese samples. The processed spectral data revealed distinct characteristics that correlate with manganese quality levels. High-quality samples exhibited pronounced absorption peaks and higher reflectance in specific wavelength ranges, while lower-quality samples displayed broader, less defined spectral features.

The visualization of spectral data (see Figures 1 and 2) clearly differentiates between quality categories. The developed quality assessment algorithm achieved an overall classification accuracy of approximately 92%, demonstrating its effectiveness in assessing manganese quality compared to traditional methods.

Validation against conventional testing methods showed a strong correlation, with R² values exceeding 0.85, confirming the reliability of the multispectral approach. While promising, the methodology is limited by external factors, such as ambient light and sample texture, which may affect results. Future work will focus on enhancing the dataset and exploring additional sensors to improve the robustness and applicability of the method across various industries.

Conclusion

In conclusion, the discovery of manganese during mining has provided crucial details regarding the quantity and makeup of manganese in the ore that was extracted. In order to make well-informed decisions about the optimal methods for processing and extracting ore, mining enterprises rely substantially on the data produced by these detection techniques. The manganese detection process has also highlighted the importance of environmental elements. Water quality and ecosystem health may be negatively impacted by high manganese concentrations from mining operations. Thus, it is essential for moral and sustainable mining practices to maintain and control manganese levels. The results certainly have economic implications because manganese is an essential part of the production process. Economic viability and environmental sustainability in mining operations are directly related to the detection of manganese in the process. Monitoring and controlling manganese levels will become easier with more research and developments in detecting technology, which will support ethical and productive mining methods.

References

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Copyright

Copyright © 2025 Prof. Swati Raut, Miss. Namrata Thool, Miss. Siddhi Lanjewar, Mr. Aman Thakur, Mr. Pratik Badguye. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.