Optimizing Tropical Mine Dewatering Using IoT-Enabled Hybrid Diesel-Solar PV Pumping with Real-Time Data Integration

A novel method to determine the optimal operating point for centrifugal pumps applied in photovoltaic pumping systems

This paper represents an electrical modeling of a photovoltaic water pumping system composed of photovoltaic panels. The system reassures electrical energy generation applying a DC buck converter in order to supply a DC motor to pump water. Known for its optimal electrical and physical properties, the super capacitor is integrated into the PV pumping system to allow storing electrical energy and powering the pumping motor at nights and cloudy days. The implementation of the perturbation and observation algorithm in the DC buck converter reassures the system’s operation around the maximum power point produced by the photovoltaic panel. Moving from quantity problem to quality problem, several internal and external factors can infect the stability of the PV pumping system and disrupt its operation around nominal point. On account of this, a pole placement technique is proposed in this paper. This technique is advocated not only for its simple processing and rapid execution, but also for being based on the conversion of the electrical model of the PV pumping system into a mathematical model that comprises matrix equations. This energy conversion process allows variations in output values in proportion to the input evolution along with positioning the system’s poles in the stable points of the S-plane. The equation of this technique applied to the photovoltaic pumping system is indicated in this paper having its program created in Matlab and installed in PLC S7-200 on account of its ability to monitor the inputs instantaneously and modify the outputs in a way proportional to the installed program. Subsequently, the validation of the robustness of this technique is reassured by the comparison between the simulation in Matlab and the experimental tests carried out in sunny periods having the engine powered by photovoltaic panels, and shaded periods where power supply is reassured by the super capacitor.

Optical Camera Communication (OCC) is an emerging technology that has attracted research interest in recent decades. Unlike previous communication technologies, OCC uses visible light as the medium to transmit data from receivers and cameras to receive the data. OCC has several advantages that can be capitalized in several implementations. However, the Internet of Things (IoT) has emerged as a technology with immense potential. Numerous research endeavors support the IoT’s prospective technology that can be implemented in various sectors, including the healthcare system. This study introduces a novel implementation of the Internet of Medical Things (IoMT) system, using OCC for real-time health monitoring and indoor location tracking. The innovative system uses standard closed-circuit television CCTV setups, integrating deep learning-based OCC to monitor multiple patients simultaneously, each represented by an LED matrix. The effectiveness of the system was demonstrated through two scenarios: the first involves dual transmitters and a single camera, highlighting real-time monitoring of vital health data; the second features a transmitter with dual cameras, focusing patient movement tracking across different camera fields of view. To accurately locate and track the position of LED arrays in the camera, the system used YOLO (You Only Look Once). Data are securely transmitted to an edge server and stored using the REST API, with a web interface providing real-time patient updates. This study highlights the potential of OCC in IoMT for advanced patient care and proposes future exploration in larger healthcare systems and other IoT domains.

Photovoltaic pumping systems are a promising solution for water supply in remote areas, yet their performance is often impacted by environmental factors such as varying irradiation and temperature. Numerous strategies and control algorithms have been developed and applied to enhance the performance of photovoltaic pumping systems, often by incorporating a direct current (DC) converter between the photovoltaic generator and the inverter. This paper explores the improvement of photovoltaic pumping systems through the adjustment of frequency and speed control, responding to variations in irradiation and temperature, without the need for a DC/DC converter. Speed control is achieved through a combination of Fuzzy Logic and Passivity-Based (FLC-PBC)approach. The integration of these two control strategies enhances the system’s performance, enabling it to operate closer to its maximum efficiency point, even under fluctuating environmental conditions. Simulation results demonstrate significant improvements in system stability, efficiency, and energy conversion, highlighting the potential of this hybrid control approach for optimizing photovoltaic pumping systems in renewable energy applications.

Open-pit mining often induces geological hazards such as slope instability, surface subsidence, and ground fissures. To support sustainable mine operations and safety, high-resolution monitoring and mechanism-based interpretation are essential tools for early warning, risk management, and compliant reclamation. This study focuses on the Baorixile open-pit coal mine in Inner Mongolia, China, where 48 Sentinel-1 images acquired between 3 March 2017 and 23 April 2021 were processed using the Small-Baseline Subset and Distributed-Scatterer Interferometric Synthetic Aperture Radar (SBAS-DS-InSAR) technique to obtain dense and reliable time-series deformation. Furthermore, a Trend–Periodic–Residual Subspace-Constrained Regression (TPRSCR) method was developed to decompose the deformation signals into long-term trends, seasonal and annual components, and residual anomalies. By introducing Distributed-Scatterer (DS) phase optimization, the monitoring density in low-coherence regions increased from 1055 to 338,555 points (approximately 321-fold increase). Deformation measurements at common points showed high consistency (R2 = 0.97, regression slope = 0.88; mean rate difference = −0.093 mm/yr, standard deviation = 3.28 mm/yr), confirming the reliability of the results. Two major deformation zones were identified: one linked to ground compaction caused by transportation activities, and the other associated with minor subsidence from pre-mining site preparation. In addition, the deformation field exhibits a superimposed pattern of persistent subsidence and pronounced seasonality. TPRSCR results indicate that long-term trend rates range from −14.03 to 14.22 mm/yr, with a maximum periodic amplitude of 40 mm. Compared with the Seasonal-Trend decomposition using LOESS (STL), TPRSCR effectively suppressed “periodic leakage into trend” and reduced RMSEs of total, trend, and periodic components by 48.96%, 93.33%, and 89.71%, respectively. Correlation analysis with meteorological data revealed that periodic deformation is strongly controlled by precipitation and temperature, with an approximately 34-day lag relative to the temperature cycle. The proposed “monitoring–decomposition–interpretation” framework turns InSAR-derived deformation into sustainability indicators that enhance deformation characterization and guide early warning, targeted upkeep, climate-aware drainage, and reclamation. These metrics reduce downtime and resource-intensive repairs and inform integrated risk management in open-pit mining.

The authors studied a working off-grid type photovoltaic (PV) pumping system for irrigation use. The methodology was based on digitalization analysis and numerical modeling as a preliminary stage. A mathematical model of the PV pumping installation considered the determination of the characteristic equations for all its components. These have been used together with the SISIFO simulation software to achieve the performances of the mechanical and electrical components of an advanced PV pumping system. Its global performance features, namely the monthly energy yield, monthly pumping yield, and monthly total performances (energy and flow rate) were introduced. Digital platform (DP) for PV systems characterized by three advanced technologies—machine learning (ML), digital twin (DT) and artificial intelligence (AI) was developed. The simulation results were discussed for a specific case study conducted for a location in the Southern Romania regarding the irrigation potential, taking into account the main meteorological parameters: solar irradiance and ambient temperature, related to the site. The AI approach was implemented to achieve an optimum operation of the PV pumping system by the use of the maximum power point tracking (MPPT) method and the MATLAB/Simulink software. A unified development of all the components of the PV pumping system using the SISIFO simulation software was established by the authors, with major implications in the development of solar PV installations on large-scale.

The paper titled "Data Acquisition System for IoT Frameworks with Monitor and Control Using SAAS Technology" explores the convergence of Internet of Things (IoT) technology with real-time data monitoring, aiming to contribute to the evolving landscape of IoT applications. Building upon foundational principles in the field, the research integrates components such as the ESP32 microcontroller, Arduino Uno and Nano, NRF24L01 wireless modules, and sensors like MQ135. This integration facilitates comprehensive data acquisition, wireless communication, and real-time monitoring, establishing a foundation for user-centric IoT technology. The system addresses challenges in data reliability, noise interference, and user-centric control by incorporating a dedicated mobile application. The paper presents a detailed account of the project's architecture, circuit design, specifications, simulation results, and future scope, offering valuable insights into the potential of versatile and efficient IoT systems.

Photovoltaic pumping systems have become a key solution for sustainable water supply, especially in remote and off-grid areas. Yet, their performance often drops under changing solar conditions. To address this, we introduce Spider-Tailed Horned Viper Optimization (STHVO), a novel nature-inspired MPPT technique specifically designed for such applications. A PV generator, a step-up converter, and a radial-flow pump powered by an induction motor are all part of the suggested configuration. The system was subsequently tested under standard irradiance (1000W/m²) and real-world irradiance variations obtained from the Bni Hadifa region. By achieving 98.92% efficiency, delivering a peak hydraulic power of 72W, and sustaining a steady 0.65L/s flow rate, simulation results demonstrate that STHVO performs better than traditional tactics. It also ensures rapid tracking in less than 0.6s and constant motor speed at 195rad/s. These outcomes show how the method can enhance solar-powered systems' energy reliability and water delivery.

Standard frequency converters (FCs) are evaluated in this work as a universal solution for photovoltaic (PV) water pumping systems. FCs can be connected directly to a PV generator to power centrifugal submersible pumps driven by three‐phase asynchronous induction motors. In comparison with dedicated products, the use of standard FCs and centrifugal motopumps encompasses several advantages: power range, broad range of products and technological independence. This paper describes the main rules to implement standard FCs in PV pumping systems, and presents a model to estimate its performance from standard pumps manufacturers' data. The experimental validation of the model is also presented. Copyright © 2003 John Wiley & Sons, Ltd.

Background: The healthcare sector is experiencing a transformative shift due to advancements in technology, particularly with the Internet of Things (IoT). IoT integration in healthcare is poised to revolutionize patient monitoring and management, particularly for individuals with chronic conditions. The Grand View Research Inc. analysis projected a significant increase in IoT penetration in healthcare, with a market value of approximately $409.9 billion by 2022. Aim: This article aims to explore the applications, benefits, and future potential of IoT devices in real-time health monitoring for patients with chronic conditions. Methods: The review encompasses various IoT-based health monitoring systems, including wearable and implantable devices, biosensors, and remote patient monitoring systems. The methodologies of existing IoT applications, such as the UbiMon project and various ZigBee-based systems, are analyzed to understand their impact on patient care. Results: IoT technologies facilitate real-time monitoring of vital signs, improve chronic disease management, and enhance emergency response systems. Examples include smart inhalers, ECG monitors, and remote surgery systems. The integration of IoT in healthcare has led to improved patient outcomes, reduced emergency waiting times, and better resource management in hospitals. Conclusion: IoT is transforming healthcare by enabling continuous, real-time monitoring of chronic conditions and enhancing overall patient care.

Abstract: An essential component of urban infrastructure is the collection and handling of waste. Public health suffers when there are insufficiently effective and optimized systems. In this study, an eco-friendly, effective, and efficient "smart dustbin system" utilizing cloud computing, mobile applications, and the Internet of Things is proposed. This system uses sensors to give real-time garbage level monitoring and uses a mobile app to send alert messages to truck drivers to ensure timely collection. Cloud computing also makes it possible for real-time analytics, scalable sensor data storage, and easy access to bin and route data from any location, all of which improve system efficiency. The Traveling Salesman Problem (TSP) is used to determine the best course of action, which lowers operating expenses and enhances waste management in general. Keyword: IoT sensors, Cloud computing, Mobile application, Real-time monitoring, Traveling Salesman Problem (TSP)

Aquaculture is the farming of aquatic organisms in natural, controlled marine and freshwater environments. The real-time monitoring of aquatic environmental parameters is very important in fish farming. Internet of things (IoT) can play a vital role in the real-time monitoring. This paper presents an IoT framework for the efficient monitoring and effective control of different aquatic environmental parameters related to the water. The proposed system is implemented as an embedded system using sensors and an Arduino. Different sensors including pH, temperature, and turbidity, ultrasonic are placed in cultivating pond water and each of them is connected to a common microcontroller board built on an Arduino Uno. The sensors read the data from the water and store it as a comma-separated values (CSV) file in an IoT cloud named ThingSpeak through the Arduino microcontroller. To validate the experiment, we collected data from 5 ponds of various sizes and environments. After experimental evaluation, it was observed among 5 ponds, only three ponds were perfect for fish farming, where these 3 ponds only satisfied the standard reference values of pH (6.5-8.5), temperature (16-24 °C), turbidity (below 10 ntu), conductivity (970-1825 μS/cm), and depth (1-4) meter. At the end of this paper, a complete hardware implementation of this proposed IoT framework for a real-time aquatic environment monitoring system is presented.

This paper reviews the emerging and critical role of Artificial Intelligence (AI) and the Internet of Things (IoT) in transforming modern agriculture into a more sustainable and efficient practice. Faced with escalating global population demands and a myriad of challenges such as climate change, labor costs, and market fluctuations, the agricultural sector is at a pivotal juncture. The integration of AI and IoT offers a beacon of hope, enabling smarter farming methods through real-time monitoring, data analysis, and management of agricultural processes. This review delves into the application of these technologies in various facets of farming, including precision agriculture, crop yield forecasting, and the management of resources. It explores how AI's analytical prowess can predict and address crop diseases, automate farming operations, and assist in decision-making, while IoT's network of sensors and devices facilitates the real-time tracking and monitoring of farm conditions. Additionally, the synergy of these technologies with blockchain and cloud computing is examined, highlighting their potential in enhancing transparency, data security, and supply chain management. Collectively, these technological innovations are not just reshaping the agricultural landscape but are instrumental in steering it towards a more sustainable, productive, and resilient future.

The agricultural industry is experiencing a profound transformation with the integration of Internet of Things (IoT) technology.This study presents the development and evaluation of a web-enabled microcontroller embedded system integrated with IoT technology for real-time monitoring and control of microclimate parameters, viz.temperature, relative humidity (RH), and light intensity inside a naturally ventilated greenhouse (polyhouse).The new IoT-based automated microclimate monitoring and controlling system comprised of microcontroller (Arduino Nano 33 IoT), temperature and humidity sensor (DHT22), light sensor (BH1750), and actuators (exhaust fans and foggers).It was designed to optimize the environmental conditions, critical for crop cultivation inside a polyhouse.It enabled remote monitoring and control via an IoT platform and Global System for Mobile Communication (GSM) module anywhere in the world.The IoT-based automation system was effective in maintaining an average temperature, relative humidity (RH), and light intensity of 34C, 64.33%, and 32,000 lx, respectively, during the test period, resulting in an average temperature reduction of 3C and an increase in RH of 4%.The data observed from inside and outside the polyhouse demonstrated the capability of the IoT-based automation system's real-time monitoring and controlling of optimum microclimate parameters inside the polyhouse.

As a critical branch of the Internet of Things (IoT) in the medicine industry, the Internet of Medical Things (IoMT) significantly improves the quality of healthcare due to its real-time monitoring and low medical cost. Benefiting from edge and cloud computing, IoMT is provided with more computing and storage resources near the terminal to meet the low-delay requirements of computation-intensive services. However, the service offloading from health monitoring units (HMUs) to edge servers generates additional energy consumption. Fortunately, artificial intelligence (AI), which has developed rapidly in recent years, has proved effective in some resource allocation applications. Taking both energy consumption and delay into account, we propose an energy-aware service offloading algorithm under an end-edge-cloud collaborative IoMT system with Asynchronous Advantage Actor-critic (A3C), named ECAC. Technically, ECAC uses the structural similarity between the natural distributed IoMT system and A3C, whose parameters are asynchronously updated. Besides, due to the typical delay-sensitivity mechanism and time-energy correction, ECAC can adjust dynamically to the diverse service types and system requirements. Finally, the effectiveness of ECAC for IoMT is proved on real data.