Future Technology

Design and thermodynamic analysis of a hybrid gas turbine-steam methane reforming system

Minh Hoang Tran — 2024-12-22

This study proposed and evaluated the new hydrogen production design of the Gas Turbine - Steam Methane Reforming (GT-SMR) hybrid process. The superheated byproduct gas from the GT system is utilized as a combustion agent for the boiler of the SMR system, minimizing the required heat energy input for hydrogen production. The overall energy consumption and energy generation are calculated and simulated to determine the system's operational performance. Under no efficiency losses, the design is tested to understand how the significant input parameters such as temperature, pressure, and steam/methane ratio (S/C) affect the system's overall performance. From the data generated, the system's efficiency was directly proportional to the pressure and temperature while inversely proportional to the S/C values. However, in actual applications, the menthane conversion rate often fluctuated depending on the adjustments of these factors, regardless of their thermodynamic relationship with the SMR efficiency. With the addition of other energy waste information, a complete simulation showed the reversed effect of pressure. Although the temperature and S/C ratio improved the overall performance, the hybrid system efficiency reached its limits beyond certain values.

State-of-the-art techniques and algorithms for swift and precise fault detection and protection in transmission lines

Siphesihle Sibonelo Xulu — 2025-01-19

Transmission lines are crucial for power systems, enabling bulk power transfer from generation sites to load centers. They face challenges such as faults, losses, and delays, necessitating effective management and maintenance strategies. The aim of this paper is to conduct a systematic literature review focusing on techniques and algorithms for swift and precise fault detection and protection in transmission lines. The methodology included a collection of relevant papers, a filtering process, eligibility identification, synthesizing, and trend analysis. This process was facilitated using the Scopus database and VOSviewer software. Results of this survey revealed some key noticeable aspects (among others) across the studies, which included the utilization of diverse signal processing and machine learning techniques to analyze voltage and current signals for identifying faults. This work will contribute by reviewing recent advances in signal processing, analyzing methods to enhance fault detection speed and accuracy, exploring the use of machine learning and neural networks in fault detection models, investigating advanced relay technologies and protection schemes, evaluating statistical techniques for fault isolation, and examines indexing techniques and evolutionary programming tools for precise fault identification, while also proposing future research directions.

An innovative control of the charging and discharging for the battery management operation using a bidirectional converter

Mohammad Junaid Khan — 2025-01-21

This research article explores the control strategies for managing the battery charging and discharging operations using a bidirectional converter. Bidirectional converters offer flexibility and allow batteries to receive and deliver power. Battery systems are an important part of electrical vehicles (EVs), and they can be charged by renewable energy integration and the public grid. The battery bank manages the DC (Direct Current) link and fulfills the load requirement for charging. The main aim of this article, the proportional integral derivative (PID) controller, is to design and control the duty cycle of the bidirectional converter to ensure efficient and safe battery operation. In this regard, the current of battery banks is regulated during the charging and discharging phases. To ensure optimum charging and discharging control, further work can be implemented using artificial intelligence (AI) based technology in the battery management system (BMS) operation.

Experimental investigation of a cooling and cleaning system for enhanced photovoltaic panel performance

Roua Toujani — 2025-01-22

This paper presents the design and development of a cooling and cleaning system for photovoltaic (PV) installations. The key contribution of this work is the use of an Arduino-controlled closed-loop water circulation system to clean and cool the PV panels. The proposed solution incorporates sensors to measure the temperature, current, and voltage of the PV panels, as well as the water level in the tank. These measurements are used to control the pump's operation, regulating the flow of water across the PV panels. The study aims to improve the performance of PV systems by addressing two main challenges: the heating of the panels and the accumulation of dust. In regions like the Sahara, dust and sand reduce the efficiency of PV panels, necessitating regular cleaning. To address these issues, a cooling and cleaning prototype was developed. Tests and temperature measurements were performed under identical weather conditions (i.e., the same ambient temperature and solar radiation), demonstrating the effective operation of the proposed system and an improvement in PV efficiency. The automated cooling and cleaning system successfully reduced the panel temperature and boosted the output power to 287.81 W under radiation of 761.13 W/m², resulting in an efficiency of 19.15%. In contrast, under the same radiation, the uncooled panels produced only 283.48 W, yielding an efficiency of 18.86%, a 4.33 W difference.

AI-enabled geospatial solutions for waste collection and forecasting for smart cities application: insights from Kathmandu municipality

Krishna Bikram Shah — 2025-02-07

Effective waste management is a critical global concern, especially in urban areas where efficient systems are essential for reducing litter, minimizing environmental contamination, and enhancing urban aesthetics. This study presents a comprehensive framework for optimizing waste management in Kathmandu Municipality, focusing on the spatial allocation of collection points, bin requirements, and predictive waste level modeling. This approach is based on the primary parameters of the rate at which waste is generated, the capacity of a bin, the density of waste, and the frequency of collection. The model also accommodates waste segregation-this means effective bin deployment across categories of waste to avoid wasting resources. It includes a time-series forecasting model, simulating waste accumulation for 7 days with seasonality influences, holiday influence, fluctuation of the population, and socio-economic influence. Trend of generation waste is reported at an interval of 6 hours in order to enhance precision within the schedule of collecting wastes. Lower risk of overflow of the bins due to the services before bins overflow. This holistic framework shall consequently provide data-driven scalable solutions to Kathmandu Municipality in optimizing its routes for collecting wastes, enhancing resource efficiency, and adapting the patterns of producing wastes on a real-time basis.