Muhammad Kamran Khan, Muhammad Imran Khan, M. Rehan
Developing countries are facing the problem of environmental degradation. Environmental degradation is caused by the use of non-renewable energy consumptions for economic growth but the consequences of environmental degradation cannot be ignored. This primary purpose of this study is to investigate the nexus between energy consumption, economic growth and CO2 emission in Pakistan by using annual time series data from 1965 to 2015. The estimated results of ARDL indicate that energy consumption and economic growth increase the CO2 emissions in Pakistan both in short run and long run. Based on the estimated results it is recommended that policy maker in Pakistan should adopt and promote such renewable energy sources that will help to meet the increased demand for energy by replacing old traditional energy sources such as coal, gas, and oil. Renewable energy sources are reusable that can reduce the CO2 emissions and also ensure sustainable economic development of Pakistan.
Abstract A brief overview is presented involving the terms of availability of hydrogen, its properties and possible sources and its production methods, and finally, its relationship with renewable energy utilisation, environment and climate. Solar hydrogen, preferably obtained from water, is confirmed once more to be the most environment and climate compatible (causing the least damage), energy source; though not necessarily the most economic one. Production cost of hydrogen obtained from terrestrial biomass, is not the lowest either, however carbon-neutral feature of terrestrial biomass renders it highly desirable in view of steep rise in global temperature.
Hydrogen is an important part of any discussion on sustainability and reduction in emissions across major energy sectors. In addition to being a feedstock and process gas for many industrial processes, hydrogen is emerging as a fuel alternative for transportation applications. Renewable sources of hydrogen are therefore required to increase in capacity. Low-temperature electrolysis of water is currently the most mature method for carbon-free hydrogen generation and is reaching relevant scales to impact the energy landscape. However, costs still need to be reduced to be economical with traditional hydrogen sources. Operating cost reductions are enabled by the recent availability of low-cost sources of renewable energy, and the potential exists for a large reduction in capital cost withmaterial and manufacturing optimization. This article focuses on the current status and development needs by component for the low-temperature electrolysis options.
Souraya Goumri‐Said, Ghouti Abdellaoui, Mohammed Benali Kanoun
A comprehensive first‐principles and machine learning study is conducted on 102 halide double perovskites to identify promising candidates for thermoelectric applications. The HSE06 hybrid functional within the Quantum ATK framework is used to accurately determine electronic structures, bandgaps, and total and partial densities of states. Boltzmann transport theory is applied to figure out important thermoelectric parameters, such as the Seebeck coefficient, electrical conductivity, and ZT values over a wide range of temperatures. Supervised machine learning models are trained on density functional theory (DFT)‐derived descriptors to speed up the discovery of new materials. These models demonstrate high predictive accuracy for thermoelectric performance across different chemical spaces. A detailed analysis of the electronic band structures and orbital contributions is carried out for Rb2GeI6, Rb2PbI6, Cs2SnBr6, and In2PtCl6, some of the best‐performing compounds. A wide range of behaviors is observed, including metallic, degenerate, and wide‐bandgap semiconducting, which correlate with distinct transport properties. This unified method shows how using accurate DFT, transport theory, and machine learning together can help find new materials with specific functions. This will lead to the development of next‐generation thermoelectric technologies based on environmentally friendly halide perovskites.
Environmental technology. Sanitary engineering, Renewable energy sources
I. Vervlimmeren, I. Vervlimmeren, I. Vervlimmeren
et al.
<p>Offshore wind turbines are crucial for sustainable energy production but face significant challenges in operational reliability and maintenance costs. In particular, the scalability and practicality of failure detection systems are a key challenge in large-scale wind farms. This paper presents a scalable, comprehensive approach to failure prediction based on the normal behavior modeling (NBM) framework that integrates three components: a cloud-based pipeline, an undercomplete autoencoder for temperature-based anomaly detection, and a time-aware anomaly filtering method. The pipeline enables dynamic scaling and streamlined deployment across multiple wind farms. The autoencoder was trained exclusively on healthy 10 min SCADA data and produces detailed anomaly scores that serve as the input for our filtering technique. It was trained on 4 years of data from a large offshore wind farm in the Dutch–Belgian zone and achieved unhealthy–healthy (UHH) ratios of up to 1.69 and 1.21 for the generator and gearbox models, respectively. The filtering method refines the raw anomaly scores by comparing turbine signals to a windowed fleet median. By aggregating scores via sliding windows and employing robust distance metrics, the method reduces the volume of anomaly scores by up to 65 % without sacrificing predictive accuracy. This selective filtering effectively minimizes noise and non-relevant anomalies, enhancing the efficiency of maintenance analysis.</p>