Outlook of carbon capture technology and challenges

The greenhouse gases emissions produced by industry and power plants are the cause of climate change. An effective approach for limiting the impact of such emissions is adopting modern Carbon Capture and Storage (CCS) technology that can capture more than 90% of carbon dioxide (CO2) generated from power plants. This paper presents an evaluation of state-of-the-art technologies used in the capturing CO2. The main capturing strategies including post-combustion, pre-combustion, and oxy – combustion are reviewed and compared. Various challenges associated with storing and transporting the CO2 from one location to the other are also presented. Furthermore, recent advancements of CCS technology are discussed to highlight the latest progress made by the research community in developing affordable carbon capture and storage systems. Finally, the future prospects and sustainability aspects of CCS technology as well as policies developed by different countries concerning such technology are presented

Prospects and challenges of concentrated solar photovoltaics and enhanced geothermal energy technologies

Reducing the total emissions of energy generation systems is a pragmatic approach for limiting the environmental pollution and associated climate change problems. Socio economic activities in the 21st century is highly determined by the energy generation mediums, particularly the renewable resources, across the world. Therefore, a thorough investigation into the technologies used in harnessing these energy generation mediums should contribute to their further advancement. Concentrated Solar Photovoltaics (CSP) and Enhanced Geothermal Energy (EGE) are considered as emerging renewable energy technologies with high potential to be used as suitable replacements for fossil products (petroleum, coal, natural gas etc.). Despite the accelerated developments in these technologies, they are still facing many challenges in terms of cost. This review paper presents a detailed background about these renewable energy technologies and their main types such as solar tower, parabolic trough, and so on. Also, the principle challenges impeding the advancement of these energy technologies into commercialisation are discussed. Possible solutions for the main challenges are presented and the future prospects for such energy generation mediums are reported

Computer-Aided Training for Quranic Recitation

AbstractComputer Aided Language Learning (CALL) systems have gained popularity due to the flexibility they provide in empowering students to practice their language skills at their own pace. Detection/Correction of specific pronunciation error is an important component of an effective language learning system. Learning the correct rules of the Holy Quran recitation is important to every Muslim. In this work, we developed a Computer Aided Quranic Recitation Training system to detect errors in continuous recitation of Holy Quran and increase the accuracy of the error detection. We have integrated Automatic Speech Recognition (ASR) and classifier-based approach to detect recitation errors. Error detection is done in two successive stages: first, an HMM-based ASR recognizes the recitation, detects the insertion, deletion and substitution of phones and provides phonetic time alignments, and then classifier based approach is used to distinguish between confusing phones to achieve improved detection rate. In this implementation we implemented only two classifiers, one to discriminate between emphasized and non-emphasized utterances of the letter “R” in Arabic, and the other to distinguish between closely related, often confused letter pronunciations. The results show, that the system has achieved a 91.2% word-level accuracy

Overview of ocean power technology

This work discusses and provides a critical expose of some of the newly emerging renewable energy technologies with special concentration on marine energy generation. The work shows that there are several promising new developments in harvesting marine energy and it examines some of these technologies and discusses their advantages and some of the obstacles that are impeding the commercialization of these emerging technologies. This includes wave energy harvesting, tidal energy harvesting, ocean thermal energy and the utilisation of salinity gradients for electricity generation. The work emphasises the fact that these new emerging technologies are currently at the developing stages and has a long way to go before successful commercialization and wide adoption become the norm. The work stresses the need for more research and developmental work to address several of the technical issues that need to be addressed including devices designs, their installation and maintenance, the infrastructure which includes the grid and power transmission as well as losses, their use in arrays, and their longevity. This work underlines the lack of reliable studies on the long term impacts of these technologies on both the marine environment and nearby habitations and highlights the need for proper environmental and social impact assessments of these technologies. The work concludes that combination of technical, policy and economic advances will enable marine energy technologies to play a large role in combination with currently adopted non-pollution renewable energy resources to provide the world population with their energy needs and contribute to affecting significant reduction in the use of non-renewable and other polluting fuels worldwide

Artificial neural network driven prognosis and estimation of Lithium-Ion battery states: Current insights and future perspectives

Lithium-ion batteries currently represent the dominant energy storage technology due to their superior efficiency and widespread compatibility, especially in Electric Vehicles (EVs). Normally, a Battery Management System (BMS) is used to monitor and optimize the states of these batteries in order to maintain efficient and safe operating performance. However, estimating the state of Li-ion batteries is not a straightforward process. Accordingly, there has been extensive interest in the use of Artificial Intelligence (AI) methods for this purpose.This work is a comprehensive review of Artificial Neural Network (ANN) use in the estimation of Li-ion battery states, including state of charge, state of health, remaining useful life, thermal state and other parameters. The estimation accuracy and robustness are analyzed based on error evaluation metrics alongside study remarks. It was found that feed forward neural networks were the most utilized for estimating Li-ion battery states. Moreover, convolutional neural networks have also shown good estimation performance in number of studies and illustrate huge potential. Finally, this work presents future recommendations to expand the research scope as well as maximize the applicability of ANNs as computational tools for battery technologies