Cost Analysis of Adsorption based Air Capture of CO2

Carbon dioxide (CO2) capture and conversion to value added products, such as chemicals, polymers, and carbon-based fuels represents a promising approach to transform a potential threat to the environment into a value-added product for long term sustainability. Emerging Carbon Capture Technologies: Towards a Sustainable Future provides a multidisciplinary view of the research that is being carried out in this field, covering materials and processes for CO2 capture and utilization and including a broad discussion of the impact of novel technologies in carbon capture on the energy landscape, society and climate. Of interest to students, researchers and professionals in industries related to greenhouse gas mitigation, post-combustion CO2 capture processes, coal-fired power plants, environmental sustainability, green solvents, green technologies, and the utilization of clean energy for environmental protection, this book covers both the experimental and theoretical aspects of novel materials and process development providing a holistic approach toward a sustainable energy future. Cost Analysis of adsorption based direct air capture of CO2 using porous sorbents, especially porous polymer networks (PPNs) provide a thorough, non-specialist introduction to technologies aimed at reducing greenhouse gas emissions from burning fossil fuels during power generation and other energy-intensive industrial processes, such as steelmaking. Extensively revised and updated, this second edition provides detailed coverage of key carbon dioxide capture methods along with an examination of the most promising techniques for carbon storage. The book opens with an introductory section that provides background regarding the need to reduce greenhouse gas emissions, an overview of carbon capture and storage (CCS) technologies, and a primer in the fundamentals of power generation. Chapters focus on key carbon capture technologies, including absorption, adsorption, and membrane-based systems, addressing their applications in both the power and non-power sectors

Experimental investigation of engine valve train friction considering effects of operating conditions and WPC surface treatment

Reduction in friction ensures fuel economy, control on emissions and durability of components in internal combustion engines. A modern gasoline internal combustion engine was instrumented to determine the friction values at the cam–roller interface considering the effects of surface treatment and engine operating state. A series of tests under different operating speeds and lubricant inlet temperatures were undertaken using both an original surface roller and a Wonder Process Craft (WPC) surface-treated engine roller. The results clearly revealed a substantial reduction in friction magnitude for the WPC surface-treated engine roller in comparison to the original roller while operating under similar conditions, indicating their strong potential for employment in engines. An increase in friction with the rise in temperature was also observed for both types of rollers, whereas increased lubricant entraining velocity due to higher operating speed had the opposite impact. A considerable reduction in frictional drive torque ranging from 8% to 28% was observed by employing the WPC-treated roller in comparison to original/untreated roller at various operating conditions, which signifies the strong potential for employment of WPC surface treatment in the roller/follower valve train engines

A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation (4G) and 5th generation (5G) etc. Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way. Instead of the classical random walk, this paper proposes the quantum walk to achieve high image security. Classical random walk exhibits randomness due to the stochastic transitions between states, on the other hand, the quantum walk is more random and achieve randomness due to the superposition, and the interference of the wave functions. The proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient, entropy, histogram, time complexity, number of pixels change rate and unified average intensity etc. All experimental results validate the proposed scheme, and it is concluded that the proposed scheme is highly secured, lightweight and computationally efficient. In the proposed scheme, the values of the correlation coefficient, entropy, mean square error (MSE), number of pixels change rate (NPCR), unified average change intensity (UACI) and contrast are 0.0069, 7.9970, 40.39, 99.60%, 33.47 and 10.4542 respectively

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework

Network intrusion detection leveraging machine learning and feature selection

Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attack perception stage, when wrestling with huge-scale and heterogeneous data. In this paper, for dimensionality reduction of data, we use Correlation-based Feature Selection (CFS) and Naïve Bayes (NB) classifier techniques. The proposed Intrusion Detection System (IDS) classifies attacks using a Multilayer Perceptron (MLP) and Instance-Based Learning algorithm (IBK). The accuracy of the introduced IDS is 99.87% and 99.82% with only 5 and 3 features out of 78 features for IBK. Other metrics such as precision, Recall, F-measure, and Receiver Operating Curve (ROC) also confirm the principal performance of IBK compared to MLP

Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles

The highly efficient Interior Permanent Magnet Synchronous Motor (IPMSM) is ubiquitous choice in Electric Vehicles (EVs) for today’s automotive industry. IPMSM control requires accurate knowledge of an immeasurable critical Permanent Magnet (PM) flux linkage parameter. The PM flux linkage is highly influenced by operating temperature which results in torque derating and hence power loss, unable to meet road loads and reduced life span of electrified powertrain in EVs. In this paper, novel virtual sensing scheme for estimating PM flux linkage through measured stator currents is designed for an IPMSM centric electrified powertrain. The proposed design is based on a Uniform Robust Exact Differentiator (URED) centric Super Twisting Algorithm (STA), which ensures robustness and finite-time convergence of the time derivative of the quadrature axis stator current of IPMSM. Moreover, URED is able to eliminate chattering without sacrificing robustness and precision. The proposed design detects variation in PM flux linkage due to change in operating temperature and hence is also able to establish characteristics of fault detection. The effectiveness and accuracy in different operating environments of the proposed scheme for nonlinear mathematical IPMSM model with complex EV dynamics are verified thorough extensive simulation experiments using MATLAB/Simulink

Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles

The highly efficient Interior Permanent Magnet Synchronous Motor (IPMSM) is ubiquitous choice in Electric Vehicles (EVs) for today’s automotive industry. IPMSM control requires accurate knowledge of an immeasurable critical Permanent Magnet (PM) flux linkage parameter. The PM flux linkage is highly influenced by operating temperature which results in torque derating and hence power loss, unable to meet road loads and reduced life span of electrified powertrain in EVs. In this paper, novel virtual sensing scheme for estimating PM flux linkage through measured stator currents is designed for an IPMSM centric electrified powertrain. The proposed design is based on a Uniform Robust Exact Differentiator (URED) centric Super Twisting Algorithm (STA), which ensures robustness and finite-time convergence of the time derivative of the quadrature axis stator current of IPMSM. Moreover, URED is able to eliminate chattering without sacrificing robustness and precision. The proposed design detects variation in PM flux linkage due to change in operating temperature and hence is also able to establish characteristics of fault detection. The effectiveness and accuracy in different operating environments of the proposed scheme for nonlinear mathematical IPMSM model with complex EV dynamics are verified thorough extensive simulation experiments using MATLAB/Simulink

Anaerobic digestion of sewage sludge for biogas & biohydrogen production: State-of-the-art trends and prospects

Advancement in the anaerobic digestion of sewage sludge for biogas production is constantly required for effective commercialization. There is great interest in exploring the scientometric and technical assessment in biogas production using sewage sludge from various sources in the anaerobic digestion process. Therefore, this work provides a bibliometric and technical study of anaerobic digestion for biogas production from sewage sludge in the last ten years, based on Web of Science data. The vital parameters of the bibliometric study are authors, countries, and institutional collaboration along with the authors' keywords. In terms of technical assessment, challenges are highlighted with critical analysis of previous work performed both on a laboratory scale and on a pilot scale. The study recommends key considerations and new combination techniques to enhance biohydrogen production with optimized process conditions which will provide vital economic impact for technological adaptation and commercialization.Web of Science329art. no. 12541