Biodiesel, an environmentally degradable and renewable biofuel derived from organic matter, has exhibited its capacity as a viable and sustainable substitute for traditional diesel fuel. Numerous comprehensive investigations have been conducted to assess the effects of biodiesel on internal combustion engines (ICEs), with particular emphasis on diesel engine performance metrics, combustion dynamics, and emission profiles. Biodiesel demonstrates a significant decrease in emissions of particulate matter (PM), hydrocarbon (HC), and carbon monoxide (CO) in diesel engines. The addition of biodiesel has shown a minor decrease in power output and a slight increase in fuel consumption and nitrogen oxide (NOx) emissions. Nevertheless, the extensive implementation of biodiesel, despite its potential to effectively reduce detrimental emissions, has encountered obstacles stemming from external influences including restricted availability of feedstock, volatile petroleum oil prices, and inadequate governmental backing. This review presents a concise summary of significant advancements in the global adoption of biodiesel from a sustainability perspective. This review provides valuable insights into the challenges and opportunities associated with the advancement of sustainable biofuel technologies by synthesizing the current state of palm biodiesel and examining global trends in biodiesel implementation. The wider adoption of biodiesel can be facilitated by addressing concerns pertaining to feedstock availability, price stability, and policy support. This would allow for the realization of significant environmental advantages and contribute to a more environmentally friendly and sustainable biofuel.

The rapid development of digitalization, the Internet of Things (IoT), and Industry 4.0 has led to the emergence of the digital twin concept. IoT is an important pillar of the digital twin. The digital twin serves as a crucial link, merging the physical and digital territories of Industry 4.0. Digital twins are beneficial to numerous industries, providing the capability to perform advanced analytics, create detailed simulations, and facilitate informed decision-making that IoT supports. This paper presents a review of the literature on digital twins, discussing its concepts, definitions, frameworks, application methods, and challenges. The review spans various domains, including manufacturing, energy, agriculture, maintenance, construction, transportation, and smart cities in Industry 4.0. The present study suggests that the terminology “3 dimensional (3D) digital twin” is a more fitting descriptor for digital twin technology assisted by IoT. The aforementioned statement serves as the central argument of the study. This article advocates for a shift in terminology, replacing “digital twin” with “3D digital twin” to more accurately depict the technology’s innate potential and capabilities in Industry 4.0. We aim to establish that “3D digital twin” offers a more precise and holistic representation of the technology. By doing so, we underline the digital twin’s analytical ability and capacity to offer an intuitive understanding of systems, which can significantly streamline decision-making processes using the digital twin.

Electric vehicles (EVs) have seen significant growth due to the increasing awareness about environmental concerns and the negative impacts of internal combustion engine vehicles (ICEVs). The electric vehicle landscape is rapidly evolving, with EV policies, battery, and charging infrastructure and electric vehicle-to-everything (V2X) at its forefront. This review study used a bibliometric analysis of the Scopus database to investigate the development of EV technology. This bibliometric study specifically focuses on analyzing electric vehicle trends, policy implications, lithium-ion batteries, EV battery management systems, charging infrastructure, EV smart charging technologies, and V2X. Through this detailed bibliometric analysis discussion, we aim to provide a better understanding of holistic EV technology and inspire further research in electric vehicles. The analysis covers the period from 1990 to 2022. This bibliometric analysis underscores the interplay of electric vehicle policies, technology, and infrastructure, specifically focusing on developments in battery management and the possibility of V2X technology. In addition, this bibliometric analysis suggests the synchronization of international electric vehicle policy, advancement of battery technology, and promotion of the use of EV smart charging and V2X systems. This bibliometric analysis emphasizes that the expansion of EVs and sustainable mobility relies on a comprehensive strategy that encompasses policy, technology, and infrastructure. This bibliometric analysis recommends fostering collaboration between different sectors to drive innovation and advancements in electric vehicle technology.

Activated carbons can be produced from different precursors, including coals of different ranks, and lignocellulosic materials, by physical or chemical activation processes. The objective of this paper is to characterize oil‐palm shells, as a biomass byproduct from palm‐oil mills which were converted into activated carbons by nitrogen pyrolysis followed by CO₂ activation. The effects of no holding peak pyrolysis temperature on the physical characteristics of the activated carbons are studied. The BET surface area of the activated carbon is investigated using N₂ adsorption at 77 K with selected temperatures of 500, 600, and 700°C. These pyrolysis conditions for preparing the activated carbons are found to yield higher BET surface area at a pyrolysis temperature of 700°C compared to selected commercial activated carbon. The activated carbons thus result in well‐developed porosities and predominantly microporosities. By using this activation method, significant improvement can be obtained in the surface characteristics of the activated carbons. Thus this study shows that the preparation time can be shortened while better results of activated carbon can be produced.

The adsorption of remazol brilliant blue R (RBBR) dye on pinang frond based activated carbon (PF-AC) was investigated in a batch process. The effects of initial dye concentration, contact time, solution temperature, and solution pH were evaluated. The adsorption equilibrium and kinetic were found to follow Freundlich isotherm models and pseudo-second-order kinetic model, respectively. The mechanism of the adsorption process was found from the intraparticle diffusion model. Result from adsorption thermodynamic show that interaction for RBBR dye was found to be feasible, nonspontaneous, and endothermic. The results indicated that the PF-AC is very effective for the RBBR adsorption from aqueous solution.

The Industry 5.0 revolution has launched a new age of intelligent manufacturing equipment, which is crucial to our society and economy. Industry 5.0 aims to enhance human potential by integrating cutting-edge IT technology, Artificial Intelligence (AI), the Internet of Things (IoT), robots, and augmented reality into everyday living, especially in smart industrial settings, to maximize human potential. Smart Production Processes (SP2), accessible standards, and resource sharing on the web have increased network vulnerabilities as enterprises adopt them. These vulnerabilities primarily target industry IoT systems, threatening private data. Existing system issues include network vulnerabilities, production capability, and data management. Traditional applications struggle with industrial data’s bulk and complexity, causing data analysis, security, and privacy challenges. Modern industrial systems use AI and ML for big data analysis and data processing to overcome these issues. Optimizing human-machine synergy minimizes costs and equipment maintenance and boosts efficiency. An ML-assisted Smart Production Process (ML-SP2) and Intrusion Detection System (ML-IDS) have been proposed to assess and address Industrial 5.0 concerns. Data capture, predictive maintenance and optimization, transparent decision-making, and proactive maintenance are integrated into the manufacturing process with the ML-SP2. The ML-IDS detects network vulnerabilities using ensemble methods and manages distribution efficiently. The ML-IDS uses the variety of ML techniques such as random forest, decision tree and support vector machines that identifies the intruder with maximum prediction accuracy. In addition, the intruder activities are observed with the help of the convolution networks that improve the overall intruder activities recognition. The smart industrial production phase’s effectiveness is measured by the RPL (0.9), maintenance cost (2500$), production performance (1.0), VDR (97.6%), FAR, accuracy (99%), precision (94%)-recall (96%), and f1-measure (98%) compared to the existing methods such as ML-RF-FLID, NRDD-DBSCAN, and OC-SVM.