Fuels

Laboratory-based research on microbial fuel cells (MFCs) is often costly and limited to a small number of variables, making optimization challenging. However, machine learning (ML) offers a promising solution by enabling efficient multivariate principal component analysis (PCA) and multivariable optimization. These techniques can provide significant insights and optimization opportunities. The goal of this study is to propose an ML-based approach to explore the relationships between bioelectricity generation (in terms of voltage, power density (PD), current density (CD), and coulombic efficiency (CE)) and two key variables, chemical oxygen demand (COD) and pH, as well as to recommend their optimal combinations. Specifically, the objectives are to (1) integrate a laboratory-based MFC study with multivariate data analyses; (2) apply PCA to reduce data complexity by focusing on the principal components that account for the greatest variance, thus improving interpretability; and (3) identify the optimal combinations of COD and pH for maximizing bioelectricity generation. The PCA results demonstrated that COD positively influenced the generated voltage while having an inverse effect on CE. Additionally, both PD and CD increased with higher pH values. The optimal combination of COD and pH improved CD, PD, and CE; however, their optimal combination for generated voltage differed, with higher COD leading to higher voltage. The optimal predicted voltage, CD, PD, and CE of the study were 795.71 (mV), 1451.80 (mA/m²), 57.46 (mW/m²), and 4.85%, respectively. By integrating ML approaches, this study contributed to the optimization of bioelectricity generation from wastewater and offered valuable insights for researchers working in this field.

The aviation industry, responsible for approximately 2.5–3.5% of global greenhouse gas emissions, faces increasing pressure to adopt sustainable energy solutions. Hydrogen, with its high gravimetric energy density and zero carbon emissions during use, has emerged as a promising alternative fuel to support aviation decarbonization. However, its large-scale implementation remains hindered by cryogenic storage requirements, safety risks, infrastructure adaptation, and economic constraints. This study aims to identify and evaluate the primary technical and operational risks associated with hydrogen utilization in aviation through a comprehensive Monte Carlo Simulation-based risk assessment. The analysis specifically focuses on four key domains—hydrogen leakage, cryogenic storage, explosion hazards, and infrastructure challenges—while excluding economic and lifecycle aspects to maintain a technical scope only. A 10,000-iteration simulation was conducted to quantify the probability and impact of each risk factor. Results indicate that hydrogen leakage and explosion hazards represent the most critical risks, with mean risk scores exceeding 20 on a 25-point scale, whereas investment costs and technical expertise were ranked as comparatively low-level risks. Based on these findings, strategic mitigation measures—including real-time leak detection systems, composite cryotank technologies, and standardized safety protocols—are proposed to enhance system reliability and support the safe integration of hydrogen-powered aviation. This study contributes to a data-driven understanding of hydrogen-related risks and provides a technological roadmap for advancing carbon-neutral air transport.

Succinimide additives play an important role in combating engine deposits and are therefore commonly blended in fuels. As many of the methods currently used to quantify them in fuel rely on time-consuming techniques and the use of expensive laboratory equipment, a more practical approach was explored. For this purpose, an existing method for aqueous samples involving a colour reaction with Rose Bengal dye and spectrophotometric detection in the UV/Vis range was modified for usage in the nonpolar fuel matrix and tested for applicability. The result was an accessible method for determining the succinimide additive content of diesel fuel—including biodiesel—that is easy to implement in the laboratory routine.