For a few decades now fast depleting fossil fuels has been a major challenge. Fast expanding population and increased rate of urbanization has increased energy demand. This makes the current scenario worse. Fossil fuels' emissions are another challenge. Apart from fossil fuel emissions, the untreated disposal of waste cooking oil presents another environment’s sustainability challenge. The treatment of waste cooking oil as fuel presents a tangible solution to challenge. In this research article, impact of the engine speed and the concentration of titanium dioxide (TiO₂) nanoparticles (NPs) in diesel-biodiesel blended fuels on the engine’s performance. The emission characteristics of a single-cylinder four-stroke diesel engine has also been examined. TiO₂ NPs were produced by a sol-gel methodology. The diesel-biodiesel combination was fortified with TiO₂ NPs at 40, 80 and 120 ppm. These mixtures were used to power the diesel engine, which was then run at 1150, 1400, 1650, 1900 and 2150 RPM. Interaction between engine speeds and nanoparticle concentrations and investigation of their combined effect on engine performance and emissions was done using response surface methodology. The minimum BSFC of 0.33994 kg/kWh and maximum BTE of 25.90% were found for B30 + 120 ppm biodiesel blend at 2150 rpm as compared to all other tested fuels. The emissions including CO and HC emissions were recorded as 25.61486 kg/kWh and 0.05289kg/kWh respectively at 2150 rpm for B30 + 120 ppm biodiesel blend while NO_x on the contrary side exhibits a slight escalation with increasing engine speed and nanoparticles concentration. The findings of the experiments demonstrated that adding TiO₂ nanoparticles to diesel–biodiesel blends is an effective way to enhance the performance of diesel engines while simultaneously reducing the emissions. It was also discovered that the mathematical model that was built can efficiently estimate the performance of the engine and the emission levels.

In this study, the non-edible Chinaberry Seed Oil (CBO) is converted into biodiesel using microwave assisted transesterification. The objective of this effort is to maximize the biodiesel yield by optimizing the operating parameters, such as catalyst concentration, methanol-oil ratio, reaction speed, and reaction time. The designed setup provides a controlled and effective approach for turning CBO into biodiesel, resulting in encouraging yields and reduced reaction times. The experimental findings reveal the optimal parameters for the highest biodiesel yield (95 %) are a catalyst concentration of 1.5 w/w, a methanol-oil ratio of 6:1 v/v, a reaction speed of 400 RPM, and a reaction period of 3 min. The interaction of the several operating parameters on biodiesel yield has been investigated using two methodologies: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM provides better modeling of parameter interaction, while ANN exhibits lower comparative error when predicting biodiesel yield based on the reaction parameters. The percentage improvement in prediction of biodiesel yield by ANN is found to be 12 % as compared to RSM. This study emphasizes the merits of both the approaches for biodiesel yield optimization. Furthermore, the scaling up this microwave-assisted transesterification system for industrial biodiesel production has been proposes with focus on its economic viability and environmental effects.

Industry 5.0 has the capacity to surpass the technology -oriented efficiency of Industry 4.0 and advance sustainable development objectives such as prioritizing human needs, ensuring socio-environmental sustainability, and enhancing resilience. Digital twins and simulation technologies improve manufacturing, evaluate products and operations, and predict any potential adverse consequences. With digital twin technology, everything that exists in the physical world will eventually be duplicated in the digital realm. Within the context of Industry 5.0, this study aims to investigate the impact of digital twin technology on the fan manufacturing sector. The proposal for implementing the enabling industry 5.0 application was presented to the chief engineers of eight distinct fan manufacturers. Out of these, five responded positively and their feedback was subsequently followed up on. Three different avenues such as production, supply chain, and testing transparency were proposed for industry 5.0 implementation. The exploration of testing transparency is being undertaken based on a consensual decision. The Web of Things standard that enables digital twin generation in industry 4.0 is implemented to enable the testing transparency. This data was linked with the internal digital twin of fan motor created using ANSYS. This digital twin can predict the lifespan of the motor by analyzing the temperature of the motor housing surface. Toward sustainability and resilience with Industry 4.0 and Industry 5.0 may provide insights into this alignment.

The electrochemical treatment of canal water was investigated in a batch-wise system in the presence of stainless steel 316-grade electrodes. Three effective process parameters, including current density, reaction time, and electrode spacing, were evaluated in the range of 0.25–2.5 mA/cm², 1–10 min, and 0.5–2.5 cm, respectively. Operational variables of electrochemical disinfection are optimized in response surface methodology (RSM) using Box–Behnken design. Before electrochemical disinfection, a pretreatment process of coagulants mixing for turbidity removal was conducted. Results revealed that a 10 ppm dosage of Ferric chloride (FeCl₃.6H₂O) and alum (Al₂(SO₄)₃·16H₂O) at neutral pH is appropriate. Furthermore, the RSM analysis shows that interelectrode spacing is the most prominent factor affecting the disinfection performance, and increasing electrode spacing inversely affects the disinfection efficiency. Results revealed that 1.52 mA/cm² current density, 6.35 min reaction time, and 1.13 cm of electrode spacing are the optimum conditions, resulting in a statistically 98.08% disinfection of the total coliform. The energy required for electrochemically disinfection of water at optimum conditions was 0.256 kWh/m³.

The increasing environmental concerns associated with fossil fuels have intensified the demand for sustainable and renewable energy sources such as biodiesel. In this study, biodiesel production from palm-based waste cooking oil was optimized using Response Surface Methodology (RSM) and a Kolmogorov-Arnold Networks (KANs) model. A Central Composite Design (CCD) was employed to evaluate the effects of reaction temperature (55-70 °C), reaction time (60-120 min), agitation speed (500-700 rpm), catalyst concentration (1-5 wt%), and methanol-to-oil ratio (6:1-12:1) on biodiesel yield. The maximum experimental biodiesel yield of 99.7% was obtained under optimized reaction conditions, demonstrating the effectiveness of the transesterification process. The RSM model exhibited coefficient of determination (R² = 0.9597) and identified the optimal conditions as 55 °C reaction temperature, 120 min reaction time, 500 rpm agitation speed, 1 wt% catalyst loading, and 12:1 methanol-to-oil ratio. To further enhance prediction accuracy, a KAN model was developed and evaluated. The KAN model achieved an overall coefficient of determination of R² = 0.9576, with superior training performance (R² = 0.9893). The model exhibited low prediction error (RMSEP = 5.6664), with most relative errors within ±10% indicating strong capability in capturing nonlinear relationships. Comparative analysis revealed that while RSM provides reliable and interpretable optimization, the KAN model offers enhanced flexibility and improved prediction of complex nonlinear interactions. Therefore, KAN presents a promising approach for advanced optimization of biodiesel production processes. This study demonstrates the potential of KAN as a next-generation modeling tool for complex biofuel production systems.

Expanding energy demand and increasing environmental concerns regarding fossil fuels have prompted the development of renewable fuel technologies. This study examined g-C₃N₄/MoS₂ composite photocatalysts with different weight percentages using XRD, FESEM, EDX, FTIR, and UV-Vis spectroscopy. Under UV and VL exposure, the efficiency was evaluated for the synthesis of biodiesel utilising WCO as a feedstock. According to UV-Vis analysis, the optimised g-C₃N₄/MoS₂ composite had the lowest band gap of 1.86 eV, confirming the improved VL absorption and decreased band gap. The structural and morphological investigation verified the competent relations and successful heterojunction development amid g-C₃N₄ and MoS₂, which facilitates effective detachments of charges. The maximum biodiesel yield of 64% under UV exposure and 55% under VL illumination was demonstrated by the optimized composite 70:30 with reaction parameters 3 hours, 40 °C, 12:1 m/o ratio, 2 wt.% catalyst loading, and 600 rpm. The biodiesel synthesised was confirmed to meet the standards for biodiesel, and the photocatalyst demonstrated good activity for up to five reaction cycles. The synergistic relationship and viable charge division in g-C₃N₄/MoS₂ heterojunction are responsible for the enhanced photocatalytic activity, indicating the capability of g-C₃N₄ as a sustainable system for biodiesel production application.