Amit Pal

Internal combustion engine (ICE) is still the major source of energy for transportation systems worldwide. However, their environmental impacts continue to be a major issue, especially in developing countries like India. Since there is a lack of environmental evaluation of ICE technologies under Indian operating conditions, this study presents a comprehensive Life Cycle Assessment (LCA) of a newly developed diesel engine under Indian operating conditions. The assessment follows the ISO 14040 framework and employs the ReCiPe Life Cycle Impact Assessment (LCIA) methodology across production, operation, and end‐of‐life (EoL) phases. The operational phase is considered the major contributor to environmental degradation, mainly because of the consumption of diesel fuel. When neat diesel is used as the fuel, the highest impact is observed for Climate Change Potential (CCP) with 363,513.60 kg CO ₂ ‐eq, followed by fossil resource scarcity with 58,358.51 kg oil‐eq and Acidification Potential (AP) with 18,193.93 kg SO ₂ ‐eq. Photochemical Ozone Formation Potential (POFP) and Fine Particulate Matter Formation (FPMF) also present significant impacts with 259.16 kg NOx‐eq. and 501.45 kg PM‐eq., respectively. The results emphasize the need for new fuel alternatives like biodiesel, technological solutions like hybridization and proper policy measures for successfully reducing environmental emissions associated with ICE.

Hydrogen energy is an alternative to fossil fuels. It has the potential to generate electricity when used in a fuel cell. A fuel cell generates electricity till it receives hydrogen and oxygen. The present work evaluated a 1‐kW proton exchange membrane fuel cell for its performance at variable operating parameters. The operational parameters are essential in examining the outputs from the fuel cell. Hydrogen flow rate (HFR) and gas pressure were two operating parameters considered for the cell performance. The hydrogen was generated from a hydrogen production and purification unit, whereas the oxygen was captured from the air. About 61% reduction in the HFR from 13 to 5 L per minute (LPM) resulted in only 29% reduction in output current. In contrast, the addition and subtraction of 10% gas pressure from the rated value resulted in an 8% and 14% decline in output power. Similarly, a decline in the overall system efficiency was observed with increasing gas pressure from 0.4 bar to 0.9 bar, whereas the overall system efficiency increased at 5 LPM from 1 LPM. Therefore, the lower HFR reduced the cell output. Still, the hydrogen requirement also decreased significantly, which showed that the percentage decline in output was comparatively lower than the percentage of hydrogen saved.

To counter the concern of the future availability of fossil fuel due to ever‐increasing energy demand and the emission generated due to its utilization, alternative renewable fuels, such as biogas and fuel additives in the form of nanoparticles emerge as viable alternative fuels. To date, no research has been carried out on the optimization of the co‐addition rate of nanoparticles and biogas. In this study, engine load (0.7–3.5 kW), Nickel oxide (NiO) Nanoparticles doped rate (NDR, 0–50 ppm), and Biogas Flow Rate (BFR, 0.5–1 kg/h) were selected as independent input variables to optimize compression ignition (CI) engine emission and performance outputs through the response surface methodology (RSM) approach. The F‐value of the RSM model indicates that the engine load variable has the greatest impact on engine output responses, with the BFR and NDR variables following in importance. At 2.54 kW engine load, 47.48 ppm NDR, and 0.747 kg/h BFR, the best engine output response was seen when the RSM model including the desirability function was optimized. Based on the optimization results, an assertion can be made that the use of nanoparticles in conjunction with biogas has a substantial impact on the emissions and efficiency of CI engines.

In this study, experimental works were carried out in three different drying methods named HE-ETADS, greenhouse solar dryer (GHSD), and open sun drying (OSD) to compare thin layer drying kinetics, concept of mass transfer, and quality assessment of banana slices. Initial moisture content (MC) of banana slices was obtained 78 ± 2.0% (wb), which decreased to 23.2 ± 2.0% (wb), 25.6 ± 2.0% (wb), and 28.8 ± 2.0 % (wb) in all three drying systems respectively in 9hours of drying time. Average drying rate was evaluated as 7.89, 7.65, and 7.25gwater/g solid.hr in HE-ETADS, GHSD, and OSD, respectively. Weibull model (WM) defines thin layer drying kinetics of banana slices in all three drying processes. Maximum hardness and shrinkage factor of dried banana slices were obtained as 373.6g and 75%, respectively, in HE-ETADS. Effective moisture diffusivity, activation energy, and mass transfer coefficient were computed as 1.11E-07 to 2.48E-07m²s⁻¹, 30.25kJ/mole, 3.21E-04 to 1.0E-04m/s, in HE-ETADS. Similarly, in GHSD and OSD these factors were observed as 1.21E-07 to 2.34E-07m²s⁻¹, 41.25kJ/mole, 3.15E-04 to1.0E-04 m/s and 1.3E-07 to 2.21E-07m²s⁻¹, 56.89kJ/mole, 3.01E-04 to1.0E-04m/s. Maximum total color changes were noted in OSD. Hence, HE-ETADS can potentially dry high moisture content crops effectively within a minimum drying period.

The Taguchi technique has been used in this study to optimise the production parameters of biodiesel made from Argemone mexicana seed oil for maximum yield. This biodiesel has been produced through the transesterification process of non-edible vegetable oil of Argemone mexicana seeds. The molar ratio (A), Catalyst concentration (B), and reaction time or heating time (C) were chosen as the three factors that have a substantial impact on biodiesel production. All three factors are considered at three levels for experimental runs according to the L9 orthogonal array. The Biodiesel yield percentage was then measured for every experimental run. The signal to noise ratio has been calculated using the statistical software Minitab, based on the larger is better criterion. After analysing the Taguchi response table, the optimum level of all the parameters has been found out. The ANOVA technique was also applied to get the most significant factors that affect the biodiesel yield by calculating the percentage contribution of each factor. According to our results, the best combination of all the factors for Biodiesel yield is A3B3C1. The utmost and least influencing factors for Biodiesel production were heating time and molar ratio, with their contribution factors of 57.31% and 10.96%, respectively. Thus, the order of credence of all the three factors was CBA.