Issue 7

Because of its better physio-chemical qualities, biofuel is indeed a viable substitute source of energy for diesel vehicles. The biofuels used in this study is synthesized from pumpkin seed oil using the esterification reaction technique. The biodiesel was synthesized under ideal conditions and was employed to evaluate the impact of injection pressure varying from 200 to 275 bar in 25 bar increments and was tested for its effectiveness, combustions and the emission performance of 1 cylinder, 4 stroke DI diesel engine at a regular momentum of 1500 rpm using 25% Pumpkin seed biodiesel (PSBD25). The outcomes demonstrated that the BTE of PSBD25 be raised by 1.68 %, the release of CO, HC, and smoke were diminished, but NO emissions were boosted at higher IP of 250 bar compared to the standard IP of 200bar. Pressure of Cylinder and heat release rate for PSBD25 were increased for PSBD25, but ignition delay was shortened when compared to PSBD25 at standard IP of 200 bar. It is concluded that, there is a greater progress in performance and ignition features, as well as a reduction in exhaust gas release for PSBD25 with the exception of NO emissions at 250 bar IP at maximum load conditions.

Hazardous metal chromium is used extensively in many different industrial processes and is commonly found in industrial effluents. Hazardous pollutants such inorganic anions, metal ions, synthetic xenobiotics, and others have contaminated ground and surface waters in India and other parts of the world as a result of continued industrial expansion and agricultural development. This study uses betel seed powder and a fixed bed reactor (FBR) technology running at room temperature to extract chromium (VI+) from a synthetic water environment. The fixed-bed research included significant experiments such as the development of breakthrough curves (including the effects of starting Cr (VI+) concentration, BSF size, and flow rate) and bed-depth service time (BDST) approach. Betel Seed Powder was used as a biosorbent to remove Cr (VI+) from aqueous solution. The effects of several variables, including bed height (2.5, 5.5, and 7.5 cm), flow rate of 40, 60, and 100 ml/min, were investigated at an influent pH of 6.5. It was shown that as bed depth, flow velocity, and starting concentration rose, so did the exhaustion time. The biosorptive capacity of betel seed powder in fixed bed reactor tests was approximately 8–9 times higher than that of betel seed powder in continuous stirred batch reactor study. For the removal of Cr (VI+) by BSF, the BDST method achieved a higher bed efficiency of 98.9% and was ideal. The biosorptive ability of BSP was found to be highly correlated with the initial Cr (VI+) concentration (No = 0.089Co).

Dyes play a crucial role in various industries, but their environmental impact and potential health risks have raised concerns. This review paper examines the classification and applications of dyes, highlighting the challenges posed by synthetic dyes and the growing interest in natural dyes. The impact of textile dyes on human health, ecosystems, water, soil, and air emissions is explored. The focus then shifts to sustainable dyeing practices in the denim industry, which can significantly reduce its environmental footprint and enhance product sustainability. The adoption of sustainable dyes aligns with circular economy principles, leading to energy and water savings. Collaboration, consumer awareness, and the integration of Life Cycle Assessment (LCA) are essential for driving sustainable dye adoption and promoting circular economy principles. The role of Environmental, Social, and Governance (ESG) factors in sustainable denim dyeing is emphasized, encompassing environmental, social, and governance considerations throughout the supply chain. The need to address greenwashing and validate sustainability claims is discussed, highlighting the importance of certifications and transparency. Chemical dyes are recognized as key contributors to achieving sustainability targets in the denim industry, aligning with the Science Based Targets initiative (SBTi) and reducing carbon emissions. The review concludes by stressing the importance of sustainable dyeing practices, considering water and energy usage, waste generation, and emissions, to minimize the denim industry's environmental impact and achieve sustainability objectives.

Researchers have been focusing on protecting water quality and preventing environmental contamination caused by industrial discharge of heavy metals. Various methods have been explored for extracting these metals from wastewater, but they often prove to be costly and rely on non-renewable resources. Adsorption, however, offers a promising solution due to its stability, affordability, and ease of implementation. In this research, continuous biosorption tests were conducted using a glass column filled with Sargassum Wightii (SW) seaweed, with Pb(II) ions used as a model solute. Varying bed heights (15-25cm) and flow rates (0.3-0.6 L/hr) in the column tests aimed to maximize biosorption efficiency. The optimal conditions for metal uptake and removal were a bed height of 25cm and a flow rate of 0.3L/hr, resulting in Sargassum Wightii adsorbing 41.17mg/g of metals with a removal efficiency of 69.07%. The Modified dose-response model effectively characterized Pb(II) biosorption at different bed heights and flow rates. Column regeneration studies demonstrated breakthrough and sorption within 2 hours, complete depletion at 24 hours, and an elution efficiency of 99.2% using 0.1M NaOH, allowing for up to three cycles of biosorbent reuse.

Owing to increased population, the ground water pollution and disposal of solid waste from the domestic, commercial and the industrial sources has become higher. Mainly, leachate from sanitary landfills increase the ground water pollution and disposal of solid wastes may produce the emission of carcinogenic greenhouse gases. The gases include Carbon-dioxide (CO2), Nitrogen-dioxide (NO2), Methane (CH4) and Hydrogen Sulphide (H2S) which will contributes to the major and detrimental in nature. To know about the extremity of the gases, implementation of integrated sensors networks can transmit and store data in the cloud using the Internet of Things (IoT) to perform future prediction using Machine Learning Algorithm. To estimate ground water pollution, sample locations have been chosen using random sampling method which holds 89% efficiency in data sampling. Due to the increased proportion of liquid and solid waste at 17:93 ratio existence in the city, the study elaborates the health impacts of pathogens by pointing as a cancer capital of around 34% influence rates. Based on the CO2 emission and water pollution analysis, the modelling is done using Linear Regression Machine Learning algorithm which flags up the emission rate that could occur for the next three months with 86% accuracy. Also, appropriate mitigation measures can be suggested to the local government in the view of reducing both ground water pollution and the gaseous emissions. Finally, the identified pollution potential in the Vendipalayam site is compared with other landfill sites level of pollution for providing the adaptive measures further enhances environmental sustainability.

Numerous alternative fuels such as biodiesel as well as alcohols have been commercialized in both the industrial and transportation sectors. Cottonseed oil and propanol piqued our curiosity in this context. Many researches has been conducted on a single cylinder four strokes diesel engine competent of generating 6.3 kW at 2000 r.p.m and in changing load conditions for mixture of Cottonseed oil as well as Propanol by means of diesel geared up on a volume base, namely D90-C10, D85-C10-P5, D80-C10-P10, D80-P20, D75-C20-P5 and D70-C20-P10. The results suggest that adding cottonseed oil to diesel improves thermal efficiency while lowering specific fuel usage. With an increase in HC emission, escalating the percentage of cottonseed oil in the merge trim down emanation parameters like CO, CO2 and NOX. Propanol added to a cottonseed oil merged along with diesel has a comparable consequence to adding pure cottonseed oil in various proportions.

The present work aimed to carry out the phytochemical analysis and antioxidant activity of hydro-methanolic, ethyl acetate and butanolic extracts of Dianthus sylvestris subsp, aristidis (Batt,) Greuter & Burdet leaves. Phenolics, flavonoids and flavonols contents were measured by the Folin-Ciocalteu reagent and the aluminum chloride (AlCl3) method respectively. Phytochemical analysis was determined by using LC-ESI-MS/MS method. Antioxidant activity, in vitro, was evaluated by six different assays including: DPPH scavenging, ABTS cation radical, Ferrous ions chelating, Phenanthroline, Reducing power and Silver nanoparticle. The findings showed that the butanolic extract was found to be higher in phenolics and flavonoids contents (121.45 ± 1.87 μg GAE/mg extract and 57.09±0.14 μg QE/mg extract respectively). While ethyl acetate extract was found to be higher in falvonols contents (41.90±0.98µg QE/mg extract). The LC-ESI-MS/MS analysis of ethyl acetate and butanolic extracts revealed the presence of fifteen compounds among which: four phenolic acids, five flavonoids, one phenolic aldehyde and three vitamins, with P- coumaric acid, Hesperetin, Vanillin, Ascorbic Acid as major compounds respectively. The butanolic and ethyl acetate extracts exhibited the highest antioxidant activity in Phenanthroline assay (A0,5: 17.75± 0.72 µg/mL and A0.5: 21.02 ± 0.43 µg/mL respectively). On the basis of the significant findings, D. sylvestris subsp, aristidis would be recommended as a new potential source of natural antioxidant for pharmaceutical industries.

Land Use and Land Cover (LULC) maps perform a significant part in Remote Sensing (RS) in monitoring and analyzing earth information for human development. Because of the high spectrum variability, LULC classification from RS data is extremely difficult. The study's objective is to assess the object-based LULC classification (OBC) accuracy of composite images from the Landsat 8 OLI (Operational Land Imager) and auxiliary features using SNIC segmentation and five classifiers on Google Earth Engine (GEE). The outcome of the study indicates that the OBC with only spectral features achieves lesser accuracy because small objects on the land surface cannot be observed. But when OBC is paired with a variety of auxiliary features, the OBC may recognize small objects with greater accuracy. After many subsequent assessments the combination of the following aspect: composite image of all the seven bands, new feature set, Simple Non-Iterative Clustering (SNIC) segmentation algorithm, and Support Vector Machine (SVM) classification algorithm gives better accuracy of ~ 94.42% and kappa coefficient of 92.07. The inclusion of auxiliary features and the OBC method reduces the misclassification rate related to the confusion of bare land, uncultivated land, and urban, which provides accurate information on forest, waterbody, and bare land classes.

Forest fires are a global natural calamity causing significant economic damage and loss of lives. Professionals forecast that forest fires would raise in the future because of climate change. Early prediction and identification of fire spread would enhance firefighting and reduce affected zones. Several systems have been advanced to detect fire. In recent times, Unmanned Aerial Vehicles (UAVs) are used for tackling this issue because of their ability, high flexibility, and their cheap price to cover vast areas during the nighttime or daytime. But still they are limited by difficulties like image degradation, small fire size, and background complexity. This study develops an automated Forest Fire Detection using Metaheuristics with Deep Learning (FFDMDL-DI) model. The presented FFDMDL-DI technique exploits the DL concepts on drone images to identify the occurrence of fire. To accomplish this, the FFDMDL-DI technique makes use of the Capsule Network (CapNet) model for feature extraction purposes with a biogeography-based optimization (BBO) algorithm-based hyperparameter optimizer. For accurate forest fire detection, the FFDMDL-DI technique uses a unified deep neural network (DNN) model. Finally, the tree growth optimization (TGO) technique is utilized for the parameter adjustment of the DNN method. To depict the enhanced detection efficiency of the FFDMDL-DI approach, a series of simulations were performed. The outcomes reported improvements in the FFDMDL-DI method over other DL models.