Volume 25 - 2023

In the present work, the potential of cumnium cyminum as an effective bio adsorbent for the removal of cationic dye (Red 95) was investigated. Batch adsorption experiments were carried out to investigate the effects of adsorbent dosage, pH and contact time with initial dye concentration on adsorption. In connection with adsorbent dosage the highest removal efficiency was 143.64mg/L of removal efficiency 88.36%, regarding pH study the peak removal attained with 141.37mg/L with the removal percentage 86.76% and for contact time study the removal efficiency attained in the concentration of 142.10mg/L in the removal percentage of 87.41%. The adsorption studies demonstrated that the Thomson model, and Yoon nelson model are excellent descriptions of adsorption processes. The maximum adsorption capacity of 143.64 mg/L toward biosynthesized nano adsorbent with removal rates of 88.36% and greatest R2 value of around 0.9879 was well suited to the equilibrium data. The ANOVA was used for the analysis of variance to determine the best parameters for the overall adsorption model. Utilizing SEM, XRD, and FTIR methods, the adsorbent was characterized. Different desorbing agents, such as NaOH, HCl, and NaCl, were examined for the desorption of cationic dye (Red 95) loaded with adsorbent generated from cumnium cyminum, and it was shown that HCl was the most effective desorbing agent when combined with deionized doubly distilled water (DDDW). The proportion removal of cationic dyes was improved with increasing adsorbent dose and contact time and the percent of color exclusion was decreasing with increasing pH.

This research examines the scope for reducing the air pollution caused by the transportation sector through the infusion of biofuel and antioxidants ensuring the partial replacement of conventional fuel. A four-stroke, direct-injection, multi-cylinder and constant-speed diesel engine was employed for the experimental analysis. The ternary fuel blend with a composition of 70% diesel fuel, 20% rubber seed oil and 10% of pentanol and sea weed extract in different ratios were blended to form B20, B20A1, B20A2 and B20A3. The experimental results showed smoke and carbon monoxide reduced by 4.71%, unburned hydrocarbons by 2.2 %. The NOx emission which was slightly higher compared to diesel but not higher than B20 for the blend B20A2 by 5%. The brake thermal efficiency was increased by 3.71% when compared to diesel fuel. Consequently, the B20A2 (5% seaweed and 5% pentanol additive with 70% diesel and 20% of rubber seed methyl ester oil) was found to be an environmentally biodegradable gasoline with the best emission properties. The study findings are quite promising for future which seeks towards zero emission with optimum quantity of biofuel to replace diesel.

The process of making portland cement uses up a lot of resources and has a harmful environment effect since it produces a lot of greenhouse emissions. The by-product of the stone cutting and polishing industries is Bethamcharla waste Stone Powder (BWSP). Each industry produces an average of one tonne of waste each year, which is then simply deposited on the plains of Bethamcharla. In order to convince civil engineers to employ this new industrial waste material in Geopolymer Concrete (GPC), this research will examine the effects of substituting BWSP for Ground Granulated Blast-furnace Slag (GGBFS). Experimental investigations were conducted toward issues relating to strength. In the amounts of 20, 40 & 60 % by weight of GGBFS, the BWSP was employed as a substitute. Initial total mixes are prepared during the experimental inquiry with varying molarities of 8, 10, 12, 14, and 16 as well as changes to the mix proportions. According to the trials, the 16 molarity and 1:1.32:3.1 (GGBFS: FA: CA) mix with alkaline solution delivered preferable outcomes, allowing for the fullest possible use of BWSP in the mix. The current study provides information on the behaviour of BWSP in Geopolymer Concrete. As determined by the study, the minimum strength of concrete (M25) cannot be affected while using 60% BWSP with 16 molarity for construction.

Air quality, radiation pollution, and water pollution were thekey featuresthat possess genuine challenges to the environment. Proper monitoring wasneededin such a way that the world couldaccomplish sustainable development, withkeeping a healthy society. Recently, environment monitoring is become a smart environment monitoring (SEM) scheme, with development of modern sensors and the advancements in the internet of things (IoT). IoT devices were mainly used in WSN for pollution control, vehicle marking, temperature control, and effective waste management. Consequently, modern method of environmental monitoring is called as SEM system, owing to usage of wireless sensors, IoT and AI.This paper leverages IoT devices for sustainable air pollution monitoring. The presented model derives an improved red fox optimizer with deep learning based air pollution monitoring system (IRFODL-APMS) using IoT devices. The presented IRFODL-APMS technique makes use of different IoT devices to collect data. Besides, the IRFODL-APMS model performs prediction process using deep learning based on long short term memory (LSTM). At last, IRFO technique is exploited as a hyperparameter tuning process of the LSTM model to accomplish enhanced prediction performance. The presented IRFODL-APMS model is simulated under distinct measures and the outcomes reported the enhanced predictive outcomes of the IRFODL-APMS approach over other existing models.