Characterisation and Treatment of Automobile and Battery Water Waste Using Coagulation and Adsorption Technique

Adsorption processes have seen a broad level of usage by various researchers over the years to remove heavy metals from waste streams. An adsorbent frequently used is activated carbon. Although activated carbon is used extensively in water and wastewater industries, it remains expensive. In recent years, there has been a need for economical and safe methods for heavy metal elimination from contaminated water. This need has only grown, which gave rise to research aimed toward producing low-cost alternatives to activated carbon. There is an urgent need to explore all possible sources of inexpensive agro-based adsorbents, and heavy metal removal's feasibility should be studied in great detail. This research objective is to learn how inexpensive adsorbents can remove heavy metals from wastewater. Treating Automobile battery waste efficiently remains a significant challenge due to its enormous quantity, hazardous nature, and complexity. When effective treatment technology has poor implementation, this aggravates the situation and worsens the situation further in countries with high and rising populations. This leads to more pressures of urbanisation and industrials, giving room for more industrial waste.This research suggests the best possible options by critically reviewing the existing practices. While slow biological treatment schemes usually fail to support microbial activities in the presence of toxic components in concentrations, other physicochemical plans often need to meet strict waste disposal and discharge regulations. The approach where sludge disposal is thermally incinerated has never seen environmental justification, and this is because of dioxin release and obnoxious substances transferred directly. Newly emerging membrane-based schemes can be flexible, environmentally friendly, petite, economically self-sufficient, and easy to implement and maintain after installation

Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network

The research used an artificial neural network (ANN) to examine optimum extraction conditions and phytochemical contents of Luffa cylindrica seed oil. The oil yield was predicted using an artificial neural network. The performance of the ANN and response surface methodology models was compared. The optimum extraction yielded 7.567% oil yield, 185.676 mg/l phenol, and 45.087 mg/l terpineol at 75.57 °C extraction temperature, 5.77 h extraction time, and 10.68 g/mol n-hexane concentration, respectively. These data show that the oil output is poor but has a significant phenol and terpenoid content that may be employed in pharmaceutical sectors. The FT-IR analysis of Luffa cylindrica seed oil revealed a high level of unsaturated hydrocarbons and esters, making the oil appropriate for using in the paint industry and creating cosmetics

Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network

The research used an artificial neural network (ANN) to examine optimum extraction conditions and phytochemical contents of Luffa cylindrica seed oil. The oil yield was predicted using an artificial neural network. The performance of the ANN and response surface methodology models was compared. The optimum extraction yielded 7.567% oil yield, 185.676 mg/l phenol, and 45.087 mg/l terpineol at 75.57 °C extraction temperature, 5.77 h extraction time, and 10.68 g/mol n-hexane concentration, respectively. These data show that the oil output is poor but has a significant phenol and terpenoid content that may be employed in pharmaceutical sectors. The FT-IR analysis of Luffa cylindrica seed oil revealed a high level of unsaturated hydrocarbons and esters, making the oil appropriate for using in the paint industry and creating cosmetics