Biomass and lipid production by the marine diatom Chaetoceros affinis were characterized under continuous light with aeration. Media based on palm oil mill effluent (POME; 10, 20 and 30 % v/v in distilled water) were used together with a standard control medium. The maximum biomass concentration on day 12 of batch cultures in control medium was 821 ± 71 mg L-1. Under identical conditions, in the best POME medium (20 % POME v/v in distilled water with other inorganic components), the biomass concentration was reduced by ∼11 % to 734 ± 66 mg L-1. The lipid content of the biomass grown in the control medium was 50.8 ± 4.5 % by dry weight, but was a little lower (48.9 ± 4.1 % by dry wt) in the above specified best POME medium. In the best POME medium, oleic acid was the major fatty acid (72.3 ± 5.2 % by weight) in the total lipids extracted from the biomass and monounsaturated fatty acids were the main type of fatty acids (74.6 ± 5.2 %). POME levels of >20 % in the medium suppressed both biomass and lipid production relative to the medium with 20 % POME.
The study investigates the heat transfer and friction factor properties of ethylene glycol and glycerol-based silicon dioxide nanofluids flowing in a circular tube under continuous heat flux circumstances. This study tackles the important requirement for effective thermal management in areas such as electronics cooling, the automobile industry, and renewable energy systems. Previous research has encountered difficulties in enhancing thermal performance while handling the increased friction factor associated with nanofluids. This study conducted experiments in the Reynolds number range of 1300 to 21,000 with particle volume concentrations of up to 1.0%. Nanofluids exhibited superior heat transfer coefficients and friction factor values than the base liquid values. The highest enhancement in heat transfer was 5.4% and 8.3% for glycerol and ethylene glycol -based silicon dioxide Nanofluid with a relative friction factor penalty of ∼30% and 75%, respectively. To model and predict the complicated, nonlinear experimental data, five machine learning approaches were used: linear regression, random forest, extreme gradient boosting, adaptive boosting, and decision tree. Among them, the decision tree-based model performed well with few errors, while the random forest and extreme gradient boosting models were also highly accurate. The findings indicate that these advanced machine learning models can accurately anticipate the thermal performance of nanofluids, providing a dependable tool for improving their use in a variety of thermal systems. This study's findings help to design more effective cooling solutions and improve the sustainability of energy systems.