Biochemical Oxygen Demand

MATHEMATICAL MODELS FOR THE ANALYSIS AND FORECASTING OF RIVER WATER POLLUTION USING THE MULTIFRACTAL METHOD

This paper explores multifractal analysis for the selected time series water pollution data set and further prediction based on BOD measure with ARFIMA-based fractal model. MFDFA multifractal algorithm is applied for estimating the fractal differentiation parameter of the ARFIMA. The obtained results are compared with similar obtained with autoregressive ARIMA model and basic ARFIMA fractal model. The study reveals an enhancement in accuracy with the use of combination of multifractal analysis and fractal methods for water pollution prediction

DEVELOPMENT OF SOFTWARE AND ALGORITHMIC EQUIPMENT FOR PREDICTION OF RIVER WATER POLLUTION USING FRACTAL ANALYSIS METHODS

This paper explores the application of the ARFIMA fractal model for prediction of the dynamics of river water pollution based on BOD measure. The study begins by conducting a review of related works in the field of water quality analysis. At this stage also a suitable dataset is selected, that is used to train the ARFIMA, one of the machine learning models. GPH semi-parametric algorithm is applied for estimating the fractal differentiation parameter of the ARFIMA.

CHLORELLA VULGARIS IN WASTEWATER TREATMENT PROCESSES – PRACTICAL EXPERIENCE

Wastewater from human settlements contains a significant amount of organic and biogenic substances. Insufficiently treated wastewater enters surface water and leads to their eutrophication. The usage of microalgae in wastewater treatment has significant advantages in comparison with other methods of removing biogenic substances. Namely: effective and simultaneous removal of nitrogen and phosphorus without reagents management facilities, oxygen formation. Using microalgae in wastewater treatment is a new environmentally friendly biotechnological method.