Abstract
The paper presents a new approach to solving the problem of water quality control in rivers. We proposed an intelligent system that monitors and controls the quality of water in a river. The distributed measuring system works with a central control system that uses the intelligent analytical computing system. The Biochemical Oxygen Demand (BOD) and Dissolved Oxygens (DO) index was used to assess the state of water quality. Because the results for the DO measurement are immediate, while the measurement of the BOD parameter is performed in a laboratory environment over a period of several days, we used Artificial Neural Networks (ANN) for immediate estimation BOD to overcome the problem of controlling river water quality in real time. Mathematical models of varying complexity that represent indicators of water quality in the form of BOD and DO were presented and described with ordinary and distributed-parameters differential equations. The two-layered feed-forward neural network learned with supervised strategy has been tasked with estimating the BOD state coordinate. Using classic ANN properties, the difficult-to-measure river ecological state parameters interpolation effect was achieved. The quality of the estimation obtained in this way was compared to the quality of the estimation obtained using the Kalman–Bucy filter. Based on the results of simulation studies obtained, it was proved that it is possible to control river aeration based on the measurements of particular state coordinates and the use of an intelligent module that completes the “knowledge” concerning unmeasured data. The presented models can be further applied to describe other cascade objects.
Highlights
Contaminated water, partially or completely polluted as a result of household, industrial, agricultural and other uses is commonly referred to as waste water
The existence of a large deficit of dissolved oxygen at high Biochemical Oxygen Demand (BOD) values is a natural phenomenon, which in the case of high oxygen demand generates an increase in the Dissolved Oxygens (DO) deficit
The use of artificial neural networks to solve the problem represented by BOD and DO indicators has been presented
Summary
Contaminated water, partially or completely polluted as a result of household, industrial, agricultural and other uses is commonly referred to as waste water. The largest part of the pollution in waste water contains detergents, organic matters and oils. There are different approaches for removing these contaminants or eliminating them. We can divide them into two groups of methods: artificial or natural. The first method uses a set of filters: mechanic filters (for precipitation, flocculation, trapping pollutants by organisms and by hyporheic sediment, sorption on mineral and organic particles), chemical filters (for chemical degradation of pollutant, as abiotic oxidation and photo-oxidation) and biochemical filters (biodegradation of pollutants, assimilation). The second method, referred to as self-purification, is a natural process of rivers, lakes or canals to recover the rate of dissolved oxygen
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have