Abstract
Considering the exponential growth of today’s industry and the wastewater results of its processes, it needs to have an optimal treatment system for such effluent waters to mitigate the environmental impact generated by its discharges and comply with the environmental regulatory standards that are progressively increasing their demand. This leads to the need to innovate in the control and management information systems of the systems responsible to treat these residual waters in search of improvement. This paper proposes the development of an intelligent system that uses the data from the process and makes a prediction of its behavior to provide support in decision making related to the operation of the wastewater treatment plant (WWTP). To carry out the development of this system, a multilayer perceptron neural network with 2 hidden layers and 22 neurons each is implemented, together with process variable analysis, time-series decomposition, correlation and autocorrelation techniques; it is possible to predict the chemical oxygen demand (COD) at the input of the bioreactor with a one-day window and a mean absolute percentage error (MAPE) of 10.8%, which places this work between the adequate ranges proposed in the literature.
Highlights
Pursuing the ideas outlined in the sustainable development goals (SDGs), countries have been showing concern for terrestrial ecosystems even more for the reuse and conservation of water quality
One of the concerns that exists and will be resolved day by day is related to the contamination of liquid effluents that arise from industrial uses
The most common problem regarding the quality of effluent water in industries is eutrophication, the result of large amounts of nutrients, which leads to the purity of the water being reduced [1]
Summary
Pursuing the ideas outlined in the sustainable development goals (SDGs), countries have been showing concern for terrestrial ecosystems even more for the reuse and conservation of water quality. Industry daily faces the challenge of treating wastewater as a result of its processes The monitoring of this treatment yields a large volume of revealing data that can increase the efficiency in the removal of the contaminant load in the water. Taking into account the exponential growth of industry at present and the amount of wastewater that its processes generate, it is essential for it to have an optimal treatment system for such effluents to mitigate the environmental impact generated by its discharges and comply with the environmental regulatory standards that increase their demand This leads to innovation both in the treatment systems and in control and information management systems thereof to achieve a more efficient process, whose advantages have been evidenced in different developed countries [3]. The present work refers to industrial wastewater, which is that from the discharges of manufacturing industries [5], and uses data from the activated sludge process in the biological stage for developing an intelligent system, making use of machine learning algorithms that allow for automatic extraction of information from previous examples and infer about new data [6], achieving the forecasting of the chemical oxygen demand (COD), which is an indicator of water pollution and is a key variable to evaluate the efficiency of the WWTP process [7]
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