Analysing the effects of culture parameters on wastewater treatment capability of microalgae through association rule mining

Abstract

Large-scale implementation of the microalgae-based wastewater treatment is touching its end point rapidly. However, improvements are still needed to be done, particularly the optimization of cultivation parameters, including light intensity, CO2 content, initial inoculum level and nutrient concentration in wastewater. Machine learning algorithms can easily optimize the cultivation parameters by analysing the microalgae-based wastewater treatment process dataset without experimental runs. In the present investigation, one of the data mining tools, association rule mining has been used to find specific conditions of 11 cultivation parameters for enhancing microalgae growth in wastewater. General rules derived from association rule mining showed that biomass productivity and nutrient removal efficiency can be increased by keeping CO2 content between 0.53% and 2.53%, light intensity in the range of 200-1500 μmol m-2 s-1, initial inoculum level from 0.2 to 0.4 g/L and N/P ratio nearly 15:1-50:1. This extracted information can be used to design future experimental runs and will help in implementation of the process at a large scale without wet laboratory experiments. © 2022 Elsevier Ltd.