Copper Ion Removal Method and Engineering Dosing Prediction Model Based on Chlorella–Actinomycetes Symbiotic System

Abstract

To address the lack of systematic ratio-screening logic and engineering decision-making basis in existing microalgae-bacteria symbiotic systems for heavy metal wastewater treatment, the biological removal systems were constructed using different ratios of Chlorella sp. and Streptomyces sp. to treat Cu2+-containing wastewater in this research. Cu2+ concentrations were determined by DDTC colorimetric spectrophotometry to calculate removal efficiency, and the optimal end point time for Cu2+ removal was investigated under different algae-bacteria ratios. The results showed that, within the advantageous region of "more algae, less bacteria", the optimal operable inoculation volume ratio of Chlorella sp. to Streptomyces sp. was further identified as 3:1. On this basis, a predictive model based on the particle swarm optimization-support vector regression (PSO-SVR) algorithm was introduced to solve the engineering dosing volume problems, in which the initial concentration and target compliance days were regarded as the inputs and the required dosing volume as the output. The results demonstrated that the model exhibited good generalization performance (R2 = 0.9025) and was further transformed into a visualized "concentration–time frame-dosage" decision heatmap. This study confirmed the synergistic advantage of the Chlorella/Streptomyces system in Cu2+ removal and provided visual references and model support for compliance-based dosing decisions.