Sustainable bioremediation of shrimp aquaculture wastewater using chlorella sp. Microalgae

Shrimp farming is a rapidly expanding sector within aquaculture and has dramatically increased global production. This growth has brought challenges in the management of wastewater. The wastewater from shrimp aquaculture effluents contains harmful organic and inorganic contaminants, including nitrogenous chemicals such as ammonium and nitrate, which result from excessive feed and high stocking density. These contaminants pose serious threats to ecosystems, human health, and the growth and survival of aquatic animals. Green microalgae have gained attention as an effective alternative treatment in shrimp wastewater management strategies. However, the potential of microalgae to become an extensive application will require continuous monitoring to overcome current limitations regarding harvesting for future applications. Therefore, the first objective was to determine the bioremediation capabilities of green microalgae, Chlorella sp., isolated from shrimp farms across varying microalgae concentrations of 0% (v/v) to 60% (v/v). Findings revealed that the optimum dosages were 30% (v/v) and 40% (v/v) for Marine water (MW) and Freshwater (FW), respectively. On Day 10, microalgae achieved significant nutrient removal for ammonia, nitrite, and orthophosphate, with efficiencies of 96.77%, 82.07%, 75.96% for MW and 90.10%, 87.09%, 95.60% for FW. Furthermore, microalgae cell density increased by over tenfold, leading to specific growth rates of 0.18 day-1 and 0.15 day-1. The second objective was to evaluate the harvesting efficiency of the excess microalgae by focusing on the dosage that achieved the highest nutrient removal efficiency. In this study, a bio-flocculant fungus, Aspergillus niger, and Response Surface Methodology (RSM) with a central composite design was used to assess the effects of fungus dosage, pH, and mixing rate. The optimized conditions yielded harvesting efficiencies exceeding 90% at a dosage of 30 g L-1, pH of 7, and mixing rate of RPM 125. The third objective involved using machine learning using Waikato Environment for Knowledge Analysis (WEKA) to develop models for bioremediation and bio-harvesting processes. In this study, various algorithms were used to analyze the dataset involving computational methods to create predictive models that enhance the efficiency of the processes. The analysis using WEKA software identified the M5P algorithm as the most effective for nutrient removal and microalgae harvesting due to its high correlation coefficient (>0.9) and low error rates (<0.1). The M5P decision tree analysis results can identify the optimal conditions for bioremediation and harvesting. This can help save time when designing new experiments for large-scale wastewater treatment. A user-friendly interface was developed to facilitate practical applications that integrate the predictive model. This interface makes it easy for future research and simplifies the process of large-scale wastewater treatment. The findings of this study highlight that Chlorella sp. effectively removes pollutants, Aspergillus niger is helpful for aggregating microalgae, and the application of the M5P algorithm enhances both bioremediation and harvesting processes. These findings contribute to sustainable bioremediation and biomass harvesting while promoting sustainable environmental practices and renewable energy production.

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