Bioaugmentation Technology for Treatment of Toxic and Refractory Organic Waste Water Based on Artificial Intelligence

Assessing industrial wastewater effluent toxicity using boosting algorithms in machine learning: A case study on ecotoxicity prediction and control strategy development

Trace heavy metals have a tendency to persist in the effluent of industrial wastewater treatment facilities, leading to toxic effects on downstream water bodies. Traditional assessment methods relied on animal testing, but ethical concerns have rendered them unacceptable. An alternative solution is to evaluate wastewater toxicity using trophic-level aquatic organisms as bioassays. However, these bioassay methods involve costly and time-consuming chemical and biological analytical experiments. In this study, an artificial intelligence-powered water quality assessment (AiWA) approach is proposed for predicting industrial effluent ecotoxicity to further enhance the quick and cost-effective ecotoxicity assessment process. Initially, 99 samples were collected from industrial wastewater treatment plants representing 21 different industries in the Republic of Korea. Fourteen parameters were measured, encompassing both physicochemical and ecotoxicological aspects. Boosting algorithms, especially extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost), were employed for model development. XGBoost outperformed AdaBoost in terms of model performance. Feature selection analysis revealed that conductivity, copper, lead, selenium, pH, and zinc concentrations were the most suitable inputs for training the boosting model. The innovated XGBoost-based AiWA model demonstrated significantly higher performance (i.e., up to 80%) compared to conventional models with an R2 value of exceeding 0.94 and root mean square error of 3.5 toxicity unit for predicting the integrated toxicity unit (ITU). Additionally, pH and conductivity emerged as crucial indicators for reflecting ecotoxicity levels. Specially, this case study indicated that non-toxic/directly dischargeable levels (TU ≤ 1) were achieved when the pH ranged from 6.8 to 8.4 and the conductivity remained below 1651 μS/cm. These findings are expected to facilitate rapid and cost-effective detection of heavy metal ecotoxicity in industrial wastewater effluents, aiding decision-making in wastewater management.

Advanced bioremediation by an amalgamation of nanotechnology and modern artificial intelligence for efficient restoration of crude petroleum oil-contaminated sites: a prospective study

Crude petroleum oil spillage is becoming a global concern for environmental pollution and poses a severe threat to flora and fauna. Bioremediation is considered a clean, eco-friendly, and cost-effective process to achieve success among the several technologies adopted to mitigate fossil fuel pollution. However, due to the hydrophobic and recalcitrant nature of the oily components, they are not readily bioavailable to the biological components for the remediation process. In the last decade, nanoparticle-based restoration of oil-contaminated, owing to several attractive properties, has gained significant momentum. Thus, intertwining nano- and bioremediation can lead to a suitable technology termed 'nanobioremediation' expected to nullify bioremediation's drawbacks. Furthermore, artificial intelligence (AI), an advanced and sophisticated technique that utilizes digital brains or software to perform different tasks, may radically transfer the bioremediation process to develop an efficient, faster, robust, and more accurate method for rehabilitating oil-contaminated systems. The present review outlines the critical issues associated with the conventional bioremediation process. It analyses the significance of the nanobioremediation process in combination with AI to overcome such drawbacks of a traditional approach for efficiently remedying crude petroleum oil-contaminated sites.