A comprehensive review on the treatment of pharmaceutically active compounds using moving bed biofilm reactor: A systematic meta-analysis coupled with meta-neural approach.

Pharmaceutically active compounds (PhACs) in wastewater pose challenges to cleaner environment due to their recalcitrance and toxicity, restricting the use of conventional treatment methods. On the other hand, advanced oxidation processes face technical complexity and financial constraints, which also discourage their applicability especially in large scale treatment system. Moving Bed Biofilm Reactor (MBBR) as an advanced biological treatment system has shown remarkable efficacy and cost-effectiveness in treating various PhACs. However, studies report significant variations in the efficacy of MBBR across removing different pollutants, leading to a complication in their performance assessment. The present review has targeted a systematic meta-analysis coupled with a meta-neural approach over the conventional bibliometric study. The statistical approach resolves the publication bias and associated formation of a pertinent databases, providing significant insights into MBBR's performance and process variables. The novel approach of meta-neural exhibited a multivariate prediction model with a significant F value of 257.66 and a p-value of <0.001 relating the role of various process parameters on the treatment efficacy. Among various pharmaceuticals, beta-blockers were eliminated most effectively by MBBR technology, with removal rates exceeding those of antibiotics, analgesics, antidepressants, fibrates, and anticonvulsants. Sensitivity analysis revealed the significant influence of the operating parameters on the outcome in the order of initial COD > HRT > filling ratio > pH > initial concentration of the contaminant. The present meta-analysis approach vis-à-vis meta-neural is instrumental for delineating the technology selection and design for removing PhACs or other emerging contaminants.