Optimization techniques predict the conditions required to increase the efficacy of azo dyes degradation by microbial sources and also decrease the number of experimental runs to achieve the maximum percentage of degradation. Interaction of variables such as medium components and process parameters can be determined using optimization tools. | OPTIMIZATION OF MEDIA COMPONENTS AND PROCESS PARAMETERS FOR MICROBIAL MEDIATED REMEDIATION OF AZO DYES A REVIEW Rajeswari Uppala and Azhaguchamy Muthukumaran Address es Dr. Azhaguchamy Muthukumaran Department of Biotechnology Kalasalingam Academy of Research and Education Krishnankoil 626126 Tamil Nadu India Phone Number 91- 9486276120. Corresponding author https ARTICLE INFO ABSTRACT Received 6. 8. 2020 Azo dyes are one of the most commonly used synthetic dyes with enormous applications in the textile industry. The recalcitrant Revised 6. 8. 2021 properties of azo dyes could be attributed to the highly complex chemical organization. The limitations such as high cost and emergence Accepted 12. 8. 2021 of secondary toxic pollutants as by-products associated with physicochemical mode of degradation urged researchers to explore Published potential alternatives. Microorganisms having versatile metabolic pathways and adaptations to different environmental conditions gained the attention of researchers to exploit them for azo dyes degradation in a cost-effective manner. The azo dye degradation using microbial sources proved to be a promising approach as compared to conventional physicochemical approaches. Microorganisms can Review induce different metabolic pathways in response to the external environment. The biodegradation efficacy of the microorganisms-based approach can be maximized by optimizing the culture media and process parameters. Optimization techniques predict the conditions required to increase the efficacy of azo dyes degradation by microbial sources and also decrease the number of experimental runs to achieve the maximum percentage of degradation. Interaction of variables such as medium components and process parameters can be determined using optimization tools. Response Surface Methodology RSM and Artificial Neural Network ANN based optimization approaches were discussed in this review .
