ABSTRACT
Bioremediation techniques, which harness the metabolic activities of microorganisms, offer sustainable and environmentally friendly approaches to contaminated soil remediation. These methods involve the introduction of specialized microbial consortiums to facilitate the degradation of pollutants, contribute to soil restoration, and mitigate environmental hazards. When selecting the most effective bioremediation technique for soil decontamination, precise and dependable decision-making methods are critical. This research endeavors to tackle the aforementioned concern by utilizing the tool of aggregation operators in the framework of the Linguistic Intuitionistic Fuzzy (LIF) environment. Linguistic Intuitionistic Fuzzy Sets (LIFSs) provide a robust framework for representing and managing uncertainties associated with linguistic expressions and intuitionistic assessments. Aggregation operators enrich the decision-making process by efficiently handling the intrinsic uncertainties, preferences, and priorities of MADM problems; as a consequence, the decisions produced are more reliable and precise. In this research, we utilize this concept to devise innovative aggregation operators, namely the linguistic intuitionistic fuzzy Dombi weighted averaging operator (LIFDWA) and the linguistic intuitionistic fuzzy Dombi weighted geometric operator (LIFDWG). We also demonstrate the critical structural properties of these operators. Additionally, we formulate novel score and accuracy functions for multiple attribute decision-making (MADM) problems within LIF knowledge. Furthermore, we develop an algorithm to confront the complexities associated with ambiguous data in solving decision-making problems in the LIF Dombi aggregation environment. To underscore the efficacy and superiority of our proposed methodologies, we adeptly apply these techniques to address the MADM problem concerning the optimal selection of a bioremediation technique for soil decontamination. Moreover, we present a comparative evaluation to delineate the authenticity and practical applicability of the recently introduced approaches relative to previously formulated techniques.
ABSTRACT
In this work, the bioremediation of wastewater from the textile industry with indigo dye content was carried out using combined bioaugmentation, bioventilation, and biostimulation techniques. Initially, the inoculum was prepared by isolating the microorganisms from the textile wastewater in a 2 L bioreactor. Then, the respirometry technique was implemented to determine the affinity of the microorganisms and the substrate by measuring CO2 and allowed the formulation of an empirical mathematical model for the growth kinetics of the microorganism. Finally, the bioremediation was carried out in a 3 L bioreactor obtaining an indigo dye removal efficiency of 20.7 ± 1.2%, 24.0 ± 1.5%, and 29.7 ± 1.1% for equivalent wavelengths of 436 nm, 525 nm, and 620 nm. The chemical oxygen demand showed an average reduction of 88.9 ± 2.5%, going from 470.7 ± 15.6 to 52.3 ± 10.7 ppm after 30 days under constant agitation and aeration. A negative generalized exponential model was fitted to assess the affinity of the microorganism with the wastewater as a substrate by evaluating the production of CO2 during the bioremediation. Bioremediation techniques improve water discharge parameters compared to chemical treatments implemented in the industry, reducing the use of substances that can generate secondary pollution. Bioaugmentation, biostimulation, and bioventing of the textile wastewater in this study demonstrate the potential of these combined techniques to serve as an efficient alternative for indigo-contaminated wastewater in the textile industry.