Application of statistical modeling for the production of highly pure rhamnolipids using magnetic biocatalysts: Evaluating its efficiency as a bioremediation agent
Creators
- 1. Integrated Bioprocessing Laboratory, School of Bioengineering, SRM Institute of Science and Technology (SRM IST), Kattankulathur, Tamil Nadu 603203 (India)
- 2. Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649–003 Lisboa (Portugal)
- 3. Department of Chemical Engineering, College of Engineering, Khalifa University, Abu Dhabi 127788 (United Arab Emirates)
- 4. Department of Chemistry, SRM Valliammai Engineering College, Kattankulathur, Chennai 603203, Tamil Nadu (India)
Description
Highlights: • Highly pure rhamnolipids (RLs) was produced using immobilized naringinase and CaLB. • The biocatalysts were immobilized on cMNPS with high catalytic load and stability. • Optimization using ANN-GA model increased rhamnolipids yield from 72.36% to 80.25%. • ANN-GA model was more efficient than CCD model with a mean square error of 1.04%. • RL-assisted soil washing showed 95.35 ± 1.2% removal of TPHs from sediment soil. In the present study, highly pure rhamnolipids (RLs) was produced using biocatalysts immobilized on amino-functionalized chitosan coated magnetic nanoparticles. Upon immobilizing naringinase and Candida antarctica lipase B (CaLB) under the optimized conditions, an enhanced operational stability with biocatalytic loads of 935 ± 2.4 U/g (naringinase) and 825 ± 4.1 U/g (CaLB) were achieved. Subsequently, the immobilized biocatalysts were utilized sequentially in a two-step RLs synthesis process. The key parameters involved in RLs production were optimized using artificial neural network (ANN) coupled genetic algorithm (GA) and were compared with composite central design (CCD). On validating the efficiency of both models, mean square errors of 1.58% (CCD) and 1.04% (ANN) were obtained. Optimization of parameters by ANN-GA resulted in 1.2-fold increase in experimental RLs yield (80.53%), which was 1.05-fold higher when compared to CCD model. Further, to establish the efficiency of RLs as a bioremediation agent, it was utilized as washing agent. It was observed that at a soil to RLs volume of 1:05, RLs concentration of 0.4 mg/mL, a 95.35 ± 1.33% removal of Total Petroleum Hydrocarbons (TPHs) was obtained at 35 ℃ and 160 rpm in 75 min. Thus, this strategy provides an efficient biocatalytic toolbox for RLs synthesis, which can be effectively used as a bioremediation agent.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.jhazmat.2021.125323Additional details
Identifiers
- DOI
- 10.1016/j.jhazmat.2021.125323;
- PII
- S0304389421002867;
Publishing Information
- Journal Title
- Journal of Hazardous Materials
- Journal Volume
- 412
- Journal Page Range
- vp.
- ISSN
- 0304-3894
- CODEN
- JHMAD9
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54028943
- Subject category
- S54: ENVIRONMENTAL SCIENCES; S02: PETROLEUM;
- Descriptors DEI
- AMINO ACIDS; BIOREMEDIATION; CANDIDA; COMPUTERIZED SIMULATION; DESIGN; ECOLOGICAL CONCENTRATION; ERRORS; GENETIC ALGORITHMS; HYDROCARBONS; NANOPARTICLES; NEURAL NETWORKS; OLIGOSACCHARIDES; OPTIMIZATION; PETROLEUM; SEDIMENTS; SOILS
- Descriptors DEC
- ALGORITHMS; CARBOHYDRATES; CARBOXYLIC ACIDS; ENERGY SOURCES; EUMYCOTA; FOSSIL FUELS; FUELS; FUNGI; MATHEMATICAL LOGIC; MICROORGANISMS; ORGANIC ACIDS; ORGANIC COMPOUNDS; PARTICLES; PLANTS; REMEDIAL ACTION; SACCHARIDES; SIMULATION; YEASTS
Optional Information
- Copyright
- Copyright (c) 2021 Elsevier B.V. All rights reserved.