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Modeling biosurfactant production from agroindustrial residues by neural networks and polynomial models adjusted by particle swarm optimization

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Abstract

Biosurfactants are molecules with wide application in several industrial processes. Their production is damaged due to inefficient bioprocessing and expensive substrates. The latest developments of strategies to improve and economize the biosurfactant production process use alternative substrates, optimization techniques, and different scales. This paper presents a study to compare the performances of classical (polynomial models) and modern tools, such as artificial intelligence to aid optimization of the alternative substrate concentration (alternative based on beet peel and glycerol) and process parameters (agitation and aeration). The evaluation was developed in two different scales: Erlenmeyer flask (100 mL) and bioreactor (7 L). The intelligent models were implemented to verify the ability to predict the emulsification index and biosurfactant concentration in smaller scale and the biosurfactant concentration and the superficial tension reduction (STR) in bigger scale, resulting in four different situations. The overall results of the predictions led to artificial neural networks as the best performing modeling tool in all four situations studied, with R2 values ranging from 0.9609 to 0.9974 and error indices close to 0. Also, four different models (Wu, Contois, Megee, and Ghose-Tyagi) were adjusted by particle swarm optimization (PSO) in order to describe the kinetics of biosurfactant production. Contois model was the only one to present R2 ≥ 0.97 for all monitored variables. The findings described in this work present an adjusted model for the prediction of biosurfactant production and also state that the most adjusted kinetic model for further studies on this process is Contois model, leading to the conclusion that biomass growth is limited by a single substrate, considering only glucose.

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All data and materials as well as software application or custom code support this paper’s published claims and comply with field standards.

Abbreviations

\({EH}_{24h}\) :

Emulsification height after 24 h

\({EH}_{0h}\) :

Emulsification height at time zero

\(E\) :

Emulsification index

\(BC\) :

Biosurfactant concentration

\(STR\) :

Superficial tension reduction

\(v\) :

Particle’s velocity

\(p\) :

Particle’s position

\(iw\) :

Inertia weight

\({c}_{gk}\) :

Global learning coefficient

\({c}_{pk}\) :

Personal learning coefficient

\(K1\) :

Kinetic constant for glucose

\(K1a\) :

Kinetic constant for oxygen

\(Yxg\) :

Theoretical yield in terms of biomass

\(t\) :

Time (h)

\(X\) :

Biomass concentration (g L−1)

\(S\) :

Substrate concentration (g L−1)

\({S}_{c}\) :

Sucrose concentration (g L−1)

\(P\) :

Product concentration (g L−1)

\({P}_{\mathrm{max}}\) :

Maximum product concentration (g L−1)

\({C}_{{\mathrm{O}}_{2}}\) :

Oxygen concentration (g L−1)

\({C}_{{\mathrm{O}}_{{2}_{S}}}\) :

Saturated oxygen concentration (g L−1)

\({K}_{S} e {K}_{S}^{^{\prime}}\) :

Saturation constant (g L−1)

\({K}_{P} e {K}_{P}^{^{\prime}}\) :

Inhibition constant per product (g L−1)

\({K}_{X} e {K}_{X}^{^{\prime}}\) :

Inhibition constant by biomass (g L−1)

\({K}_{{O}_{2}}\) :

Oxygen saturation constant (g L−1)

\({K}_{i}\) :

Substrate inhibition constant (g L−1)

\({k}_{1}\), \({k}_{2}\), \({k}_{3}\) :

Kinetic constants (variable units)

\(N, n, m, k\) :

Kinetic parameters (dimensionless)

\({m}_{S}\) :

Maintenance coefficient for the substrate (g L−1 or h−1)

\({\mu }_{S}\) :

Specific substrate consumption speed (h−1)

\({\mu }_{x}\) :

Specific speed of microbial growth (h−1)

\({\mu }_{m}\) :

Specific speed of microbial growth (h−1)

\(\gamma\) :

Specific product formation speed (h−1)

\({\gamma }_{m}\) :

Specific product formation maximum speed (h−1)

\({\mu }_{{\mathrm{O}}_{2}}\) :

Specific breathing speed (h−1)

\({\mu }_{{m}_{{\mathrm{O}}_{2}}}\) :

Specific maximum breathing speed (h−1)

\({Q}_{{\mathrm{O}}_{2}}\) :

Specific respiration rate (\({g}_{{O}_{2}}{g}_{\mathrm{cells}}^{- 1}{h}^{-1}\))

\({m}_{O}\) :

Maintenance coefficient for \({O}_{2} ({g}_{{O}_{2}}{g}_{\mathrm{cells}}^{-1}{h}^{-1}\))

\({Y}_{O}\) :

Conversion factor from \({O}_{2}\) to cells (\({g}_{{O}_{2}}{g}_{\mathrm{cells}}^{- 1}\))

\({k}_{L}a\) :

Volumetric transfer coefficient of \({O}_{2}\) (h−1)

\({Y}_{X/S}\) :

Theoretical biomass yield (dimensionless)

\({Y}_{P/S}\) :

Theoretical product yield (dimensionless)

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Funding

The authors received financial support from the CNPq/MCT, CAPES, FAPERJ, and FINEP for the Department of Chemical and Material Engineering (DEQM) at the Pontifical Catholic University of Rio de Janeiro (PUC-Rio).

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Authors and Affiliations

  1. Department of Chemical and Materials Engineering (DEQM), Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rua Marquês de São Vicente, 225 – Gávea, Rio de Janeiro, RJ, 22430-060, Brazil

    Rodrigo de Andrade Bustamante, Juan Santos de Oliveira & Brunno Ferreira dos Santos

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  1. Rodrigo de Andrade Bustamante
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  2. Juan Santos de Oliveira
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  3. Brunno Ferreira dos Santos
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Rodrigo de A. Bustamante, Juan S. de Oliveira, and Brunno F. Santos. The first draft of the manuscript was written by Rodrigo de A. Bustamante, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Brunno Ferreira dos Santos.

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de Andrade Bustamante, R., de Oliveira, J.S. & dos Santos, B.F. Modeling biosurfactant production from agroindustrial residues by neural networks and polynomial models adjusted by particle swarm optimization. Environ Sci Pollut Res 30, 6466–6491 (2023). https://doi.org/10.1007/s11356-022-22481-3

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