Abstract

Scaling up bioprocesses from the experimental to the pilot or industrial scale involves heuristics and scale relationships that are far from the specific phenomena and are usually not connected to the experimental data. In complex systems, the scaling-up methodology must connect the experimental data with the tools of engineering design. In this work, a two-stage gold bioleaching process was used as a case study to develop a mathematical model of bioprocess scaling that combines the design of experiments with dimensional analysis using the Buckingham Pi theorem to formulate a predictive model that allows scaling up bioprocesses. It was found that the C/N, C/K, and T/C ratios are dimensionless factors that can explain the behavior of a system. Using the Pearson Product–Moment bivariate analysis, it was found that the dimensionless factors C/N and C/K were correlated with the leaching potential of the fermented broth at 1060 cm−1. With these results, a non-linear logarithmic model based on dimensionless parameters was proposed to explain the behavior of the system with a correlation coefficient of R2 = 0.9889, showing that the optimal conditions to produce fermented broth comprised a C/N ratio close to 50 and a C/K ratio close to 800, which allows predicting the scaling of the bioprocess.

Highlights

  • The global bioprocess market surpassed USD 180 million in 2019 and is forecast to be valued at USD 360 million by 2024, with an annual growth of 14.6% [1]

  • 25−2 fractional factorial design of experiments were applied to evaluate the relevant factors in the fermentation stage

  • The use of design of experiments as statistical methods for the optimization of culture media in the fermentation process can overcome the limitations of the classical methodology, which evaluates one factor at a time and can be a valuable tool for the optimization of the production of metabolites

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Summary

Introduction

The global bioprocess market surpassed USD 180 million in 2019 and is forecast to be valued at USD 360 million by 2024, with an annual growth of 14.6% [1]. The number of industrial bioprocesses is growing due to the increased market demand for the bulk production of products developed through fermentation to produce various relevant compounds that have applications in the chemical industry, biofuels, materials, nutritional ingredients, health products, food, and pharmaceutical products [2]. During the development and scale-up of the bioprocess, a commercial reduction between 10% and 30% occurs in performance, mainly associated with mass and heat transfer issues, in addition to mixing difficulties in industrial-scale bioreactors, in which a subpopulation of microorganisms occurs with reduced efficiency and production capacity, a phenomenon often called “population heterogeneity”. FFoorrththeefefremrmenentattaitoionnstsatgaeg,et,hteheopoepreartaiotinoanlaclocnodnidtiiotinosnws werereveavlualautaedtedusuinsginag 2a5−225−2 frfaractcitoionnalaflafcatcotroiariladl edseigsnig.nT.hTehiendinepdenpdeenndtevnatrviaabrlieasbwleserwe cearerbcoanrbsounrcsoeu(Crc)e, n(iCtr)o, gneintro(Nge),n p(oNta),sspioutmass(Kiu)m, th(Ke )a,dthdeitaiodndittriaocneterlaecme enletms (eTn)t,sa(nTd), aagnidtaatgioitnat(iAo)n, (inA)t,eirnmtseromf ms oefcmhaencihcanl oircaleorartaioenramtioinximngix, iansgs,haoswshnoiwnnTianbTlea1b.leT1h.eThreesrpeosnpsoenvsearviarbilaebsleins itnhitshisstastgaegwe wereer:ep: pHH (M(Meetttlteler–r–TTooleldedooppHHmmeteetre)r,)p, prerseesnenceceoof fpprortoetieninbbyyBBiuiurertetmmetehthooddoolologgyy[1[91]9,]a, nanddoorgrgananicic aaccididssqquuaanntitfiifcicaatitoionn(c(ictirtircic, ,ooxxaalilcic, ,mmaalilcic, ,aannddacaecteitcicacaicdids)s)bybyhihgihg-hp-perefrofromrmanancecelilqiquuidid cchhrorommaatotoggrraapphhyyiinnaann HHPPLLCC (Shimadzu Coorrppoorraattiioonn,,UUSSAA))eeqquuipipppeeddwwitihthaaUUVV/V/IVSIdSidoioddeeaarrarayyddeetetecctotor—r—DDAADDaatt221100nnmm

C Source
Mathematical Model
Bioprocess
Estimated Effects
Dimensional Analysis Using the Buckingham Pi Theorem
Experimental Results Obtained from the Correlation of the Mathematical Model
Results of the Mathematical Model
Conclusions
Patents
Full Text
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