Model-based optimization and scale-up of multi-feed simultaneous saccharification and co-fermentation of steam pre-treated lignocellulose enables high gravity ethanol production

Abstract

BackgroundHigh content of water-insoluble solids (WIS) is required for simultaneous saccharification and co-fermentation (SSCF) operations to reach the high ethanol concentrations that meet the techno-economic requirements of industrial-scale production. The fundamental challenges of such processes are related to the high viscosity and inhibitor contents of the medium. Poor mass transfer and inhibition of the yeast lead to decreased ethanol yield, titre and productivity. In the present work, high-solid SSCF of pre-treated wheat straw was carried out by multi-feed SSCF which is a fed-batch process with additions of substrate, enzymes and cells, integrated with yeast propagation and adaptation on the pre-treatment liquor. The combined feeding strategies were systematically compared and optimized using experiments and simulations.ResultsFor high-solid SSCF process of SO2-catalyzed steam pre-treated wheat straw, the boosted solubilisation of WIS achieved by having all enzyme loaded at the beginning of the process is crucial for increased rates of both enzymatic hydrolysis and SSCF. A kinetic model was adapted to simulate the release of sugars during separate hydrolysis as well as during SSCF. Feeding of solid substrate to reach the instantaneous WIS content of 13 % (w/w) was carried out when 60 % of the cellulose was hydrolysed, according to simulation results. With this approach, accumulated WIS additions reached more than 20 % (w/w) without encountering mixing problems in a standard bioreactor. Feeding fresh cells to the SSCF reactor maintained the fermentation activity, which otherwise ceased when the ethanol concentration reached 40–45 g L−1. In lab scale, the optimized multi-feed SSCF produced 57 g L−1 ethanol in 72 h. The process was reproducible and resulted in 52 g L−1 ethanol in 10 m3 scale at the SP Biorefinery Demo Plant.ConclusionsSSCF of WIS content up to 22 % (w/w) is reproducible and scalable with the multi-feed SSCF configuration and model-aided process design. For simultaneous saccharification and fermentation, the overall efficiency relies on balanced rates of substrate feeding and conversion. Multi-feed SSCF provides the possibilities to balance interdependent rates by systematic optimization of the feeding strategies. The optimization routine presented in this work can easily be adapted for optimization of other lignocellulose-based fermentation systems.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-016-0500-7) contains supplementary material, which is available to authorized users.

Bacillus coagulans MA-13: a promising thermophilic and cellulolytic strain for the production of lactic acid from lignocellulosic hydrolysate

“…The strain robustness was tested by cultivations in the liquid fraction (hydrolysate) of a wheat straw slurry, pre-treated by sulfuric acid-catalysed hydrolysis and steam explosion as described elsewhere [ 43 ] and containing inhibitors such as 3.8 g/l of acetic acid, 4.0 g/l of furfural and 1.4 g/l of 5-hydroxymethylfurfural (HMF); concentrations were determined by HPLC (see below). The pH of the hydrolysate was adjusted to 5.5 using concentrated NaOH, before being mixed with the above-mentioned medium and molasses as a source of sucrose [ 43 ] to achieve concentrations of hydrolysate ranging from 10 to 80% (v/v). Prior to microscale cultivations, B. coagulans MA-13 was grown into the stationary phase in LB medium at 55 °C and shaking speed of 180 rpm.…”

Ethanol Production from High Solid Loading of Rice Straw by Simultaneous Saccharification and Fermentation in a Non-Conventional Reactor

“…Only the overall process efficiency obtained in the present study (67%) can still be improved. In order to do that, efforts will Finally, a global analysis of the results obtained in the present study compared to the current literature on ethanol production by SSF in fed-batch mode using different biomass types at high solids content ( [17,31,33,[36][37][38][39] -Table 9) revealed that the strategy proposed here is very promising. As can be seen, Zhao et al [36] and Zhang et al [17] achieved the highest ethanol titers (80.0 and 84.7 g/L) using pre-treated sugarcane bagasse and corncob, respectively.…”

Multi-Scale Variability Analysis of Wheat Straw-Based Ethanol Biorefineries Identifies Bioprocess Designs Robust Against Process Input Variations

“…A macro-kinetic model was used to describe the bioprocess. The model was developed and validated by Wang et al (2016) to simulate batch and multi-feed SSF processes based on steam pretreated wheat straw at 5-15 filter paper units (FPU)·g −1 WIS and 0.02 g Cells ·g −1 WIS at maximally 13% water insoluble solids (WIS). Multi-feed SSF processes enable efficient mixing and high cell viabilities as solids and cells are added at discrete times with sufficient liquefaction in between.…”

“…Due to the solid additions and their subsequent hydrolysis, the cumulative WIS is higher than the 13% maximum operating WIS. In the case study the simulated multi-feed process conditions resembled the experimental conditions of a demo plant run at an enzyme activity of 9.3 FPU·g −1 WIS , a cell load of 0.02 g Cells ·g −1 WIS and a final cumulative WIS of 21.85%, with cell additions at 0, 12, 24, 48, and 72 h and solid additions at 0, 4, 12, 24, 48, and 72 h (Wang et al, 2016, Figure 6). Batch processes were simulated at identical relative cell and enzymatic activity loadings, but at 13% final WIS, all of which was added initially.…”

“…The framework was developed with the objective to systematically assess the effects of input variations on the performance indicators of a wheat straw-based ethanol biorefinery. A previously published bioprocess model describing simultaneous saccharification and fermentation to ethanol (Wang et al, 2016) was integrated with flowsheet, TEA and LCA models to form a multi-scale model describing the biorefinery. The validity of the simulated results at bioprocess scale was analyzed by local sensitivity and uncertainty analyses.…”