Enhanced bioethanol production from potato peel waste: nano-assisted detoxification, co-fermentation with Saccharomyces cerevisiae and Pichia stipitis, and process scale-up.

Abstract

The environmental and climate impacts associated with fossil fuel consumption have led to the search for sustainable alternatives, including renewable, bio-based fuels and products from lignocellulosic biomasses. Lignocellulosic biomass, particularly agro-industrial residues like potato peels, has emerged as a promising feedstock for second-generation bioethanol production. However, the microbial conversion of lignocellulosic biomass into large-scale bioethanol production is challenged by several factors, including the formation of inhibitory compounds during biomass pretreatment, inefficient utilization of mixed sugars and low yield. Therefore, this study explored the co-fermentation of S. cerevisiae and P. stipitis, integrated with nanoparticle-based detoxification of inhibitory compounds, process kinetics and scale-up to enhance bioethanol production efficiency from potato peel waste (PPW) hydrolysates. In this study, the pretreated PPW hydrolysate was subjected to a Fe3O4 nanoparticle-assisted detoxification process modelling and optimization using Response Surface Methodology (RSM) for the simultaneous removal of five key fermentation inhibitors: furfural, 5-hydroxymethylfurfural (HMF), phenol, acetic acid, and formaldehyde. An artificial neural network (ANN) was employed to model the removal of inhibitors and identify key process variables. The RSM and ANN strategies were complemented with sensitivity analysis, adsorption-desorption kinetics, and thermodynamic studies to further elucidate the underlying detoxification mechanisms. The detoxified PPW hydrolysate was employed for bioethanol production, comparatively using simultaneous saccharification and fermentation (SSF) and separate hydrolysis and fermentation (SHF) strategies to establish the most efficient fermentation strategy. This was followed by substrate utilization, growth and bioethanol production kinetic studies. Thereafter, the simultaneous saccharification and co-fermentation (SSCF) process with S. cerevisiae and P. stipitis was modelled and optimized using the RSM to maximize sugar bioconversion for improved bioethanol production. The optimised SSCF was evaluated for preliminary scale-up with specific consideration given to reactor geometry, rheological behaviour, and hydrodynamic parameters. First, the pretreated PPW hydrolysate was subjected to optimized nanoparticle-assisted detoxification conditions, resulting in significant removal efficiencies for furfural (1.65-fold), 5-hydroxymethylfurfural (1.21-fold), phenol (1.95-fold), acetic acid (3.25-fold), and formaldehyde (1.55-fold), without sugar loss. Sensitivity analysis revealed that pH and initial inhibitor concentration were the critical influencing factors. The characteristic profile and adsorption mechanism of Fe3O4 nanoparticles (NPs) were elucidated using scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), UV spectrophotometry, X-ray diffraction (XRD), point of zero charge (PZC), and the Brunauer–Emmett–Teller (BET), suggesting surface adsorption and complexation as the primary detoxification mechanisms. The adsorption mechanisms were further substantiated through pseudo-first-order, pseudo-second-order, intra-particle diffusion, Elovich, Langmuir, Freundlich, Temkin and thermodynamic studies. The finding indicated chemisorptive interactions between Fe3O4 nanoparticles and inhibitors. Phenol and HMF adsorption best fitted the Langmuir model (R² = 0.997 and 0.966), respectively, suggesting monolayer adsorption on uniform surfaces. Acetic acid and formaldehyde followed the Freundlich model (R² = 0.985 and 0.980), respectively, indicating heterogeneous surface adsorption, while furfural fitted the Temkin model (R² = 0.970), reflecting moderate, energy-distributed adsorption. The Fe3O4 nanoadsorbent demonstrated a multi-site adsorption mechanism involving hydrogen bonding, π–π interactions, and electrostatic forces, consistent with chemisorption. Hence, the optimally detoxified hydrolysate was employed for bioethanol production. The SSF bioethanol production strategies demonstrated improved performance over SHF, achieving a saccharification efficiency of 88.95% and a bioethanol yield of 26 g/L. Consequently, the logistic function, modified Gompertz, Luedeking-Piret, and modified Luedeking-Piret performed on the SSF process further revealed improved metabolic performance that was predominantly growth-associated (α = 3.515 g P/g X and γ = 6.543 g S/g X), with a low cell maintenance coefficient (δ) value (0.0019 g S/g X h). This data indicated that S. cerevisiae relies strongly on efficient substrate availability and processability for optimal ethanol biosynthesis under detoxified conditions. However, the inefficient bioconversion of fermentable sugars in the detoxified hydrolysate necessitated the adoption of a co-fermentation strategy to enable simultaneous utilization of glucose and xylose present in the hydrolysate. For the SSCF process optimization, the optimal co-fermentation conditions were determined to be a zero-hour co-inoculation time, an inoculation ratio of 1:4 (S. cerevisiae to P. stipitis), and a 10% solid loading. Under these conditions, improved bioethanol concentration of 48.7 g/L, maximum specific growth rate (μmax) (0.752 h-1), bioethanol productivity (4.06 g/L/h), yield (0.505 g/g) and fermentation efficiency of 99% were achieved. Scaling the SSCF process from 1 L to 10 L while maintaining constant non-gassed power (0.012 W), impeller tip speed (0.35 m/s), temperature (35°C), and pH 5.78, ensured comparable bioethanol production efficiencies with productivities of 3.653 and 3.635 g/L/h and bioethanol concentrations of 43.68 g/L at 1 L and 10 L, respectively. A desirable pumping capacity (VP = 3.9 ˟ 10-4 m3/s) and a 10-fold reduction in Power-to-Volume ratio (P/VL= 1.2 W/m3) characterised the 10L scale, indicating a significant improvement in energy efficiency at pilot scale. The logistic model (R2 >0.938) yielded maximum specific growth rate (μmax) of 0.436 h−1 (1 L) and 0.415 h−1 (10 L), and maximum biomass concentration (Xmax) of 16.40 g/L (1 L) and 16.88 g/L (10 L). The modified Gompertz model (R2 >0.99) showed comparable maximum potential bioethanol concentration [Pm] (44.37 g/L for 1 L; 44.09 g/L for 10 L), maximum bioethanol production rate [rp,m] (6.68 g/L/h for 1 L; 6.63 g/L/h for 10 L), and lag time [tL] (2.94 h for 1 L; 2.69 h for 10 L). Interestingly, growth-associated product (α, 4.744 g P/g X) and substrate utilization (γ, 6.611 g S/g X) coefficients further aligned with experimental data (R2 >0.90), confirming model fitness across scales. This study presents a novel integrated strategy that combines dual optimisation of Fe3O4 nanoparticle-assisted detoxification of pretreated potato peel waste (PPW) hydrolysate and co-fermentation of detoxified PPW hydrolysate using S. cerevisiae and P. stipitis to enhance bioethanol production. The findings further demonstrated the efficiency and underlying mechanisms of Fe3O4 nanoparticles in improving the processability of hydrolysate, substrate affinity, substrate uptake as well as growth kinetics of S. cerevisiae and P. stipitis, thereby enhancing bioethanol productivity, yield and subsequent pilot scale-up. Underscoring the suitability of the developed process design for industrial implementation towards sustainable large-scale bioethanol production from agricultural residues.