Mechanistic simulation of batch acetone–butanol–ethanol (ABE) fermentation with in situ gas stripping using Aspen Plus™

Mathematical modeling of ABE fermentation on glucose substrate with Zn supplementation for enhanced butanol production

Supplementation or limitation of some micronutrients during acetone-butanol-ethanol (ABE) fermentation has led to improvement in butanol yield and productivity. A mechanistic model of ABE fermentation offers insights in understanding these complex interactions and improving productivity through optimal culture conditions. This study proposes a mechanistic kinetic model of ABE fermentation by two Clostridium Acetobutylicum strains, L7 and ATCC 824 using glucose as sole carbon source without zinc and with various zinc doses. The model incorporates enzyme regulation by zinc on several glycolytic, acidogenesis and solventogenesis enzymes. The model was fitted and validated to experimental data collected from the published literature. The simulated results were in compliance with the experimental data, most importantly indicating higher glucose consumption and butanol productivity when supplemented with zinc compared to the control culture. The average squared correlation factor (R2) between the experimental and the simulated results, without and with zinc, were 0.99 and 0.96 for glucose, and 0.89 and 0.95 for butanol, respectively. A sensitivity analysis performed on the fitted and validated model indicated that the glucose consumption and growth parameters most influenced the model outputs. The developed model can be used as a template for modeling ABE fermentation under different combinations of micronutrients that may offer improved butanol yield and productivity.

The Influence of Sugar Composition and pH Regulation in Batch and Continuous Acetone–Butanol–Ethanol Fermentation

Acetone–butanol–ethanol (ABE) fermentation is influenced by external conditions. This work aimed to study the influence of pH regulation on monosaccharide composition in batch and continuous fermentation processes to determine butanol production and productivity. Batch fermentations with ammonium acetate or calcium carbonate combined with minimum pH control (pH ≥ 4.8 or 5.1) were assessed with pure xylose and glucose/xylose mixtures (ratios of 1:1 and 3:1). Continuous two-stage fermentation was developed using plastic rings to retain the biomass. Although batch fermentations with pure xylose performed better without active minimum pH control with both buffers, minimum pH control was necessary to metabolize xylose in the presence of glucose. Xylose uptake was favored by the use of calcium carbonate and pH ≥ 5.1 at a ratio of 1:1, while ammonium acetate and a pH ≥ 4.8 was the best option for a 3:1 ratio. The best butanol production and productivity values with sugar mixtures in batch reactors were 8.8 g L−1 and 0.61 g L−1 h−1 with an ammonium acetate pH ≥ 4.8 (ratio 3:1). The glucose/xylose ratio combined with pH regulation thus modulated xylose metabolism and solvent production in batch modes. Immobilized cells combined with operating at D = 0.333 h−1 and pH regulation increased butanol productivity almost fourfold up to 2.4 ± 0.2 g L−1 h−1.

Sustainable Design Approach for Modeling Bioprocesses from Laboratory toward Commercialization: Optimizing Chitosan Production

Enhancing the biochemical supply chain towards sustainable development requires more efforts to boost technology innovation at early design phases and avoid delays in industrial biotechnology growth. Such a transformation requires a comprehensive step-wise procedure to guide bioprocess development from laboratory protocols to commercialization. This study introduces a process design framework to guide research and development (R&D) through this journey, bearing in mind the particular challenges of bioprocess modeling. The method combines sustainability assessment and process optimization based on process efficiency indicators, technical indicators, Life Cycle Assessment (LCA), and process optimization via Water Regeneration Networks (WRN). Since many bioprocesses remain at low Technology Readiness Levels (TRLs), the process simulation module was examined in detail to account for uncertainties, providing strategies for successful guidance. The sustainability assessment was performed using the geometric mean-based sustainability footprint metric. A case study based on Chitosan production from shrimp exoskeletons was evaluated to demonstrate the method’s applicability and its advantages in product optimization. An optimized scenario was generated through a WRN to improve water management, then compared with the case study. The results confirm the existence of a possible configuration with better sustainability performance for the optimized case with a sustainability footprint of 0.33, compared with the performance of the base case (1.00).

Comparison of Biobutanol Production Pathways via Acetone-Butanol-Ethanol Fermentation Using a Sustainability Exergy-Based Metric

The incorporation of sustainability aspects into the design of chemical processes has been increasing since the last century. Hence, there are several proposed methodologies and indicators to assess chemical facilities through process analysis techniques. A comprehensive assessment involving economic, environmental, safety, and exergy parameters of two alternatives for butanol production from Manihot esculenta Crantz (cassava waste) is presented in this study. The modeling of process topologies involved using Aspen Plus software. Topology 1 generated a product flow rate of 316,477 t/y of butanol, while this value was 367,037 t/y for topology 2. Both processes used a feed flow of 3,131,439 t/y of biomass. This study used seven technical indicators to evaluate both alternatives, which include the return of investment, discounted payback period, global warming potential, renewability material index, inherent safety index, exergy efficiency, and exergy of waste ratio. Otherwise, this study implemented an aggregate index to assess overall sustainability performance. The results revealed that topology 2 presented higher economic normalized scores for evaluated indicators, but the most crucial difference between these designs came from the safety and exergetic indexes. Topology 1 and topology 2 obtained weighted scores equaling to 0.48 and 0.53; therefore, this study found that the second alternative gives a more sustainable design for butanol production under evaluated conditions.