Department of Biotechnology, Delft University of Technology, Delft, The Netherlands.
Laboratory of Optimization, Design and Advanced Control (LOPCA), School of Chemical Engineering, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.
Biotechnol Bioeng. 2019 Oct;116(10):2473-2487. doi: 10.1002/bit.27108. Epub 2019 Jul 24.
Syngas fermentation is one of the bets for the future sustainable biobased economies due to its potential as an intermediate step in the conversion of waste carbon to ethanol fuel and other chemicals. Integrated with gasification and suitable downstream processing, it may constitute an efficient and competitive route for the valorization of various waste materials, especially if systems engineering principles are employed targeting process optimization. In this study, a dynamic multi-response model is presented for syngas fermentation with acetogenic bacteria in a continuous stirred-tank reactor, accounting for gas-liquid mass transfer, substrate (CO, H ) uptake, biomass growth and death, acetic acid reassimilation, and product selectivity. The unknown parameters were estimated from literature data using the maximum likelihood principle with a multi-response nonlinear modeling framework and metaheuristic optimization, and model adequacy was verified with statistical analysis via generation of confidence intervals as well as parameter significance tests. The model was then used to study the effects of process conditions (gas composition, dilution rate, gas flow rates, and cell recycle) as well as the sensitivity of kinetic parameters, and multiobjective genetic algorithm was used to maximize ethanol productivity and CO conversion. It was observed that these two objectives were clearly conflicting when CO-rich gas was used, but increasing the content of H favored higher productivities while maintaining 100% CO conversion. The maximum productivity predicted with full conversion was 2 g·L ·hr with a feed gas composition of 54% CO and 46% H and a dilution rate of 0.06 hr with roughly 90% of cell recycle.
合成气发酵是未来可持续生物基经济的最佳选择之一，因为它有可能成为将废碳转化为乙醇燃料和其他化学品的中间步骤。与气化相结合，并进行适当的下游处理，它可能构成一种高效、有竞争力的途径，用于各种废物的增值利用，特别是如果采用系统工程原理来实现过程优化。在这项研究中，提出了一个用于在连续搅拌釜式反应器中进行产乙酸菌合成气发酵的动态多响应模型，该模型考虑了气液传质、底物（CO、H ）摄取、生物质生长和死亡、乙酸再同化以及产物选择性。使用最大似然原理和多响应非线性建模框架以及元启发式优化，从文献数据中估计了未知参数，并通过生成置信区间以及参数显著性检验进行统计分析来验证模型的充分性。然后，使用该模型研究了工艺条件（气体组成、稀释率、气体流速和细胞循环）以及动力学参数的敏感性的影响，并使用多目标遗传算法来最大化乙醇生产率和 CO 转化率。观察到，当使用富 CO 的气体时，这两个目标显然是冲突的，但增加 H 的含量有利于提高生产率，同时保持 100%的 CO 转化率。在完全转化的情况下，预测的最大生产率为 2g·L·hr，进料气体组成为 54%CO 和 46%H，稀释率为 0.06hr，大约 90%的细胞循环。
