A Simple Biorefinery Concept to Produce 2G-Lactic Acid from Sugar Beet Pulp (SBP): A High-Value Target Approach to Valorize a Waste Stream.

Lactic acid is a high-value molecule with a vast number of applications. Its production in the biorefineries model is a possibility for this sector to aggregate value to its production chain. Thus, this investigation presents a biorefinery model based on the traditional sugar beet industry proposing an approach to produce lactic acid from a waste stream. Sugar beet is used to produce sugar and ethanol, and the remaining pulp is sent to animal feed. Using Bacillus coagulans in a continuous fermentation, 2781.01 g of lactic acid was produced from 3916.91 g of sugars from hydrolyzed sugar beet pulp, with a maximum productivity of 18.06 g L-1h-1. Without interfering in the sugar production, ethanol, or lactic acid, it is also possible to produce pectin and phenolic compounds in the biorefinery. The lactic acid produced was purified by a bipolar membrane electrodialysis and the recovery reached 788.80 g/L with 98% w/w purity.

Kinetic modeling and parameter estimation in a tower bioreactor for bioethanol production.

In this work, a systematic method to support the building of bioprocess models through the use of different optimization techniques is presented. The method was applied to a tower bioreactor for bioethanol production with immobilized cells of Saccharomyces cerevisiae. Specifically, a step-by-step procedure to the estimation problem is proposed. As the first step, the potential of global searching of real-coded genetic algorithm (RGA) was applied for simultaneous estimation of the parameters. Subsequently, the most significant parameters were identified using the Placket-Burman (PB) design. Finally, the quasi-Newton algorithm (QN) was used for optimization of the most significant parameters, near the global optimum region, as the initial values were already determined by the RGA global-searching algorithm. The results have shown that the performance of the estimation procedure applied in a deterministic detailed model to describe the experimental data is improved using the proposed method (RGA-PB-QN) in comparison with a model whose parameters were only optimized by RGA.