Department of Chemical Engineering and Environmental Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia.
Department of Process Engineering, University of Djilali Bounaama, Khemis Miliana 44001, Algeria.
Biomolecules. 2019 Jul 26;9(8):308. doi: 10.3390/biom9080308.
Modelling has recently become a key tool to promote the bioethanol industry and to optimise the fermentation process to be easily integrated into the industrial sector. In this context, this study aims at investigating the applicability of two mathematical models (Andrews and Monod) for molasses fermentation. The kinetics parameters for Monod and Andrews were estimated from experimental data using Matlab and OriginLab software. The models were simulated and compared with another set of experimental data that was not used for parameters' estimation. The results of modelling showed that μ = 0.179 1/h and K = 11.37 g.L for the Monod model, whereas μ = 0.508 1/h, K = 47.53 g.L and K = 181.01 g.L for the Andrews model, which are too close to the values reported in previous studies. The validation of both models showed that the Monod model is more suitable for batch fermentation modelling at a low concentration, where the highest R squared was observed at S = 75 g.L with an R squared equal to 0.99956, 0.99954, and 0.99859 for the biomass, substrate, and product concentrations, respectively. In contrast, the Andrews model was more accurate at a high initial substrate concentration and the model data showed a good agreement compared to the experimental data of batch fermentation at S = 225 g.L, which was reflected in a high R squared with values 0.99795, 0.99903, and 0.99962 for the biomass, substrate, and product concentrations respectively.
最近,模型已成为推动生物乙醇产业发展和优化发酵过程以使其易于融入工业领域的关键工具。在这种情况下,本研究旨在研究两种数学模型(Andrews 和 Monod)在糖蜜发酵中的适用性。使用 Matlab 和 OriginLab 软件从实验数据中估算了 Monod 和 Andrews 的动力学参数。对模型进行了模拟,并将其与另一组未用于参数估计的实验数据进行了比较。建模结果表明,Monod 模型的 μ = 0.179 1/h 和 K = 11.37 g/L,而 Andrews 模型的 μ = 0.508 1/h、K = 47.53 g/L 和 K = 181.01 g/L,这些值与先前研究报告的值非常接近。对两种模型的验证表明,Monod 模型更适合低浓度下的分批发酵建模,在 S = 75 g/L 时,R 平方最高,为 0.99956,生物质、底物和产物浓度的 R 平方分别为 0.99954 和 0.99859。相比之下,Andrews 模型在初始底物浓度较高时更为准确,并且模型数据与 S = 225 g/L 下的分批发酵实验数据吻合良好,这反映在 R 平方值较高,分别为 0.99795、0.99903 和 0.99962。