Integration of mathematical and experimental modeling for sustainable phycobiliprotein production via fed-batch cultures

Q1 Social Sciences
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Abstract

The production of phycobiliproteins, such as cyanobacterial phycocyanins, is a growing interest due to their diverse industrial and biotechnological applications. This study focuses on optimizing phycocyanin production using the strain Potamosiphon sp. through experimental techniques and mathematical modeling in fed-batch cultures. The methodology applied includes determining the kinetic constants by linearizing the Monod equation evaluating the concentrations of biomass, C-phycocyanin (C-PC), nitrates (NO3), and phosphates (PO4). A mathematical model of periodic fed-batch feeding was subsequently established, applying mass conservation principles and evaluating the accuracy of the Monod, Contois, Moser, and Tessier models. The results indicate that phycocyanin production is highly dependent on phosphorus and nitrogen concentrations, with optimal conversion observed at specific levels of these elements (0.832 for phosphorus and 0.805 for nitrogen in terms of C-PC and biomass, respectively). The Tessier model demonstrated the highest accuracy in predicting production and optimizing operational conditions, with a Mean Squared Error (MSE) of 0.005000 for biomass production, 0.200000 for C-PC production, and 0.000010 for substrate consumption. It also achieved high R² values of 0.980 for biomass, 0.999 for C-PC production, and 0.997 for substrate consumption. It presented the lowest Akaike Information Criterion (AIC) scores, indicating its robustness and reliability in modeling these processes and manipulating cultivation conditions and providing adequate nutrition allowed for achieving growth rates of 1.23 g/L and a C-PC concentration of 37 mg/L, which are essential for industrial applications such as natural colorants and antioxidants, among others.
整合数学模型和实验模型,通过分批进行喂养培养实现可持续藻类蛋白生产
蓝藻藻蓝蛋白等藻蓝蛋白的生产因其在工业和生物技术领域的广泛应用而日益受到关注。本研究的重点是通过实验技术和数学建模,在喂养批次培养中优化利用 Potamosiphon sp.菌株生产藻蓝蛋白的过程。采用的方法包括通过对生物量、C-藻蓝蛋白(C-PC)、硝酸盐(NO3)和磷酸盐(PO4)浓度进行评估的莫诺方程线性化来确定动力学常数。随后,应用质量守恒原理建立了周期性分批投喂的数学模型,并评估了 Monod、Contois、Moser 和 Tessier 模型的准确性。结果表明,藻蓝蛋白的生产高度依赖于磷和氮的浓度,在这两种元素达到特定水平时(就 C-PC 和生物量而言,磷和氮的最佳转化率分别为 0.832 和 0.805)。Tessier 模型在预测产量和优化操作条件方面表现出最高的准确性,生物量产量的平均平方误差(MSE)为 0.005000,C-PC 产量为 0.200000,基质消耗量为 0.000010。生物量的 R² 值也很高,分别为 0.980、0.999 和 0.997。该模型的 Akaike 信息标准(AIC)得分最低,表明其在模拟这些过程时的稳健性和可靠性,通过调节培养条件和提供充足的营养,可实现 1.23 克/升的生长率和 37 毫克/升的 C-PC 浓度,这对于天然着色剂和抗氧化剂等工业应用至关重要。
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来源期刊
CiteScore
8.40
自引率
0.00%
发文量
100
审稿时长
33 weeks
期刊介绍: The journal has a particular interest in publishing papers on the unique issues facing chemical engineering taking place in countries that are rich in resources but face specific technical and societal challenges, which require detailed knowledge of local conditions to address. Core topic areas are: Environmental process engineering • treatment and handling of waste and pollutants • the abatement of pollution, environmental process control • cleaner technologies • waste minimization • environmental chemical engineering • water treatment Reaction Engineering • modelling and simulation of reactors • transport phenomena within reacting systems • fluidization technology • reactor design Separation technologies • classic separations • novel separations Process and materials synthesis • novel synthesis of materials or processes, including but not limited to nanotechnology, ceramics, etc. Metallurgical process engineering and coal technology • novel developments related to the minerals beneficiation industry • coal technology Chemical engineering education • guides to good practice • novel approaches to learning • education beyond university.
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