{"title":"基于混合强度的管状光生物反应器设计","authors":"","doi":"10.1016/j.compag.2024.109380","DOIUrl":null,"url":null,"abstract":"<div><p>A novel design methodology for tubular photobioreactors (TPBRs) for the mass culture of microalgae that can account for mixing intensity is presented. To date, TPBRs have been mainly designed and operated under the assumption of perfect mixing with regard to photosynthesis performance (light integration regime). In this work we show that this simplification has been leads to significant errors in the prediction of the optimal dilution rate and biomass productivity. To this end, computational fluid dynamics and light distribution model have been employed to calculate the trajectories and light histories I(t) of a microalgal cell population represented by 50 particles of 5 μm diameter. The density of the microalgal cells was set at 1000 kg m<sup>−3</sup> and the tube diameters (D) were 14, 24, 44, 64 and 84 mm, with the circulation velocities (v) ranging from 0.4 to 1 m s<sup>-1</sup>. This has been coupled to a dynamic photosynthesis model in order to calculate the average photosynthetic response and hence the integration factors in TPBRs. It has been demonstrated that for a generic microalgal strain, the use of the light integration simplification (Γ = 1) would result in the prediction of an optimal dilution rate of 0.0315 h<sup>−1</sup> (for D = 14 mm and v = 0.4 m/s as an example), which would lead to an actual biomass productivity of 182.5 g biomass m<sup>−3</sup>h<sup>−1</sup> if the predicted integration factor (Γ = 0.578) is used whereas the newly proposed method predicts an optimal dilution rate of 0.0125 h<sup>−1</sup> and a biomass productivity of 362.3 g biomass m<sup>−3</sup>h<sup>−1</sup>. This demonstrates that simplifying the light integration regime is inadequate for TPBRs design and operation, resulting in significant inaccuracies. The estimation charts and regressions proposed in this work to estimate actual integration factors will enable the development of an optimization method for TPBRs based on mixing intensity.</p></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tubular photobioreactor design based on mixing intensity\",\"authors\":\"\",\"doi\":\"10.1016/j.compag.2024.109380\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A novel design methodology for tubular photobioreactors (TPBRs) for the mass culture of microalgae that can account for mixing intensity is presented. 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It has been demonstrated that for a generic microalgal strain, the use of the light integration simplification (Γ = 1) would result in the prediction of an optimal dilution rate of 0.0315 h<sup>−1</sup> (for D = 14 mm and v = 0.4 m/s as an example), which would lead to an actual biomass productivity of 182.5 g biomass m<sup>−3</sup>h<sup>−1</sup> if the predicted integration factor (Γ = 0.578) is used whereas the newly proposed method predicts an optimal dilution rate of 0.0125 h<sup>−1</sup> and a biomass productivity of 362.3 g biomass m<sup>−3</sup>h<sup>−1</sup>. This demonstrates that simplifying the light integration regime is inadequate for TPBRs design and operation, resulting in significant inaccuracies. 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引用次数: 0
摘要
本文介绍了一种用于大规模培养微藻的管式光生物反应器(TPBR)的新型设计方法,该方法可考虑混合强度。迄今为止,TPBR 的设计和运行主要是在假设光合作用性能(光整合制度)完全混合的情况下进行的。在这项工作中,我们发现这种简化会导致在预测最佳稀释率和生物量生产率时出现重大误差。为此,我们采用了计算流体动力学和光分布模型来计算由 50 个直径为 5 μm 的颗粒代表的微藻细胞群的轨迹和光历史 I(t)。微藻细胞的密度设定为 1000 kg m-3,管径 (D) 分别为 14、24、44、64 和 84 mm,循环速度 (v) 为 0.4 至 1 m s-1。该模型与动态光合作用模型相结合,以计算光合作用的平均响应,从而计算 TPBR 的整合因子。研究表明,对于一般的微藻菌株,使用光整合简化(Γ = 1)可预测最佳稀释率为 0.0315 h-1(以 D = 14 mm 和 v = 0.如果使用预测的积分因子(Γ = 0.578),则实际生物量生产率为 182.5 g 生物量 m-3h-1,而新提出的方法预测的最佳稀释率为 0.0125 h-1,生物量生产率为 362.3 g 生物量 m-3h-1。这表明,简化光整合制度对于热塑性生物还原反应器的设计和运行是不够的,会导致严重的误差。本研究提出的估算图和回归法估算实际积分因子,将有助于开发基于混合强度的 TPBR 优化方法。
Tubular photobioreactor design based on mixing intensity
A novel design methodology for tubular photobioreactors (TPBRs) for the mass culture of microalgae that can account for mixing intensity is presented. To date, TPBRs have been mainly designed and operated under the assumption of perfect mixing with regard to photosynthesis performance (light integration regime). In this work we show that this simplification has been leads to significant errors in the prediction of the optimal dilution rate and biomass productivity. To this end, computational fluid dynamics and light distribution model have been employed to calculate the trajectories and light histories I(t) of a microalgal cell population represented by 50 particles of 5 μm diameter. The density of the microalgal cells was set at 1000 kg m−3 and the tube diameters (D) were 14, 24, 44, 64 and 84 mm, with the circulation velocities (v) ranging from 0.4 to 1 m s-1. This has been coupled to a dynamic photosynthesis model in order to calculate the average photosynthetic response and hence the integration factors in TPBRs. It has been demonstrated that for a generic microalgal strain, the use of the light integration simplification (Γ = 1) would result in the prediction of an optimal dilution rate of 0.0315 h−1 (for D = 14 mm and v = 0.4 m/s as an example), which would lead to an actual biomass productivity of 182.5 g biomass m−3h−1 if the predicted integration factor (Γ = 0.578) is used whereas the newly proposed method predicts an optimal dilution rate of 0.0125 h−1 and a biomass productivity of 362.3 g biomass m−3h−1. This demonstrates that simplifying the light integration regime is inadequate for TPBRs design and operation, resulting in significant inaccuracies. The estimation charts and regressions proposed in this work to estimate actual integration factors will enable the development of an optimization method for TPBRs based on mixing intensity.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.