论文标题

在大规模生产环境中,对增长,能源和资源动态的建模和最佳控制

Modeling and optimal control of growth, energy, and resource dynamics of Hermetia illucens in mass production environment

论文作者

Padmanabha, Murali, Kobelski, Alexander, Hempel, Arne-Jens, Streif, Stefan

论文摘要

现在在许多国家采用大规模生产昆虫幼虫,并且正在采用工业生产方法。尽管有关影响幼虫生长的因素和实验室设置中确定的最佳静态参数的文献,但对于工业生产过程,有必要确定轨迹,以使增长以及生产过程都是最佳的。为了在这项工作中实现这一目标,确定了其中一些重要的要求和挑战,并在基于模型的最佳控制设置中制定了自动化过程的目标。优化框架所必需的机械模型被推导为微分方程,描述了资源(饲料,水,O2等),能量和幼虫生物量的动态变化。此外,鉴定出与最终龄发育相对应的幼虫的代谢升高,也基于实验的观察结果进行建模。建模用于建模的质量和能量平衡方法可以在各个级别(例如幼虫体,增长培养基,生产环境和外部环境)中对质量和能量通量成分的定量和区别,同时将其适用于开放和封闭/反应堆的生产设置。最后,与生产设置的固定设定点操作相比,使用合成的最佳控制器生成的轨迹进行了测试,以显示资源消耗的显着降低。这项工作中介绍的结果不仅显示了机械模型的潜力及其在识别相关过程参数(例如反应器属性,诸如体积,热电导率,执行器能力之类的反应器属性)中的应用,而且最重要的是,最重要的是根据需要进行动态优化过程并根据需要调整过程目标。

Mass production of Hermetia illucens insect larvae is now being adopted in many countries and is taking an industrial production approach. Despite abundant literature on factors that affect larvae growth and the optimal static parameters identified in laboratory setup, for an industrial production process it is necessary to identify the trajectories such that the growth as well as the production process is optimal. To achieve this in this work, some of the important requirements and challenges involved thereof are identified and objectives of the automation process are formulated within a model based optimal control setup. Mechanistic models necessary for the optimization framework are derived as differential equations that describe the dynamic variation of resources (feed, water, O2 etc.), energy, and larval biomass. In addition, the elevated metabolic activity of larvae corresponding to the final instar development is identified and also modelled based on the observation from experiments. The mass and energy balance approach used in modelling enables the quantification and distinction of the mass and energy flux components in various levels (e.g. larvae body, growing medium, production environment, and external environment) while holding its applicability for both open and closed/reactor based production setups. Finally, the trajectories generated using the synthesized optimal controller are tested under different scenarios showcasing significant reduction in resource consumption compared to a fixed set-point operation of the production setup. Results presented in this work not only showcase the potential of the mechanistic models and their application in identifying the relevant process parameters (e.g. reactor properties such as volume, thermal conductivity, actuator capacities), but most importantly in optimizing the process dynamically and tuning the process objectives as desired.

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