标题： 黑水虻在大规模生产环境中的生长、能量和资源动力学的建模与最优控制 显示英文标题

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

摘要： 黑水虻幼虫的大规模生产已在许多国家采用，并采取了工业生产的方式。 尽管文献中已有大量关于影响幼虫生长的因素以及实验室条件下确定的最佳静态参数的研究，但对于工业生产过程而言，有必要确定轨迹，使生长以及生产过程达到最优。 为了实现这一目标，在本研究中，识别了其中涉及的一些重要要求和挑战，并在基于模型的最优控制框架内制定了自动化过程的目标。 优化框架所需的机械模型被推导为描述资源（饲料、水、O2等）、能量和幼虫生物量动态变化的微分方程。 此外，还根据实验观察识别并建模了对应于最终龄期发育的幼虫代谢活动升高现象。 建模中使用的质量和能量平衡方法能够量化和区分各种层次（例如幼虫体内、培养基、生产环境和外部环境）中的质量和能量通量成分，同时适用于开放式和封闭式/反应器式生产设置。 最后，在不同场景下测试了使用综合最优控制器生成的轨迹，结果显示与生产装置的固定设定点操作相比，资源消耗显著减少。 本研究中呈现的结果不仅展示了机械模型及其在识别相关工艺参数（例如反应器特性如体积、热传导率、执行器能力）方面的潜力，更重要的是展示了它们在动态优化工艺和调整工艺目标方面的应用价值。 显示英文摘要

摘要： 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.