Author:
Howard Daniel Anthony,Ma Zheng,Veje Christian,Clausen Anders,Aaslyng Jesper Mazanti,Jørgensen Bo Nørregaard
Abstract
AbstractThe project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.
Publisher
Springer Science and Business Media LLC
Reference45 articles.
1. Afroz Z, Shafiullah GM, Urmee T, Higgins G (2018) Modeling techniques used in building HVAC control systems: a review. Renew Sust Energ Rev 83:64–84. https://doi.org/10.1016/j.rser.2017.10.044
2. AnyLogic (2017) Simulation software comparison. Available from: https://www.anylogic.com/blog/simulation-software-tool-comparison/. Accessed 18 May 2021
3. Arendt K, Jradi M, Shaker HR, Veje C (2018a) Comparative analysis of white-, gray- and black-box models for thermal simulation of indoor environment: teaching building case study. the 2018 Building Performance Modeling Conference and SimBuild co-organized by ASHRAE and IBPSA-USA: ASHRAE
4. Arendt K, Jradi M, Wetter M, Veje C (2018b) ModestPy: an open-source python tool for parameter estimation in functional mock-up units. the 1st American Modelica Conference 2018: Modelica Association and Linköping University Electronic Press
5. Blum DH, Arendt K, Rivalin L, Piette MA, Wetter M, Veje CT (2019) Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems. Appl Energy 236:410–425. https://doi.org/10.1016/j.apenergy.2018.11.093
Cited by
22 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献