TY - GEN
T1 - Greenhouse Industry 4.0 – Digital Twin Technology for Commercial Greenhouses
AU - Howard, Daniel Anthony
AU - Ma, Zheng
AU - Veje, Christian
AU - Clausen, Anders
AU - Aaslyng, Jesper Peter Mazanti
AU - Jørgensen, Bo Nørregaard
N1 - Conference code: 1
PY - 2021/9/24
Y1 - 2021/9/24
N2 - The 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.
AB - The 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.
KW - Greenhouse
KW - Industry 4.0
KW - Digital Twin
KW - Energy Flexibility
KW - Artificial Intelligence
UR - https://youtu.be/EyBqsKfwGCc
U2 - 10.1186/s42162-021-00161-9
DO - 10.1186/s42162-021-00161-9
M3 - Conference article
AN - SCOPUS:85115602271
SN - 2520-8942
VL - 4
JO - Energy Informatics
JF - Energy Informatics
IS - Suppl. 2
M1 - 37
T2 - 1st Energy Informatics Academy Conference Asia
Y2 - 29 May 2021 through 30 May 2021
ER -