Formal Properties of the Digital Twin – Implications for Learning, optimization, and Control

Author(s):  
Constantin Cronrath ◽  
Ludvig Ekstrom ◽  
Bengt Lennartson
2020 ◽  
Vol 209 ◽  
pp. 02029
Author(s):  
Nikita Tomin ◽  
Victor Kurbatsky ◽  
Vadim Borisov ◽  
Sergey Musalev

The paper proposes a concept of building a digital twin based on the reinforcement learning method. This concept allows implementing an accurate digital model of an electrical network with bidirectional automatic data exchange, used for modeling, optimization, and control. The core of such a model is an agent (potential digital twin). The agent, while constantly interacting with a physical object (electrical grid), searches for an optimal strategy for active network management, which involves short-term strategies capable of controlling the power supplied by generators and/ or consumed by the load to avoid overload or voltage problems. Such an agent can verify its training with the initial default policy, which can be considered as a teacher’s advice. The effectiveness of this approach is demonstrated on a test 77-node scheme and a real 17-node network diagram of the Akademgorodok microdistrict (Irkutsk) according to the data from smart electricity meters.


Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1325
Author(s):  
Leon S. Klepzig ◽  
Alex Juckers ◽  
Petra Knerr ◽  
Frank Harms ◽  
Jochen Strube

Lyophilization stabilizes formulated biologics for storage, transport and application to patients. In process design and operation it is the link between downstream processing and with final formulation to fill and finish. Recent activities in Quality by Design (QbD) have resulted in approaches by regulatory authorities and the need to include Process Analytical Technology (PAT) tools. An approach is outlined to validate a predictive physical-chemical (rigorous) lyophilization process model to act quantitatively as a digital twin in order to allow accelerated process design by modeling and to further-on develop autonomous process optimization and control towards real time release testing. Antibody manufacturing is chosen as a typical example for actual biologics needs. Literature is reviewed and the presented procedure is exemplified to quantitatively and consistently validate the physical-chemical process model with aid of an experimental statistical DOE (design of experiments) in pilot scale.


2018 ◽  
Vol 69 (10) ◽  
pp. 2633-2637
Author(s):  
Raluca Dragomir ◽  
Paul Rosca ◽  
Cristina Popa

The main objectives of the present paper are to adaptation the five-kinetic model of the catalytic cracking process and simulation the riser to predicts the FCC products yields when one of the major input variable of the process is change. The simulation and adaptation are based on the industrial data from Romanian refinery. The adaptation is realize using a computational method from Optimization Toolbox from Matlab programming language. The new model can be used for optimization and control of FCC riser.


1989 ◽  
Vol 24 (3) ◽  
pp. 463-477
Author(s):  
Stephen G. Nutt

Abstract Based on discussions in workshop sessions, several recurring themes became evident with respect to the optimization and control of petroleum refinery wastewater treatment systems to achieve effective removal of toxic contaminants. It was apparent that statistical process control (SPC) techniques are finding more widespread use and have been found to be effective. However, the implementation of real-time process control strategies in petroleum refinery wastewater treatment systems is in its infancy. Considerable effort will need to be expended to demonstrate the practicality of on-line sensors, and the utility of automated process control in petroleum refinery wastewater treatment systems. This paper provides a summary of the discussions held at the workshop.


Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 400 ◽  
Author(s):  
Zelin Nie ◽  
Feng Gao ◽  
Chao-Bo Yan

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.


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