DYNAMIC MODELS AND ALGORITHMS FOR OPTIMAL CONTROL IN BUILDING MATERIALS TECHNOLOGY
Rubrics: CONSTRUCTION MATERIALS AND PRODUCTS
Authors:
1.
National Research Moscow State University of Civil Engineering
Moscow, Russian Federation
Moscow, Russian Federation
Type:
Article
Pages:
from 21
to 36
Status:
In work
Subject area:
UDC 666.9.01
UDC 620.1
UDC 620.1
Language:
Russian
Keywords:
building materials, hardening kinetics, model predictive control, Industry 4.0, Construction 4.0, Kalman filter, digital twin
Abstract:
The modern production of building materials has to deal with the challenge of integrating fundamental physical and chemical knowledge of the material into operational automated control systems. The existing gap between static laboratory studies and simplified empirical models in automation is hindering the transition to the ‘Construction 4.0’ paradigm and intelligent quality management (Quality 4.0). The purpose of this study is to develop a universal quality control methodology that combines three key components, namely a fundamental physico-chemical model of the process, an algorithm for assessing the current state of the material based on indirect measurements, and an optimiser for control actions. The triad, designated as ‘Model – Assessment – Control’, allows the technological process to be viewed as a dynamic system with distributed parameters. A mathematical framework is presented, comprising heat and mass transfer equations and chemical kinetics, as well as modern state estimation methods (in particular, Kalman filters and particle filters) and optimal control algorithms, the Pontryagin maximum principle and reinforcement learning. The applicability of the methodology is demonstrated using the example of concrete mix transport. The proposed approach provides a basis for the creation of ‘smart’ manufacturing and digital twins in the construction industry.
The modern production of building materials has to deal with the challenge of integrating fundamental physical and chemical knowledge of the material into operational automated control systems. The existing gap between static laboratory studies and simplified empirical models in automation is hindering the transition to the ‘Construction 4.0’ paradigm and intelligent quality management (Quality 4.0). The purpose of this study is to develop a universal quality control methodology that combines three key components, namely a fundamental physico-chemical model of the process, an algorithm for assessing the current state of the material based on indirect measurements, and an optimiser for control actions. The triad, designated as ‘Model – Assessment – Control’, allows the technological process to be viewed as a dynamic system with distributed parameters. A mathematical framework is presented, comprising heat and mass transfer equations and chemical kinetics, as well as modern state estimation methods (in particular, Kalman filters and particle filters) and optimal control algorithms, the Pontryagin maximum principle and reinforcement learning. The applicability of the methodology is demonstrated using the example of concrete mix transport. The proposed approach provides a basis for the creation of ‘smart’ manufacturing and digital twins in the construction industry.
Keywords:
building materials, hardening kinetics, model predictive control, Industry 4.0, Construction 4.0, Kalman filter, digital twin
building materials, hardening kinetics, model predictive control, Industry 4.0, Construction 4.0, Kalman filter, digital twin
