scholarly journals Thermodynamic integration by neural network potentials based on first-principles dynamic calculations

2019 ◽  
Vol 100 (21) ◽  
Author(s):  
Shogo Fukushima ◽  
Eisaku Ushijima ◽  
Hiroyuki Kumazoe ◽  
Akihide Koura ◽  
Fuyuki Shimojo ◽  
...  
Keyword(s):  
Neural Network ◽  
First Principles ◽  
Thermodynamic Integration

A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model

2004 ◽  
Vol 126 (1) ◽  
pp. 144-153 ◽  
Author(s):  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. E. Tobler
Keyword(s):  
Neural Network ◽  
First Principles ◽  
Physical Parameters ◽  
Neural Net ◽  
Asperity Contact ◽  
Neural Network Approach ◽  
Hybrid Neural Network ◽  
Friction Component ◽  
Improved Performance ◽  
Viscous Torque

In this research, a new hybrid neural network is developed to model engagement behaviors of automotive transmission wet friction component. Utilizing known first principles on the physics of engagement, special modules are created to estimate viscous torque and asperity contact torque as preprocessors to a two-layer neural network. Inside these modules, all the physical parameters are represented by neurons with various activation functions derived from first principles. These new features contribute to the improved performance and trainability over a conventional two-layer network model. Both the hybrid and conventional neural net models are trained and tested with experimental data collected from an SAE#2 test stand. The results show that the performance of the hybrid model is much superior to that of the conventional model. It successfully captures detailed characteristics of the friction component engagement torque as a function of time over a wide operating range.

scholarly journals Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 890 ◽  
Author(s):  
Zhihao Zhang ◽  
Zhe Wu ◽  
David Rincon ◽  
Panagiotis Christofides
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
First Principles ◽  
Chemical Process ◽  
Feed Forward Neural Network ◽  
Time Optimization ◽  
Real Time Optimization

Machine learning has attracted extensive interest in the process engineering field, due to the capability of modeling complex nonlinear process behavior. This work presents a method for combining neural network models with first-principles models in real-time optimization (RTO) and model predictive control (MPC) and demonstrates the application to two chemical process examples. First, the proposed methodology that integrates a neural network model and a first-principles model in the optimization problems of RTO and MPC is discussed. Then, two chemical process examples are presented. In the first example, a continuous stirred tank reactor (CSTR) with a reversible exothermic reaction is studied. A feed-forward neural network model is used to approximate the nonlinear reaction rate and is combined with a first-principles model in RTO and MPC. An RTO is designed to find the optimal reactor operating condition balancing energy cost and reactant conversion, and an MPC is designed to drive the process to the optimal operating condition. A variation in energy price is introduced to demonstrate that the developed RTO scheme is able to minimize operation cost and yields a closed-loop performance that is very close to the one attained by RTO/MPC using the first-principles model. In the second example, a distillation column is used to demonstrate an industrial application of the use of machine learning to model nonlinearities in RTO. A feed-forward neural network is first built to obtain the phase equilibrium properties and then combined with a first-principles model in RTO, which is designed to maximize the operation profit and calculate optimal set-points for the controllers. A variation in feed concentration is introduced to demonstrate that the developed RTO scheme can increase operation profit for all considered conditions.

A hybrid neural network-first principles approach to process modeling

AIChE Journal ◽  
1992 ◽  
Vol 38 (10) ◽  
pp. 1499-1511 ◽  
Author(s):  
Dimitris C. Psichogios ◽  
Lyle H. Ungar
Keyword(s):  
Neural Network ◽  
Process Modeling ◽  
First Principles ◽  
Hybrid Neural Network
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