scholarly journals Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning

2022 ◽  
Vol 71 ◽  
pp. 103170
Author(s):  
Sita Radhakrishnan ◽  
Suresh G. Nair ◽  
Johney Isaac
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Model Development ◽  
Mechanical Ventilator ◽  
Time System ◽  
Real Time System

A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome

2010 ◽  
Vol 35 (3-5) ◽  
pp. 187-194 ◽  
Author(s):  
G. Napolitano ◽  
L. See ◽  
B. Calvo ◽  
F. Savi ◽  
A. Heppenstall
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Flood Forecasting ◽  
Tiber River

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.

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