Application of a mixed variable physics-informed neural network to solve the incompressible steady-state and transient mass, momentum, and energy conservation equations for flow over in-line heated tubes

2021 ◽  
pp. 108050
Author(s):  
Ryno Laubscher ◽  
Pieter Rousseau
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Energy Conservation ◽  
Conservation Equations ◽  
Mixed Variable

scholarly journals THEORETICAL MODELING OF A TWO-PHASED THERMOSYPHON ASSUMING THE LIQUID RESERVATORY

2007 ◽  
Vol 6 (1) ◽  
pp. 74
Author(s):  
M. A. Zanardi ◽  
N. G. C. Leite
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Steady State ◽  
Energy Conservation ◽  
Theoretical Modeling ◽  
General View ◽  
Two Phase ◽  
Conservation Equations ◽  
Stable Level ◽  
Steady State Operation

A theoretical modeling using the mass, momentum and energy conservation equations, about the intrinsic phenomena in the working of a cylindrical geometry two-phase thermosyphon operating on vertical was performed.  The conservation equations were solved in steady-state operation for all the phases of the thermosyphon. Then model also assumed the presence of a liquid reservatory whose valves of the coefficient of heat transfer that determine the operation of functioning in the reservatory, were obtained from the correlation published in literature.  The set of conservation equations was solved by using the method of finite volumes.  The results achieved were checked with experimental data from literature and also from specific experiments performed in laboratory. In a  general view, the theoric results matched reasonably well with those ones from the experiments, and the observed deviation were assumed by a inadequate prevision of the reservatory model used, besides keeping a stable level of the reservatory of liquid.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals Flame Failures and Recovery in Industrial Furnaces: A Neural Network Steady-State Model for the Firing Rate Setpoint Rearrangement

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Tahmineh Adili ◽  
Zohreh Rostamnezhad ◽  
Ali Chaibakhsh ◽  
Ali Jamali
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Optimization Problems ◽  
Network Models ◽  
Economic Loss ◽  
Neural Network Models ◽  
Firing Rates ◽  
State Models ◽  
Major Equipment ◽  
Artificial Neural Network Models

Burner failures are common abnormal conditions associated with industrial fired heaters. Preventing from economic loss and major equipment damages can be attained by compensating the lost heat due to burners’ failures, which can be possible by defining appropriate setpoints to rearrange the firing rates for healthy burners. In this study, artificial neural network models were developed for estimating the appropriate setpoints for the combustion control system to recover an industrial fired-heater furnace from abnormal conditions. For this purpose, based on an accurate high-order mathematical model, constrained nonlinear optimization problems were solved using the genetic algorithm. For different failure scenarios, the best possible excess firing rates for healthy burners to recover the furnace from abnormal conditions were obtained and data were recorded for training and testing stages. The performances of the developed neural steady-state models were evaluated through simulation experiments. The obtained results indicated the feasibility of the proposed technique to deal with the failures in the combustion system.

scholarly journals Mathematical modeling and numerical investigation of density wave instability of supercritical natural circulation loop

2021 ◽  
Vol 321 ◽  
pp. 04004
Author(s):  
Santosh Kumar Rai ◽  
Neha Ahlawat ◽  
Pardeep Kumar ◽  
Vinay Panwar
Keyword(s):  
Energy Conservation ◽  
Natural Circulation ◽  
Density Wave ◽  
Circulation Loop ◽  
Wave Instability ◽  
Conservation Equations ◽  
One Dimensional ◽  
Natural Circulation Loop ◽  
The One ◽  
Riser Height

In present paper, a mathematical model based on the one dimensional nonlinear mass, momentum and energy conservation equations has been developed to study the density wave instability (DWI) in horizontal heater and horizontal cooler supercritical water natural circulation loop (HHHC-SCWNCL). The one dimensional nonlinear mass, momentum and energy conservation equations are discretized by using finite difference method (FDM). The numerical model is validated with the benchmark results (NOLSTA model). Numerical simulations are performed to find the threshold stability zone (TSZ) and draw the stability map for natural circulation loop. Further, effect of change in diameter and riser height on the density wave instability of SCWNCL has been investigated.

Development of Neural Network Models for a Crude Oil Distillation Column

2012 ◽  
Author(s):  
Khairiyah Mohd. Yusof ◽  
Fakhri Karray ◽  
Peter L. Douglas
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Crude Oil ◽  
Distillation Column ◽  
Network Models ◽  
Ann Model ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

scholarly journals Comparison of Physics-Based, Semi-empirical and Neural Network-based Models for Model-based Combustion Control in a 3.0 L Diesel Engine

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3423 ◽  
Author(s):  
Hu ◽  
d’Ambrosio ◽  
Finesso ◽  
Manelli ◽  
Marzano ◽  
...  
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Diesel Engine ◽  
Effective Pressure ◽  
Combustion Control ◽  
Ann Model ◽  
Control Applications ◽  
Model Based ◽  
Brake Mean Effective Pressure ◽  
Semi Empirical

A comparison of four different control-oriented models has been carried out in this paper for the simulation of the main combustion metrics in diesel engines, i.e., combustion phasing, peak firing pressure, and brake mean effective pressure. The aim of the investigation has been to understand the potential of each approach in view of their implementation in the engine control unit (ECU) for onboard combustion control applications. The four developed control-oriented models, namely the baseline physics-based model, the artificial neural network (ANN) physics-based model, the semi-empirical model, and direct ANN model, have been assessed and compared under steady-state conditions and over the Worldwide Harmonized Heavy-duty Transient Cycle (WHTC) for a Euro VI FPT F1C 3.0 L diesel engine. Moreover, a new procedure has been introduced for the selection of the input parameters. The direct ANN model has shown the best accuracy in the estimation of the combustion metrics under both steady-state/transient operating conditions, since the root mean square errors are of the order of 0.25/1.1 deg, 0.85/9.6 bar, and 0.071/0.7 bar for combustion phasing, peak firing pressure, and brake mean effective pressure, respectively. Moreover, it requires the least computational time, that is, less than 50 s when the model is run on a rapid prototyping device. Therefore, it can be considered the best candidate for model-based combustion control applications.

Research of the Steady-State Modeling Method Based on Neural Network

2013 ◽  
Vol 441 ◽  
pp. 526-529
Author(s):  
Yu Hua Zhu ◽  
Dian Zheng Zhuang
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Nitric Acid ◽  
Production Process ◽  
Acid Production ◽  
Actual System ◽  
Production History ◽  
System Input ◽  
Mechanism Modeling ◽  
The Right

Nitric acid production process is complicated reaction mechanism, serious non-linear the traditional mechanism modeling to get the low accuracy of the mathematical model. This paper adopts a non-mechanism modeling, using the production history data, the use of artificial neural network has the right to arbitrary nonlinear mapping any approximation ability to simulate the relationship of the actual system input-output, trained to be get steady-state model of the nitric acid production process.

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