Use of Neural Networks in the Simulation and Optimization of Pressure Swing Adsorption Processes

Lewandowski, Jari; Lemcoff, N.O.; Palosaari, Seppo

doi:10.1002/(sici)1521-4125(199807)21:7<593::aid-ceat593>3.0.co;2-u

Cited by 25 publications

(20 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was used to simulate the dynamics of an adsorption column for wastewater treatment of water containing toxic chemicals (Bulsari and Palosaafi 1993) and to predict the occurrence of breakthrough in an ion-exchange adsorption column (Yang et al 1993). Lewandowski et al (1998) developed a method for the simulation and optimization of a pressure swing adsorption process for the separation of nitrogen from air where the neural network model was used to obtain a prediction for the process performance, namely, the specific product and yield, over a wide range of operating conditions. Carsky and Do (1999) predicted the adsorption equilibrium of binary vapour mixtures on activated carbon.…”

Section: Introductionmentioning

confidence: 77%

Neural network modelling of adsorption isotherms

et al. 2010

View full text Add to dashboard Cite

This paper examines the possibility to use a single neural network to model and predict a wide array of standard adsorption isotherm behaviour. Series of isotherm data were generated from the four most common isotherm equations (Langmuir, Freundlich, Sips and Toth) and the data were fitted with a unique neural network structure. Results showed that a single neural network with a hidden layer having three neurons, including the bias neuron, was able to represent very accurately the adsorption isotherm data in all cases. Similarly, a neural network with four hidden neurons, including the bias, was able to predict very accurately the temperature dependency of adsorption data. NomenclatureA constant for Freundlich equation (kg ethanol/ kg adsorbent) (m 3 /kg ethanol) 1/n b constant for Langmuir, Toth and Sips equations; temperature dependent variable for the TD-Toth equation (m 3 /kg adsorbate) b o constant for the TD-Toth equation (m 3 / kg adsorbate) c fluid adsorbate concentration (kg adsorbate/m 3 ) n constant for the Freundlich and Sips equations (dimensionless)

show abstract

Section: Introductionmentioning

confidence: 77%

Neural network modelling of adsorption isotherms

et al. 2010

View full text Add to dashboard Cite

show abstract

“…In biochemical engineering for example, ANNs are commonly used to model fermentation processes [7] and optimize their output to identify promising operating points for further experiments [8][9][10]. ANNs are also used as surrogate models for the optimization of chemical processes [11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Deterministic Global Optimization with Artificial Neural Networks Embedded

Schweidtmann

Mitsos

2018

J Optim Theory Appl

172

109

View full text Add to dashboard Cite

1 Abstract Artificial neural networks (ANNs) are used in various applications for datadriven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [SIOPT, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.

show abstract

“…Most systems encountered in industry are nonlinear to some extent, and in many applications nonlinear models are required to provide acceptable representations [28]. ANN is a parallel structure composed of nonlinear nodes which are connected by fixed weights and variables.…”

Section: Fundamentals Of Artificial Neural Networkmentioning

confidence: 99%

Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique

Sadighi¹,

Mohaddecy²,

Norouzian

2015

Bull. Chem. React. Eng. Catal.

View full text Add to dashboard Cite

In this paper, a hybrid model for estimating the activity of a commercial Pt-Re/Al2O3 catalyst in an industrial scale heavy naphtha catalytic-reforming unit (CRU) is presented. This model is also capable of predicting research octane number (RON) and yield of gasoline. In the proposed model, called DANN, the decay function of heterogeneous catalysts is combined with a recurrent-layer artificial neural network. During a life cycle (919 days), fifty-eight points are selected for building and training the DANN (60%), nineteen data points for testing (20%), and the remained ones for validating steps. Results show that DANN can acceptably estimate the activity of catalyst during its life in consideration of all process variables. Moreover, it is confirmed that the proposed model is capable of predicting RON and yield of gasoline for unseen (validating) data with AAD% (average absolute deviation) of 0.272% and 0.755%, respectively. After validating the model, the octane barrel level (OCB) of the plant is maximized by manipulating the inlet temperature of reactors, and hydrogen to hydrocarbon molar ratio whilst all process limitations are taken into account. During a complete life cycle results show that the decision variables, generated by the optimization program, can increase the RON, process yield and OCB of CRU to about 1.15%, 3.21%, and 4.56%, respectively.

show abstract

Use of Neural Networks in the Simulation and Optimization of Pressure Swing Adsorption Processes

Cited by 25 publications

References 8 publications

Neural network modelling of adsorption isotherms

Neural network modelling of adsorption isotherms

Deterministic Global Optimization with Artificial Neural Networks Embedded

Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique

Contact Info

Product

Resources

About