Use of neural networks and expert systems to control a gas/solid sorption chilling machine

Palau, Adrià Salvador; Velo, Enrique; Puigjaner, Luís

doi:10.1016/s0140-7007(97)00046-7

Cited by 95 publications

(53 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its devolatilization predictions have also been compared with a detailed devolatilization model (FLASHCHAIN) and were found to be consistent. Palau [Palau et al (1996[Palau et al ( , 1999] focused on using ANN model to control a gas/solid sorption chilling machine. In such systems, the cold production changes cyclically with the time due to the batchwise operation of the gas/solid reactors.…”

Section: Iron Oxide Reduction Processmentioning

confidence: 99%

Simultaneous influence of gas mixture composition and process temperature on Fe2O3->FeO reduction kinetics: neural network modeling

Piotrowski

Wiltowski

Mondal

et al. 2005

Braz. J. Chem. Eng.

View full text Add to dashboard Cite

-The kinetics of Fe 2 O 3 →FeO reaction was investigated. The thermogravimetric (TGA) data covered the reduction of hematite both by pure species (nitrogen diluted CO or H 2 ) and by their mixture. The conventional analysis has indicated that initially the reduction of hematite is a complex, surface controlled process, however once a thin layer of lower oxidation state iron oxides (magnetite, wüstite) is formed on the surface, it changes to diffusion control. Artificial Neural Network (ANN) has proved to be a convenient tool for modeling of this complex, heterogeneous reaction runs within the both (kinetic and diffusion) regions, correctly considering influence of temperature and gas composition effects and their complex interactions. ANN's model shows the capability to mimic some extreme (minimum) of the reaction rate within the determined temperature window, while the Arrhenius dependency is of limited use.

show abstract

Section: Iron Oxide Reduction Processmentioning

confidence: 99%

Simultaneous influence of gas mixture composition and process temperature on Fe2O3->FeO reduction kinetics: neural network modeling

Piotrowski

Wiltowski

Mondal

et al. 2005

Braz. J. Chem. Eng.

View full text Add to dashboard Cite

show abstract

“…A transfer function consisted generally of algebraic equations of linear or nonlinear form (Palau et al, 1999). The use of a nonlinear transfer function makes a network capable of storing nonlinear relationships between input and output.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In other words, they need examples (Kalogirou, 2000). ANNs have been used at energy systems such as solar domestic water heating (Kalogirou et al, 1999) steam generation using solar energy (Kalogirou et al, 1998), energy consumption prediction of passive solar buildings (Kalogirou and Bojic, 2000), vapourcompression refrigeration systems (Bechtler et al, 2001), heat exchangers used in refrigeration applications (Pacheco-Vega et al, 2001), gas-solid machines based on the absorption of ammonia (Palau et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of CFCs with their substitutes using artificial neural network

Arcaklıoğlu

2004

Int. J. Energy Res.

View full text Add to dashboard Cite

SUMMARYIn order to decrease global pollution due to chlorofluorocarbons (CFCs), the usage of HFC-and HCbased refrigerants and their mixtures are considered instead of CFCs (R12, R22, and R502). This was confirmed by an international consensus (i.e. Montreal Protocol signed in 1987). This paper offers to determine coefficient of performance (COP) and total irreversibility (TI) values of vapour-compression refrigeration system with different refrigerants and their mixtures mentioned above using artificial neural networks (ANN). In order to train the network, COPs and TIs of refrigerants and their some binary, ternary and quartet mixtures of different ratios have been calculated in a vapour-compression refrigeration system with liquid/suction line heat exchanger. In the calculations thermodynamic properties of refrigerants have been taken from REFPROP 6.01 which was prepared based on Helmholtz energy equation of state. To achieve this, a new software has been written in FORTRAN programming language using sub-programs of REFPROP, and all related calculations have been performed using this software using constant temperature method as reference. Scaled conjugate gradient, Pola-Ribiere conjugate gradient, and Levenberg-Marquardt learning algorithms and logistic sigmoid transfer function were used in the network. Mixing ratios of refrigerants, and evaporator temperature were used as input layer; COP and TI values were used as output layer. It is shown that R 2 values are about 0.9999, maximum errors for training and test data are smaller than 2 and 3%, respectively. It is concluded that, ANNs can be used for prediction of COP and TI as an accurate method in the systems.

show abstract

“…Recently some works about the use of ANN in energy systems have been published [12][13][14][15][16][17][18][19][20]. From the brief literature review cited, it can be observed that the many investigators have used the artificial neural network for the performance prediction of various thermal energy systems.…”

Section: Introductionmentioning

confidence: 99%

Exergy Assessment of Single Stage Solar Adsorption Refrigeration System Using ANN

Baiju

Muraleedharan

2012

ISRN Mechanical Engineering

View full text Add to dashboard Cite

A new approach based on artificial intelligence is proposed here for the exergy assessment of solar adsorption refrigeration system working with activated carbon-methanol pair. Artificial neural network model is used for the prediction of exergy destruction and exergy efficiency of each component of the system. Pressure, temperature and solar insolation are used as input variables for developing the artificial neural network model. The back propagation algorithm with three different variants such as CGP, SCG and LM are used in the network A and network B. The most suitable algorithm and the number of neurons in hidden layer are found as LM with 9 for network A and SCG with 17 for the Network B. The artificial neural network predicted results are compared with the calculated values of exergy destruction and exergy efficiency. The R 2 values of the exergy destruction and exergy efficiency of components (condenser, expansion device, evaporator, adsorbent bed, solar concentrator and overall system) are found to be close to 1. The RMS and COV values are found to be very low in all cases. The comparison of the results suggests that the artificial neural network provided results are within the acceptable range.

show abstract

Use of neural networks and expert systems to control a gas/solid sorption chilling machine

Cited by 95 publications

References 14 publications

Simultaneous influence of gas mixture composition and process temperature on Fe2O3->FeO reduction kinetics: neural network modeling

Simultaneous influence of gas mixture composition and process temperature on Fe2O3->FeO reduction kinetics: neural network modeling

Performance comparison of CFCs with their substitutes using artificial neural network

Exergy Assessment of Single Stage Solar Adsorption Refrigeration System Using ANN

Contact Info

Product

Resources

About