@article{MAKHILLJEAS201813615831,
    title = {Development of Hybrid Computational Intelligence Model for
Estimating Relative Cooling Power of Manganite-Based
Materials for Magnetic Refrigeration Enhancement},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {6},
    pages = {1575-1583},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.1575.1583},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.1575.1583},
    author = {Taoreed,Nahier,Kabiru,Sunday and},
    keywords = {Manganite-based materials,relative cooling power,sensitivity-based linear learning method,ionic radii,gravitational search algorithm,magnetic refrigeration},
    abstract = {The significance of Relative Cooling Power (RCP) of manganite-based magnetic refrigerant in
Magnetic Refrigeration (MR) technology cannot be over-emphasized. Although, MR system overcomes the
setbacks of conventional gas compression technology with its better performance, low cost and little or no
environmental hazard. However, experimental determination of the refrigerant RCP is subjected to procedures
and routines that are not only challenging but also consume appreciable time and other valuable resources.
This necessitates for a simple and cost effective modeling technique that preserves the experimental precision
and accuracy. Therefore, this research develops Sensitivity-Based Linear Learning Method (SBLLM) of training
two-layer feedforward neural network for estimating RCP of manganite-based materials using ionic radii and
dopants concentration as inputs to the model. The number of epoch and hidden neurons of the network are
optimized using Gravitational Search Algorithm (GSA). The results of the developed GSA-SBLLM Model agree
well with the experimentally measured values. The strength and robustness of the developed GSA-SBLLM
Model include its ability to incorporate up to four different dopants and their respective concentrations to
manganite material for magnetic refrigerant RCP estimation. This ability coupled with the precision of its
estimates is of significant impact in magnetic refrigeration enhancement without experimental challenges.}
    }