Browsing by Author "Reza, Md. Sumon"

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Numerical Modeling of Ammonia-Fueled Protonic-Ion Conducting Electrolyte-Supported Solid Oxide Fuel Cell (H-SOFC): A Brief Review
(MDPI Publications, 2023-09-12) Rahman, Md. Mosfiqur; Abdalla, Abdalla M.; Omeiza, Lukman Ahmed; Raj, Veena; Afroze, Shammya; Reza, Md. Sumon; Somalu, Mahendra Rao; Azad, Abul K.
Solid oxide fuel cells with protonic ion conducting electrolytes (H-SOFCs) are recognized and anticipated as eco-friendly electrochemical devices fueled with several kinds of fuels. One distinct feature of SOFCs that makes them different from others is fuel flexibility. Ammonia is a colorless gas with a compound of nitrogen and hydrogen with a distinct strong smell at room temperature. It is easily dissolved in water and is a great absorbent. Ammonia plays a vital role as a caustic for its alkaline characteristics. Nowadays, ammonia is being used as a hydrogen carrier because it has carbon-free molecules and prosperous physical properties with transportation characteristics, distribution options, and storage capacity. Using ammonia as a fuel in H-SOFCs has the advantage of its ammonia cracking attributes and quality of being easily separated from generated steam. Moreover, toxic NOx gases are not formed in the anode while using ammonia as fuel in H-SOFCs. Recently, various numerical studies have been performed to comprehend the electrochemical and physical phenomena of H-SOFCs in order to develop a feasible and optimized design under different operating conditions rather than doing costlier experimentation. The aim of this concisely reviewed article is to present the current status of ammonia-fueled H-SOFC numerical modeling and the application of numerical modeling in ammonia-fueled H-SOFC geometrical shape optimization, which is still more desirable than traditional SOFCs.
Performance Evaluation of Random Forests and Artificial Neural Networks for the Classification of Liver Disorder
(IEEE, 2018-09-20) Haque, Md. Rezwanul; Islam, Md. Milon; Iqbal, Hasib; Reza, Md. Sumon; Hasan, Md. Kamrul
Liver is the major organ inside the human body which is very supportive for digesting food, eliminating poisons, and stocking energy. The rate of Liver disorder patients is rapidly rising all over the world. But it is very hard to identify the disorder from its ambiguous symptoms which increases the mortality rate due to this disease. The paper represents an expert scheme for the classification of liver disorder using Random Forests (RFs) and Artificial Neural Networks (ANNs). The methods train the input features using 10-fold cross validation fashion. The dataset named as BUPA liver dataset is retrieved from UCI machine learning repository for our research study. The performance of the proposed scheme is assessed in view of accuracy, positive predictive value, negative predictive value, sensitivity, specificity and F1 score. The scheme delivers a better result for training but comparatively low for testing. The scheme obtained the accuracy of 80% and 85.29% by RFs and ANNs respectively along with the F1 score of 75.86% and 82.76% in testing phase.
Self-powered IoT-Based Design for Multi-purpose Smart Poultry Farm
(Scopus, 2021) Akhund, Tajim Md. Niamat Ullah; Snigdha, Shouvik Roy; Reza, Md. Sumon; Newaz, Nishat Tasnim; Saifuzzaman, Mohd.; Rashel, Masud Rana
The purpose of the present work is to make an IoT-based smart poultry farm system. In this work, the power supply is developed using renewable energy mainly with solar energy and nano-hydro. This IoT-based module helps to develop the system’s productivity to ensure farm’s constant healthy condition. The proposed system collects several types of sensor data from the farm, such as temperature, humidity, toxic gas, water level, and moisture. Then the overall conditions will be controlled and be kept proper level with the developed system automatically. This system stores all the data in the central database for further analysis and getting knowledge from them also gives notifications. Optimal values for the power and health condition are obtained through the stored data. It helps to predict future conditions too. The electrical devices, doors, and dustbins of the farm can be controlled via IoT systems through mobile phones and online platforms from remote locations and from anywhere in the world with Internet connectivity.