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Author: search.filters.author.Dasgupta, Mani SankarSubject: search.filters.subject.Artificial Neural Networks

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    Smart control of electric lamp using artificial intelligence based controller
    (IEEE, 2015) Dasgupta, Mani Sankar
    There is need and scope of controlling the power consumed by conventional electric lamps in presence of some natural light. An artificial intelligence based control system has been developed to control a lamp dimmer circuit with bidirectional triode thyristor. The light present in the room is sensed and voltage supplied to the lamp is controlled by varying the time constant of the circuit through change of resistance of a multi-turn potentiometer with a stepper motor. The resistance set in the lamp dimmer circuit is in accordance with signals from an adaptive Neural Network running in MATLAB ® . The artificial neural network (ANN) runs in real time in MATLAB ® environment to control the time constant of the lamp dimmer circuit for controlling of power consumption. Under test conditions, energy savings up to 35% is achieved.
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    Performance evaluation of a CO2 scroll expander for work recovery using artificial neural network
    (Taylor & Francis, 2017-10) Dasgupta, Mani Sankar
    CO2 trans-critical refrigeration systems operate in sub-critical zone for major part of the up-time even for warm climate regions. Recovery of expansion work from CO2 refrigeration systems is viewed as a workable solution to tide over the challenge of typical low coefficient of performance of such systems. Some of the barriers for wide spread implementation of expanders are; relatively low work recovery and high initial investment. In the present study, the functioning of a scroll work recovery expander under sub-critical condition is investigated in an open-loop setup using CO2 as working fluid. The scroll expander itself is obtained through conversion from a scroll compressor with minimal additional investment. Influence of the various operating parameters like mass flow rate, suction pressure, pressure ratio, and rotational speed on the overall performance of the system are examined. An artificial neural network is then trained in Statistical Package for the Social Sciences (SPSS) platform with part of the experimental data and the same is validated with remaining data. It is observed that, the deviation between the shaft speed and shaft work for the model based prediction and experimental results are within ±7.5% and ±11.1%, respectively. The developed artificial neural network will be useful for predicting performance of work recovery scroll expander in closed loop operation with CO2 refrigeration system in sub-critical zone.