Browsing by Author "Mitra, Kalyan"

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A novel approach towards gas turbine emission reduction by using neural networks /
(IEEE, 2023-11-15) Mitra, Kalyan
Optimizations of controlling parameters are the key factors to achieve effectual output and emission reduction from machinery running. This study relates prediction technology for gas turbine's (GT's) running optimization and emission reduction. The tool can identify upcoming disputes and alter the engine control setting to achieve highly efficient running operations. This research objects to get a machine learning approach to predict GT performance on emissions. The forecasting capabilities of this system are based on real-time data collected from a GT plant. A neural network is employed to analyze and predict the emissions of exhaust pollutants, primarily carbon monoxide and nitrogen oxides. The model performance is studied by considering the values of MSE, MAE, and residuals. The model's accuracy and precision are revealed by its low MSE (0.0035) and MAE (0.043). This approach is a cost-effective way to gage emissions accurately and can be used to confirm compliance with environmental regulations. It is also a helpful tool for monitoring the health of the turbine and identifying any potential issues with its operation.
Experimental analysis of drag forces on a floating model under varying submergence conditions
(Indian Maritime University, Kolkata Campus, 2022-08) Gnanadevan, R.; Mitra, Kalyan; Deogade, R. B.
When a floating body is towed against a stream of water it experiences drag as a result of the resistance provided by the water. An object’s resistance to motion is made up of air and water drag. Analysis of drag force is necessary to determine the hydrodynamic forces acting on the object which is required for the selection of a propulsion system. The physical model test is one of the most accurate methods because it derives the common solution by taking into account the performance of all parameters. Model alteration, Fabrication and actual experimental testing in a water are part of traditional model testing. Based on the solutions of the model tests, alterations are made to the design of the floating body and experiments are repeated until required results are obtained. Drag force measurement experiments are generally conducted on still water conditions i.e. on a floating platform carrying dead weight or with container of liquid. The present model experiments are conducted under still and monochromatic wave conditions at different rating trolley velocities and the solutions are evaluated for the difference of drag force on floating platform with dead weight and liquid at above and below the centre of gravity of the boat. The experiments will help in analysing impact of the drag force due to the effect of shift in centre of gravity in liquid when partly filled with the same dead weight load on the floating body. This analysis will help in identifying the safety precautionary standards in case of emergency situations due to free surface effects especially for Roll on and Roll off (RO/RO) and multi cargo carriers.
Validating AHP, fuzzy alpha cut and fuzzy preference programming method using clustering technique /
(Springer Nature, 2010-03-01) Mitra, Kalyan
Fuzzy multicriteria analysis (MA) methods suitable for a given decision problem usually differ in aggregation processes for handling the alternatives’ performance ratings and criteria weights. Due to their structural differences, these methods often produce inconsistent ranking results for the same fuzzy MA problem. This paper presents a validation procedure using fuzzy clustering for selecting among inconsistent ranking results produced by analytical hierarchy process (AHP), fuzzy alpha cut and fuzzy preference programming (FPP) methods for a given problem. The procedure compares the ranking results obtained by fuzzy MA methods using different aggregation algorithms with the clustering results of the alternatives by fuzzy clustering. An empirical study of evaluating major port trusts of India is conducted to demonstrate the effectiveness of the validation procedure.