Improvement of Marine Steam Turbine Conventional Exergy Analysis by Neural Network Application

Abstract

This article presented an improvement of marine steam turbine conventional exergy analysis by application of neural networks. The conventional exergy analysis requires numerous measurements in seven different turbine operating points at each load, while the intention of MLP (Multilayer Perceptron) neural network-based analysis was to investigate the possibilities for measurements reducing. At the same time, the accuracy and precision of the obtained results should be maintained. In MLP analysis, six separate models are trained. Due to a low number of instances within the data set, a 10-fold cross-validation algorithm is performed. The stated goal is achieved and the best solution suggests that MLP application enables reducing of measurements to only three turbine operating points. In the best solution, MLP model errors falling within the desired error ranges (Mean Relative Error) MRE < 2.0% and (Coefficient of Correlation) R2 > 0.95 for the whole turbine and each of its cylinders.

Estimation of gas turbine shaft torque and fuel flow of a CODLAG propulsion system using genetic programming algorithm

“…Today the artificial intelligence (AI) is implemented in various fields such as energy sector [48][49][50][51], medicine [52][53][54][55], maritime [56][57][58][59], economics [60] and etc. There are some research papers in which the AI algorithms have been implemented on the CODLAG dataset to perform some estimation of certain parameters.…”

Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

“…To differentiate these parameters from parameters contained within the neural network, namely the values of weights Y , these parameters are named hyperparameters. 61,62 Values of hyperparameters determine how well the ANN will perform the task it was designed to. The hyperparameters of the neural network varied in this article are hidden layers (number of layers and neurons per each layer 63 ), activation function of the neurons, solver, initial learning rate, type of learning rate adjustment, and regularization parameter L2.…”

Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator

“…MLP consists of an input layer, an output layer, and one or more hidden layers [16]. These layers consist of neurons, connected to the subsequent layer through weighted connections [22,23]. In the input layer, the values of neurons are defined as the values of inputs from the dataset the vector i x   mentioned in the previous section.…”