Comparison of Exergy and Various Energy Analysis Methods for a Main Marine Steam Turbine at Different Loads

This paper present energy and exergy analysis of the main marine steam turbine, which is used for the commercial LNG (Liquefied Natural Gas) carrier propulsion, at four different loads. Energy analysis is performed by using four different methods. The presented analysis allows distinguishing advantages and disadvantages of all observed energy analysis methods and its comparison to exergy analysis of the same steam turbine. Each analysis is based on the measurement results obtained in main turbine exploitation conditions. Main turbine is composed of two cylinders – High Pressure Cylinder (HPC) and Low Pressure Cylinder (LPC). At low turbine loads, the dominant power producer is HPC, while at middle and high loads the dominant power producer is LPC. Energy analysis Method 1 which is based on the same principles as exergy analysis, should be avoided if the majority of turbine losses are not known. Other observed energy analysis methods can be applied in the analysis of any steam turbine, with a note that increase in ideal (isentropic) steam expansion process divisions will result with an increase in energy losses and with a decrease in energy efficiency. Energy analysis Method 2 which consist of only one ideal (isentropic) steam expansion process, for the whole turbine and at all observed loads, results with the lowest energy losses (in the range between 639.98 kW and 6434.17 kW) as well as with the highest energy efficiency (in a range between 53.70% and 79.40%) in comparison to other applicable energy analysis methods. For the observed loads, whole main turbine exergy destruction is in range from 608.64 kW to 5922.86 kW, while the exergy efficiency range of the whole turbine is between 54.94% and 80.73%. Exergy analysis and all three applicable energy analysis methods show that increase in the main turbine load results with simultaneous increase in turbine losses and efficiencies (both energy and exergy)

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

In this paper, the publicly available dataset of condition based maintenance of combined diesel-electric and gas (CODLAG) propulsion system for ships has been utilized to obtain symbolic expressions which could estimate gas turbine shaft torque and fuel flow using genetic programming (GP) algorithm. The entire dataset consists of 11934 samples that was divided into training and testing portions of dataset in an 80:20 ratio. The training dataset used to train the GP algorithm to obtain symbolic expressions for gas turbine shaft torque and fuel flow estimation consisted of 9548 samples. The best symbolic expressions obtained for gas turbine shaft torque and fuel flow estimation were obtained based on their R2 score generated as a result of the application of the testing portion of the dataset on the aforementioned symbolic expressions. The testing portion of the dataset consisted of 2386 samples. The three best symbolic expressions obtained for gas turbine shaft torque estimation generated R2 scores of 0.999201, 0.999296, and 0.999374, respectively. The three best symbolic expressions obtained for fuel flow estimation generated R2 scores of 0.995495, 0.996465, and 0.996487, respectively

Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

An artificial neural network (ANN) approach is proposed to the problem of estimating the propeller torques of a frigate using combined diesel, electric and gas (CODLAG) propulsion system. The authors use a multilayer perceptron (MLP) feed-forward ANN trained with data from a dataset which describes the decay state coefficients as outputs and system parameters as inputs – with a goal of determining the propeller torques, removing the decay state coefficients and using the torque values of the starboard and port propellers as outputs. A total of 53760 ANNs are trained – 26880 for each of the propellers, with a total 8960 parameter combinations. The results are evaluated using mean absolute error (MAE) and coefficient of determination (R2). Best results for the starboard propeller are MAE of 2.68 [Nm], and MAE of 2.58 [Nm] for the port propeller with following ANN configurations respectively: 2 hidden layers with 32 neurons and identity activation and 3 hidden layers with 16, 32 and 16 neurons and identity activation function. Both configurations achieve R2 value higher than 0.99

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

The importance of error detection is high, especially in modern manufacturing processes where assembly lines operate without direct supervision. Stopping the faulty operation in time can prevent damage to the assembly line. Public dataset is used, containing 15 classes, 2 types of faultless operation and 13 types of faults, with 463 force and torsion datapoints. Four different methods are used: Multilayer Perceptron (MLP) selected due to high classification performance, Support Vector Machines (SVM) commonly used for a low number of datapoints, Convolutional Neural Network (CNN) known for high performance in classification with matrix inputs and Siamese Neural Network (SNN) novel method with high performance in small datasets. Two classification tasks are performed-error detection and classification. Grid search is used for hyperparameter variation and F1 score as a metric, with a 10 fold cross-validation. Authors propose a hybrid system consisting of SNN for detection and CNN for fault classification

The influence of various optimization algorithms on nuclear power plant steam turbine exergy efficiency and destruction

This paper presents an exergy analysis of the whole turbine, turbine cylinders and cylinder parts in four different operating regimes. Analyzed turbine operates in nuclear power plant while three of four operating regimes are obtained by using optimization algorithms - SA (Simplex Algorithm), GA (Genetic Algorithm) and IGSA (Improved Genetic-Simplex Algorithm). IGSA operating regime gives the highest developed mechanical power of the whole turbine equal to 1022.48 MW, followed by GA (1020.06 MW) and SA (1017.16 MW), while in Original operating regime whole turbine develop mechanical power equal to 996.29 MW. In addition, IGSA causes the highest increase in developed mechanical power of almost all cylinders and cylinder parts in comparison to the Original operating regime. All observed optimization algorithms increases the exergy destruction of the whole turbine in comparison to Original operating regime - the lowest increase causes IGSA, followed by GA and finally SA. The highest exergy efficiency of the whole turbine, equal to 85.92% is obtained by IGSA, followed by GA (85.89%) and SA (85.82%), while the lowest exergy efficiency is obtained in Original operating regime (85.70%). Analyzed turbine, which operates by using wet steam is low influenced by the ambient temperature change. IGSA, which shows dominant performance in exergy analysis parameters of the analyzed turbine, in certain situations is overpowered by GA. Therefore, in optimization of steam turbine performance, IGSA and GA can be recommended.Comment: 25 pages, 10 figures, 4 table

Comparison of Power Distribution, Losses and Efficiencies of a Steam Turbine with and without Extractions

The paper presents an analysis of two steam turbine operation regimes - regime with all steam extractions opened (base process) and regime with all steam extractions closed. Closing of all steam extractions significantly increases turbine real developed power for 5215.88 kW and increases turbine energy and exergy losses with simultaneous decrease of turbine energy and exergy efficiencies for more than 2%. First extracted steam mass flow rate has a dominant influence on turbine power losses (in comparison to turbine maximum power when all of steam extractions are closed). Cumulative power losses caused by steam mass flow rate extractions are the highest in the fourth turbine segment and equal to 1687.82 kW

Prediction of Robot Grasp Robustness using Artificial Intelligence Algorithms

Predicting the quality of the robot end-effector grasp quality during an industrial robot manipulator operation can be an extremely complex task. As is often the case with such complex tasks, Artificial Intelligence methods may be applied to attempt the creation of a model - if sufficient data exists. The presented dataset uses a publicly available dataset, consisting of 992632 measurements of position, torque, and velocity - for each of the three joints of three fingers of the simulated end-effector. The dataset is first analyzed and pre-processed to prepare it for model training. The duplicate values are removed from the dataset, as well as the statistical outliers. Then, a multilayer perceptron (MLP) machine learning algorithm is applied to 80% of the data contained in the dataset, using the Grid Search algorithm to determine the best combination of MLP hyperparameters. As the dataset consists of torque, velocity, and speed measurements for separate joints and fingers of the tested end-effector the testing is performed to see if a subset of the inputs may be used to regress the robustness of the given grip. The normalization of the dataset is also applied, and its effect on the regression quality is tested. The results, evaluated with the coefficient of determination, show that while the best model is achieved using all the possible inputs, a satisfactory result can be obtained using only velocity and torque.The results also show that the normalization of the dataset improves the regression quality in all the observed cases

On the Traveling Salesman Problem in Nautical Environments: an Evolutionary Computing Approach to Optimization of Tourist Route Paths in Medulin, Croatia

The Traveling salesman problem (TSP) defines the problem of finding the optimal path between multiple points, connected by paths of a certain cost. This paper applies that problem formulation in the maritime environment, specifically a path planning problem for a tour boat visiting popular tourist locations in Medulin, Croatia. The problem is solved using two evolutionary computing methods – the genetic algorithm (GA) and the simulated annealing (SA) - and comparing the results (are compared) by an extensive search of the solution space. The results show that evolutionary computing algorithms provide comparable results to an extensive search in a shorter amount of time, with SA providing better results of the two

Artificial neural network for predicting values of residuary resistance per unit weight of displacement

This paper proposes the usage of an Artificial neural network (ANN) to predict the values of the residuary resistance per unit weight of displacement from the variables describing ship’s dimensions. For this purpose, a Multilayer perceptron (MLP) regressor ANN is used, with the grid search technique being applied to determine the appropriate properties of the model. After the model training, its quality is determined using R2 value and a Bland-Altman (BA) graph which shows a majority of values predicted falling within the 95% confidence interval. The best model has four hidden layers with ten, twenty, twenty and ten nodes respectively, uses a relu activation function with a constant learning rate of 0.01 and the regularization parameter L2 value of 0.001. The achieved model shows a high regression quality, lacking precision in the higher value range due to the lack of data

Comparison of conventional and heat balance based energy analyses of steam turbine

This paper presents a comparison of conventional and heat balance based energy analyses of steam turbine. Both analyses are compared by using measured operating parameters from low power steam turbine exploitation. The major disadvantage of conventional steam turbine energy analysis is that extracted energy flow streams are not equal in real (polytropic) and ideal (isentropic) expansion processes, while the heat balance based energy analysis successfully resolved mentioned problem. Heat balance based energy analysis require an increase of steam mass flow rates extracted from the turbine in ideal (isentropic) expansion process to ensure always the same energy flow streams to all steam consumers. Increase in steam mass flow rate extracted through each turbine extraction (heat balance based energy analysis) result with a decrease in energy power losses and with an increase in energy efficiency of whole turbine and all of its cylinders (when compared to conventional analysis). All of the obtained conclusions in this research are valid not only for the analyzed low power steam turbine, but also for any other steam turbine with steam extractions