scholarly journals Neural Network Approach for Predicting Ship Speed and Fuel Consumption

2021 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Lúcia Moreira ◽  
Roberto Vettor ◽  
Carlos Guedes Soares
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Computing System ◽  
Fuel Oil ◽  
Network Computing ◽  
Peak Period ◽  
Neural Network Approach ◽  
Data Set ◽  
Navigation Data ◽  
Ship Speed

In this paper, simulations of a ship travelling on a given oceanic route were performed by a weather routing system to provide a large realistic navigation data set, which could represent a collection of data obtained on board a ship in operation. This data set was employed to train a neural network computing system in order to predict ship speed and fuel consumption. The model was trained using the Levenberg–Marquardt backpropagation scheme to establish the relation between the ship speed and the respective propulsion configuration for the existing sea conditions, i.e., the output torque of the main engine, the revolutions per minute of the propulsion shaft, the significant wave height, and the peak period of the waves, together with the relative angle of wave encounter. Additional results were obtained by also using the model to train the relationship between the same inputs used to determine the speed of the ship and the fuel consumption. A sensitivity analysis was performed to analyze the artificial neural network capability to forecast the ship speed and fuel oil consumption without information on the status of the engine (the revolutions per minute and torque) using as inputs only the information of the sea state. The results obtained with the neural network model show very good accuracy both in the prediction of the speed of the vessel and the fuel consumption.

Neural Network-Based Engine Propeller Matching (NN-EPM) for Trimaran Patrol Ship

2014 ◽  
Vol 493 ◽  
pp. 388-394 ◽  
Author(s):  
Eddy S. Koenhardono ◽  
Eko Budi Djatmiko ◽  
Adi Soeprijanto ◽  
Mohammad I. Irawan
Keyword(s):  
Neural Network ◽  
Fuel Consumption ◽  
Fuel Oil ◽  
Training Data ◽  
Reduction Gear ◽  
Maximum Relative Error ◽  
Oil Consumption ◽  
Testing Data ◽  
Ship Speed

In recent years efforts on reducing fuel consumption has become the greatest issue related to energy crisis and global warming. The reduction of fuel consumption can be obtained, if the ship propulsion could be operated in its best performance level. Generally this is done by an appropriate analysis of engine propeller matching (EPM). In this study an EPM based on neural-network method, or NN-EPM, is established to predict the best performance of main engines, leading at minimum fuel oil consumption. A trimaran patrol ship is selected as a case study. This patrol ship is equipped with two 2720 kW main engines each connected to a controllable pitch propeller (CPP) through a reduction gear. The input parameters are ship speedVand service margin SM, with the corresponding output parameters comprise of engine speednE, engine break horse powerPB, propeller pitchP/D, and the fuel consumptionFC. An NN-EPM 2-20-15-4 configuration has been constructed out of 100 training data and then validated by 30 testing data. The maximum relative error between results from NN-EPM and EPM analysis is 2.1%, that is in term of the fuel consumption. For other parameters the errors are well below 1.0%. These facts indicate that the use of NN-EPM to predict the main engines's performance for trimaran patrol ship is satisfactory.

THE STAG OIL FIELD FORMATION EVALUATION: A NEURAL NETWORK APPROACH

1999 ◽  
Vol 39 (1) ◽  
pp. 451 ◽  
Author(s):  
H. Crocker ◽  
C.C. Fung ◽  
K.W. Wong
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Oil Field ◽  
Training Data ◽  
Ann Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Formation Evaluation ◽  
Core Data

The producing M. australis Sandstone of the Stag Oil Field is a bioturbated glauconitic sandstone that is difficult to evaluate using conventional methods. Well log and core data are available for the Stag Field and for the nearby Centaur–1 well. Eight wells have log data; six also have core data.In the past few years artificial intelligence has been applied to formation evaluation. In particular, artificial neural networks (ANN) used to match log and core data have been studied. The ANN approach has been used to analyse the producing Stag Field sands. In this paper, new ways of applying the ANN are reported. Results from simple ANN approach are unsatisfactory. An integrated ANN approach comprising the unsupervised Self-Organising Map (SOM) and the Supervised Back Propagation Neural Network (BPNN) appears to give a more reasonable analysis.In this case study the mineralogical and petrophysical characteristics of a cored well are predicted from the 'training' data set of the other cored wells in the field. The prediction from the ANN model is then used for comparison with the known core data. In this manner, the accuracy of the prediction is determined and a prediction qualifier computed.This new approach to formation evaluation should provide a match between log and core data that may be used to predict the characteristics of a similar uncored interval. Although the results for the Stag Field are satisfactory, further study applying the method to other fields is required.

Early Warning of Gas Turbine Failure by Nonlinear Feature Extraction Using an Auto-Associative Neural Network Approach

2011 ◽  
Author(s):  
Thomas Palme´ ◽  
Peter Breuhaus ◽  
Mohsen Assadi ◽  
Albert Klein ◽  
Minkyo Kim
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Early Warning ◽  
Linear Method ◽  
Data Driven ◽  
Operational Parameters ◽  
Neural Network Approach ◽  
Data Set ◽  
Nonlinear Pca ◽  
Auto Associative Neural Network

This study investigates the application of nonlinear Principal Component Analysis (PCA), implemented through the use of Auto-Associative Neural Network (AANN), for early warning of impending gas turbine failure. The study is based on a real operational data set that includes a compressor failure. The analyzed data set consists of measured operational parameters whose identity are unknown, hence this study presents a purely data driven approach to the problem of early warning. In this case study, the use of AANNs for early detection of abnormal engine behavior could have provided the operator with a warning a few days prior to the fully developed failure, which resulted in a forced shut-down and extensive maintenance. Furthermore, a comparison is made between the nonlinear PCA by AANNs and the standard PCA model, which is an inherently linear method. The result shows that the AANN provides a more reliable detection of the failure by a higher residual generation during failure mode as well as fewer false indications prior to the failure. Consequently, this study shows that nonlinear PCA as performed with AANNs can be a valuable data driven tool for early warning of gas turbine failure.

Genetic‐algorithm/neural‐network approach to seismic attribute selection for well‐log prediction

Geophysics ◽  
2004 ◽  
Vol 69 (1) ◽  
pp. 212-221 ◽  
Author(s):  
Kevin P. Dorrington ◽  
Curtis A. Link
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Reservoir Characterization ◽  
Seismic Attributes ◽  
Optimal Number ◽  
Attribute Selection ◽  
Well Log ◽  
Seismic Attribute ◽  
Neural Network Approach ◽  
Data Set

Neural‐network prediction of well‐log data using seismic attributes is an important reservoir characterization technique because it allows extrapolation of log properties throughout a seismic volume. The strength of neural‐networks in the area of pattern recognition is key in its success for delineating the complex nonlinear relationship between seismic attributes and log properties. We have found that good neural‐network generalization of well‐log properties can be accomplished using a small number of seismic attributes. This study presents a new method for seismic attribute selection using a genetic‐algorithm approach. The genetic algorithm attribute selection uses neural‐network training results to choose the optimal number and type of seismic attributes for porosity prediction. We apply the genetic‐algorithm attribute‐selection method to the C38 reservoir in the Stratton field 3D seismic data set. Eleven wells with porosity logs are used to train a neural network using genetic‐algorithm selected‐attribute combinations. A histogram of 50 genetic‐algorithm attribute selection runs indicates that amplitude‐based attributes are the best porosity predictors for this data set. On average, the genetic algorithm selected four attributes for optimal porosity log prediction, although the number of attributes chosen ranged from one to nine. A predicted porosity volume was generated using the best genetic‐algorithm attribute combination based on an average cross‐validation correlation coefficient. This volume suggested a network of channel sands within the C38 reservoir.

scholarly journals Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

2020 ◽  
Author(s):  
S. T. Pavana Kumar ◽  
Ferdinand B. Lyngdoh
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Data Set ◽  
The Neural Network ◽  
Hidden Layer

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

scholarly journals Document Classification Using Artificial Neural Network

2019 ◽  
Vol 8 (2) ◽  
pp. 55-58
Author(s):  
Kshitij Tripathi ◽  
Rajendra G. Vyas ◽  
Anil K. Gupta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Document Classification ◽  
Vector Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Document Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

The Document classification system is the field of data mining in which the format of data is based on bag of words (BoW) or document vector model and the task is to build a machine which after successfully learn the characteristic of given data set, predicts the category of the document to which the word vector belongs. In this approach document is represented by BoW where every single word is used as feature which occurs in a document. The proposed article presents artificial neural network approach which is hybrid of n-fold cross validation and training-validation-test approach for classification of data.

scholarly journals Application of Artificial Neural Network Approach for Estimating Reference Evapotranspir

2016 ◽  
Vol 11 (2) ◽  
pp. 637-647 ◽  
Author(s):  
Khyati Vyas ◽  
R Subbaiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Resources ◽  
Water Cycle ◽  
Moisture Stress ◽  
Accurate Estimation ◽  
Climatic Data ◽  
Neural Network Approach ◽  
Data Set ◽  
Artificial Neural

The process of evapotranspiration (ET) is a vital part of the water cycle. Exact estimation of the value of ET is necessary for designing irrigation systems and water resources management. Accurate estimation of ET is essential in agriculture, its over-estimation leads to cause the waste of valuable water resources and its underestimation leads to the plant moisture stress and decrease in the crop yield. The well known Penman-Monteith (PM) equation always performs the highest accuracy results of estimating reference Evapotranspiration (ET0) among the existing methods is without any discussion. However, the equation requires climatic data that are not always available particularly for a developing country. ET0 is a complex process which is depending on a number of interacting meteorological factors, such as temperature, humidity, wind speed, and radiation. The lack of physical understanding of ET0 process and unavailability of all appropriate data results in imprecise estimation of ET0. Over the past two decades, artificial neural networks (ANNs) have been increasingly applied in modeling of hydrological processes because of their ability in mapping the input–output relationship without any understanding of physical process. This paper investigates for the first time in the semiarid environment of Junagadh, the potential of an artificial neural network (ANN) for estimating ET0 with limited climatic data set.

scholarly journals A Physics-Aware Neural Network Approach for Flow Data Reconstruction From Satellite Observations

2021 ◽  
Vol 3 ◽  
Author(s):  
Luca Schweri ◽  
Sebastien Foucher ◽  
Jingwei Tang ◽  
Vinicius C. Azevedo ◽  
Tobias Günther ◽  
...  
Keyword(s):  
Neural Network ◽  
Fluid Flows ◽  
Data Recovery ◽  
Flow Data ◽  
Neural Network Approach ◽  
Data Set ◽  
Stratocumulus Clouds ◽  
Velocity Information ◽  
Wind Retrievals ◽  
Guadalupe Island

An accurate assessment of physical transport requires high-resolution and high-quality velocity information. In satellite-based wind retrievals, the accuracy is impaired due to noise while the maximal observable resolution is bounded by the sensors. The reconstruction of a continuous velocity field is important to assess transport characteristics and it is very challenging. A major difficulty is ambiguity, since the lack of visible clouds results in missing information and multiple velocity fields will explain the same sparse observations. It is, therefore, necessary to regularize the reconstruction, which would typically be done by hand-crafting priors on the smoothness of the signal or on the divergence of the resulting flow. However, the regularizers can smooth the solution excessively and will not guarantee that possible solutions are truly physically realizable. In this paper, we demonstrate that data recovery can be learned by a neural network from numerical simulations of physically realizable fluid flows, which can be seen as a data-driven regularization. We show that the learning-based reconstruction is especially powerful in handling large areas of missing or occluded data, outperforming traditional models for data recovery. We quantitatively evaluate our method on numerically-simulated flows, and additionally apply it to a Guadalupe Island case study—a real-world flow data set retrieved from satellite imagery of stratocumulus clouds.

Automatic detection of volcanic eruptions in Doppler radar observations using a neural network approach

2020 ◽  
Author(s):  
Matthias Hort ◽  
Daniel Uhle ◽  
Fabio Venegas ◽  
Lea Scharff ◽  
Jan Walda ◽  
...  
Keyword(s):  
Neural Network ◽  
Doppler Radar ◽  
Volcanic Eruptions ◽  
Radar Data ◽  
Visual Observation ◽  
Data Sets ◽  
Network Approach ◽  
Neural Network Approach ◽  
Data Set ◽  
The Impact

<p>Immediate detection of volcanic eruptions is essential when trying to mitigate the impact on the health of people living in the vicinity of a volcano or the impact on infrastructure and aviation. Eruption detection is most often done by either visual observation or the analysis of acoustic data. While visual observation is often difficult due to environmental conditions, infrasound data usually provide the onset of an event. Doppler radar data, admittedly not available for a lot of volcanoes, however, provide information on the dynamics of the eruption and the amount of material released. Eruptions can be easily detected in the data by visual analysis and here we present a neural network approach for the automatic detection of eruptions in Doppler radar data. We use data recorded at Colima volcano in Mexico in 2014/2015 and a data set recorded at Turrialba volcano between 2017 and 2019. In a first step we picked eruptions, rain and typical noise in both data sets, which were the used for training two networks (training data set) and testing the performance of the network using a separate test data set. The accuracy for classifying the different type of signals was between 95 and 98% for both data sets, which we consider quite successful. In case of the Turriabla data set eruptions were picked based on observations of OVSICORI data. When classifying the complete data set we have from Turriabla using the trained network, an additional 40 eruptions were found, which were not in the OVSICORI catalogue.</p><p>In most cases data from the instruments are transmitted to an observatory by radio, so the amount of data available is an issue. We therefore tested by what amount the data could be reduced to still be able to successfully detect an eruption. We also kept the network as small as possible to ideally run it on a small computer (e.g. a Rasberry Pi architecture) for eruption detection on site, so only the information that an eruption is detected needs to be transmitted.</p>

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