Predicting Viscosities of Heavy Oils and Solvent–Heavy Oil Mixtures Using Artificial Neural Networks

2021 ◽  
Vol 143 (11) ◽  
Author(s):  
Zehua Chen ◽  
Daoyong Yang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Viscosity Data ◽  
Heavy Oils ◽  
Ann Model ◽  
Wide Range ◽  
Data Points ◽  
Ann Models ◽  
Artificial Neural

Abstract This study investigates the potential of artificial neural networks (ANNs) to accurately predict viscosities of heavy oils (HOs) as well as mixtures of solvents and heavy oils (S–HOs). The study uses experimental data collected from the public domain for HO viscosities (involving 20 HOs and 568 data points) and S–HO mixture viscosities (involving 12 solvents and 4057 data points) for a wide range of temperatures, pressures, and mass fractions. The natural logarithm of viscosity (instead of viscosity itself) is used as predictor and response variables for the ANNs to significantly improve model performance. Gaps in HO viscosity data (with respect to pressure or temperature) are filled using either the existing correlations or ANN models that innovatively use viscosity ratios from the available data. HO viscosities and mixture viscosities (weight-based, molar-based, and volume-based) from the trained ANN models are found to be more accurate than those from commonly used empirical correlations and mixing rules. The trained ANN model also fares well for another dataset of condensate-diluted HOs.

Estimation of Groundwater Level Using Artificial Neural Networks: a Case Study of Hatay-Turkey

2017 ◽  
Author(s):  
Fatih Üneş ◽  
Mustafa Demirci ◽  
Eyup Ispir ◽  
Yunus Ziya Kaya ◽  
Mustafa Mamak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Groundwater Level ◽  
Underground Water ◽  
Population Increase ◽  
Ann Model ◽  
Total Water Consumption ◽  
Ann Models ◽  
Artificial Neural

Groundwater, which is a strategic resource in Turkey, is used for drinking-use, agricultural irrigation and industrial purposes. Population increase and total water consumption are constantly increasing. In order to meet the need for water, over-shoots from underground water have caused significant falls in groundwater level. Estimation of water level is important for planning an efficient and sustainable groundwater management. In this study, groundwater level, monthly mean precipitation and temperature observations of Turkish General Directorate of State Hydraulic Works (DSI) in Hatay, Amik Plain, Kumlu district were used between 2000 and 2015 years. The performance evaluation was done by creating Multi Linear Regression (MLR) and Artificial Neural Networks (ANN) models. The ANN model gave better results than the MLR model.

Detecting heavy metal contamination in soil using complex permittivity and artificial neural networks

2004 ◽  
Vol 41 (6) ◽  
pp. 1054-1067 ◽  
Author(s):  
J Q Shang ◽  
W Ding ◽  
R K Rowe ◽  
L Josic
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Heavy Metal ◽  
Artificial Neural Networks ◽  
Complex Permittivity ◽  
Heavy Metal Contamination ◽  
Zinc Salt ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural

The use of the complex permittivity, an intrinsic electrical property of materials, to detect the presence and type of heavy metals in soil is investigated. The soil specimens are prepared by mixing the soil with distilled and deionized water, NaCl solutions, and copper and zinc salt solutions and compacting at known water contents. The complex permittivities of the soil specimens are measured in the laboratory using a custom-developed apparatus. A database, which includes both contaminated and uncontaminated soil specimens, is developed, with the soil water content, density, and pore-fluid salinity varying over a relatively wide range. Two artificial neural network (ANN) models are developed to (i) identify whether the heavy metals are present in the soil; and, if so, (ii) distinguish the metal type, based on the complex permittivities measured on the soil specimens. The first ANN model (identification) can correctly identify the presence of heavy metals in 90% of cases. The second ANN model (classification) can correctly classify the type of the heavy metal in 95% of cases. Better performance can be achieved if more complex permittivity data are available for the training of the networks.Key words: heavy metals, soil contamination, contamination detection, complex permittivity, artificial neural networks.

Modification of PET fabrics by hyperbranched polymer: a comparative study of artificial neural networks (ANN) and statistical approach

2013 ◽  
Vol 33 (5) ◽  
pp. 445-452 ◽  
Author(s):  
Mahdi Hasanzadeh ◽  
Tahereh Moieni ◽  
Bentolhoda Hadavi Moghadam
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Three Dimensional ◽  
Ann Model ◽  
Acid Dyes ◽  
Color Strength ◽  
Ann Models ◽  
Artificial Neural ◽  
Poly Ethylene ◽  
Rsm Model

Abstract Hyperbranched polymers (HBPs) are highly branched, three-dimensional and polydisperse macromolecules and have been employed for modification of poly(ethylene terephthalate) (PET) fabrics. The PET fabrics treatment process parameters, like HBP concentration, temperature and time, play a major role in treatment yield and dyeability of treated PET fabrics by acid dyes. Two different quantitative models, comprising response surface methodology (RSM) and artificial neural networks (ANN), were developed for predicting color strength (K/S value) of treated fabrics. The experiments were conducted based on central composite design (CCD) and a mathematical model was developed. A comparison of the predicted color strength using RSM and ANN was studied. The results obtained indicated that both RSM and ANN models show a very good relationship between the experimental and predicted response values. However, the ANN model shows more accurate results than the RSM model.

scholarly journals Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation

2005 ◽  
Vol 2 (1) ◽  
pp. 365-415 ◽  
Author(s):  
N. J. de Vos ◽  
T. H. M. Rientjes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Goodness Of Fit ◽  
Model Performance ◽  
Performance Measure ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Trade Offs ◽  
Hourly Data ◽  
Artificial Neural

Abstract. The application of Artificial Neural Networks (ANNs) on rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling in the Geer catchment (Belgium) using both daily and hourly data. The good daily forecast results indicate that ANNs can be considered alternatives for traditional rainfall-runoff modelling approaches. However, investigation of the forecasts based on hourly data reveal a constraint that has hitherto been neglected by hydrologists. A timing error occurs due to a dominating autoregressive component that is introduced by using previous runoff values as ANN model input. The reason for the popular practice of using these previous runoff data is that this information indirectly represents the hydrological state of the catchment. Two possible solutions to this timing problem are discussed. Firstly, several alternatives for representation of the hydrological state are presented: moving averages over the previous discharge and over the previous rainfall, and the output of the simple GR4J model component for soil moisture. A combination of these various hydrological state representators produces good results in terms of timing, but the overall goodness of fit is not as good as the simulations with previous runoff data. Secondly, the use of a combination of multiple measures of model performance during ANN training is suggested, since not all differences between modelled and observed hydrograph characteristics such as timing, volume, and absolute values can be adequately expressed by a single performance measure. The possible undervaluation of timing errors by the commonly-used squared-error-based functions is a clear example of this inability.

Simulation of Grinding by Means of the Finite Element Method and Artificial Neural Networks

2012 ◽  
pp. 193-218
Author(s):  
A.P. Markopoulos
Keyword(s):  
Neural Networks ◽  
Finite Element Method ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
The Finite Element Method ◽  
Ann Models ◽  
Artificial Neural ◽  
Complex Phenomena ◽  
Element Method

Simulation of grinding is a topic of great interest due to the wide application of the process in modern industry. Several modeling methods have been utilized in order to accurately describe the complex phenomena taking place during the process, the most common being the Finite Element Method (FEM) and the Artificial Neural Networks (ANN). In the present work, a FEM model and an ANN model for precision surface grinding, are presented. Furthermore, a new approach, a combination of the aforementioned methods, is proposed, and a hybrid model is presented. This model comprises the advantages of both FEM and ANN models. The three kinds of models described in this work are able to accurately predict several grinding features that define the outcome of the process and the quality of the final product.

scholarly journals Prediction of NOx Emissions for a Range of Engine Hardware Configurations Using Artificial Neural Networks

2020 ◽  
Author(s):  
Nick Papaioannou ◽  
XiaoHang Fang ◽  
Felix Leach ◽  
Martin H. Davy
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Operating Conditions ◽  
Data Accuracy ◽  
Nox Emissions ◽  
Ann Model ◽  
Baseline Training ◽  
Wide Range ◽  
Artificial Neural

Abstract The predictive ability of artificial neural networks where a large number of experimental data are available, has been studied extensively. Studies have shown that ANN models are capable of accurately predicting NOx emissions from engines under various operating conditions and different fuel types when trained well. One of the major advantages of an ANN model is its ability to relearn when new experimental data is available, thus continuously improving its accuracy. The present work explored the potential of an ANN model to predict NOx emissions for various engine configurations outside its training envelop. This work also looked into quantifying the amount of new data required to improve the accuracy of the model when exposed to unknown conditions. The chosen ANN model was constructed using data from a high-speed direct injection diesel engine and is capable of accurate NOx emissions over a wide range of operating conditions. The optimized network utilized 14 input parameters and is using 6 neurons in a single hidden layer feed-forward neural network. Experimental data from the various engine configurations tested, were then used to predict NOx from the existing ANN model. The results indicate that when the new data are within the baseline training envelop, the ANN model is capable of accurate NOx prediction even when there are substantial changes in engine configuration such as piston material. Similar results were also observed when the injector nozzle is changed. However, the model’s performance drops significantly when new data, outside the baseline training envelop, were employed indicating that additional training is required. As such, various methods for retraining the ANN model were explored with the selected method showing the best compromise between new-data accuracy and old-data accuracy retention. The retrained ANN model developed was found to be an effective tool in predicting NOx emissions for different engine configurations and operating conditions.

scholarly journals Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation

2005 ◽  
Vol 9 (1/2) ◽  
pp. 111-126 ◽  
Author(s):  
N. J. de Vos ◽  
T. H. M. Rientjes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Goodness Of Fit ◽  
Model Performance ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Trade Offs ◽  
Hourly Data ◽  
Artificial Neural ◽  
Daily Forecast

Abstract. The application of Artificial Neural Networks (ANNs) in rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling of the Geer catchment (Belgium) using both daily and hourly data. The daily forecast results indicate that ANNs can be considered good alternatives for traditional rainfall-runoff modelling approaches, but the simulations based on hourly data reveal timing errors as a result of a dominating autoregressive component. This component is introduced in model simulations by using previously observed runoff values as ANN model input, which is a popular method for indirectly representing the hydrological state of a catchment. Two possible solutions to this problem of lagged predictions are presented. Firstly, several alternatives for representation of the hydrological state are tested as ANN inputs: moving averages over time of observed discharges and rainfall, and the output of the simple GR4J model component for soil moisture. A combination of these hydrological state representers produces good results in terms of timing, but the overall goodness of fit is not as good as the simulations with previous runoff data. Secondly, the possibility of using multiple measures of model performance during ANN training is mentioned.

Hydrological modelling using artificial neural networks

2001 ◽  
Vol 25 (1) ◽  
pp. 80-108 ◽  
Author(s):  
C. W. Dawson ◽  
R. L. Wilby
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Model Complexity ◽  
Network Architectures ◽  
Training Algorithms ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Validation Data ◽  
Artificial Neural

This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conventional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.

scholarly journals Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Mehdi Nikoo ◽  
Farshid Torabian Moghadam ◽  
Łukasz Sadowski
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
Ann Models ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Different Characteristics ◽  
Mlr Model

Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

scholarly journals Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1070
Author(s):  
Abdul Gani Abdul Jameel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Groups ◽  
Average Error ◽  
Ann Model ◽  
Oh Groups ◽  
Unseen Data ◽  
Learning Capabilities ◽  
Artificial Neural ◽  
Oxygenated Fuels

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

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