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.

Using complex permittivity and artificial neural networks to identify and classify copper, zinc, and lead contamination in soil

2006 ◽  
Vol 43 (1) ◽  
pp. 100-109 ◽  
Author(s):  
F Amegashie ◽  
J Q Shang ◽  
E K Yanful ◽  
W Ding ◽  
S Al-Martini
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Heavy Metal ◽  
Artificial Neural Networks ◽  
Complex Permittivity ◽  
Soil Samples ◽  
Lead Contamination ◽  
Subsurface Contamination ◽  
Ann Models ◽  
Artificial Neural

Complex permittivity measurements combined with artificial neural networks (ANNs) are investigated as a method for assessing and identifying heavy metal contamination in soil. The measurements are carried out with a custom-built device on 164 compacted samples of a natural clayey soil, artificially contaminated with various simple salts including heavy metals (Cu, Zn, and Pb). The soil samples are prepared by mixing solutions of the various salts with the soil at various concentrations and water contents. A database has been set up consisting of complex per mittivity measurements made between the frequencies of 200 and 500 MHz and measured physical and chemical properties of the soil samples. Using this database as input, two ANN models are designed, the first to detect the presence or absence of heavy metals in the soil samples and the second to determine whether the heavy metal, if present in a given sample, is Cu, Zn, or Pb. Both ANN models perform reasonably well. Overall, the first model is able to detect the presence of heavy metals in 92.7% of cases, and the second is successful in distinguishing the particular type of heavy metal in 76.4% of all the samples containing heavy metals. These encouraging results underscore the potential of complex permittivity and ANNs as promising tools for nondestructive subsurface contamination assessment.Key words: heavy metals, subsurface contamination, complex permittivity, artificial neural networks, contaminant detection.

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.

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.

Application of artificial neural networks to predict the heavy metal contamination in the Bartin River

2020 ◽  
Vol 27 (34) ◽  
pp. 42495-42512 ◽  
Author(s):  
Handan Ucun Ozel ◽  
Betul Tuba Gemici ◽  
Ercan Gemici ◽  
Halil Baris Ozel ◽  
Mehmet Cetin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heavy Metal ◽  
Artificial Neural Networks ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Artificial Neural

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.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

scholarly journals Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 44
Author(s):  
Fernando A. N. Silva ◽  
João M. P. Q. Delgado ◽  
Rosely S. Cavalcanti ◽  
António C. Azevedo ◽  
Ana S. Guimarães ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Cross Sections ◽  
Influential Factors ◽  
Average Error ◽  
Ann Model ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Artificial Neural

The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

scholarly journals Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 854
Author(s):  
Nevena Rankovic ◽  
Dragica Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Time Estimation ◽  
Effort Estimation ◽  
Activation Functions ◽  
Estimation Model ◽  
Wide Range ◽  
Software Product ◽  
Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

scholarly journals Characterization of a Mobile Waste-Robot: a Heuristic Method to Path Planning using Artificial Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 3902-3910
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Path Planning ◽  
Mobile Robotics ◽  
Heuristic Method ◽  
Correlation Coefficients ◽  
Path Tracking ◽  
Ann Model ◽  
Artificial Neural ◽  
Tracking Time

In the field of mobile robotics, path planning is one of the most widely-sought areas of interest due to its nature of complexity, where such issue is also practically evident in the case of mobile robots used for waste disposal purposes. To overcome issues on path planning, researchers have studied various classical and heuristic methods, however, the extent of optimization applicability and accuracy still remain an opportunity for further improvements. This paper presents the exploration of Artificial Neural Networks (ANN) in characterizing the path planning capability of a mobile waste-robot in order to improve navigational accuracy and path tracking time. The author utilized proximity and sound sensors as input vectors, dual H-bridge Direct Current (DC) motors as target vectors, and trained the ANN model using Levenberg-Marquardt (LM) and Scaled Conjugate (SCG) algorithms. Results revealed that LM was significantly more accurate than SCG algorithm in local path planning with Mean Square Error (MSE) values of 1.75966, 2.67946, and 2.04963, and Regression (R) values of 0.995671, 0.991247, and 0.983187 in training, testing, and validation environments, respectively. Furthermore, based on simulation results, LM was also found to be more accurate and faster than SCG with Pearson R correlation coefficients of rx=.975, nx=6, px=0.001 and ry=.987, ny=6, py=0.000 and path tracking time of 8.47s.

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