Applicability of Neural Networks for the Fermentation of Propionic Acid by Propionibacterium acidipropionici

Ann Model ◽

Propionibacterium Acidipropionici ◽

Fermentation Time ◽

Data Points ◽

Prediction Capability

According to our best knowledge, this is the first report applying Artificial neural networks (ANN) for simulation of batch propionic acid (PA) fermentation. Therefore, the main focus of this research was to investigate the applicability of ANN on PA fermentations. To demonstrate this, we used the results of 40 Propionibacterium acidipropionici fermentations (ca 2,000 data points) to build up the ANN, and additional two independent fermentations to demonstrate the prediction capability of the observed ANN. Analyzing the predicted output parameters we observed, that ratio of propionic acid to acetic acid (PA/AA) variables can only be used for ANN after normalization. Finally, the fit of the ANN model to the measured data was fine (average correlation coefficients over 0.9). A special feature was also tested: fermentation time was also used as an input parameter, thus making the ANN suitable to predict time course of PA fermentations as well which was also satisfying.

Workflow to build a continuous static elastic moduli profile from the drilling data using artificial intelligence techniques

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01274-3 ◽

2021 ◽

Author(s):

Osama Siddig ◽

Salaheldin Elkatatny

Keyword(s):

Young’S Modulus ◽

Elastic Moduli ◽

Young's Modulus ◽

Wellbore Stability ◽

Machine Learning Techniques ◽

Support Vector ◽

Ann Model ◽

Weight On Bit ◽

Data Points

AbstractRock mechanical properties play a crucial role in fracturing design, wellbore stability and in situ stresses estimation. Conventionally, there are two ways to estimate Young’s modulus, either by conducting compressional tests on core plug samples or by calculating it from well log parameters. The first method is costly, time-consuming and does not provide a continuous profile. In contrast, the second method provides a continuous profile, however, it requires the availability of acoustic velocities and usually gives estimations that differ from the experimental ones. In this paper, a different approach is proposed based on the drilling operational data such as weight on bit and penetration rate. To investigate this approach, two machine learning techniques were used, artificial neural network (ANN) and support vector machine (SVM). A total of 2288 data points were employed to develop the model, while another 1667 hidden data points were used later to validate the built models. These data cover different types of formations carbonate, sandstone and shale. The two methods used yielded a good match between the measured and predicted Young’s modulus with correlation coefficients above 0.90, and average absolute percentage errors were less than 15%. For instance, the correlation coefficients for ANN ranged between 0.92 and 0.97 for the training and testing data, respectively. A new empirical correlation was developed based on the optimized ANN model that can be used with different datasets. According to these results, the estimation of elastic moduli from drilling parameters is promising and this approach could be investigated for other rock mechanical parameters.

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

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d8324.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 3902-3910

Keyword(s):

Neural Networks ◽

Path Planning ◽

Mobile Robotics ◽

Heuristic Method ◽

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.

Winter surface air temperature prediction over Japan using artificial neural networks

Weather and Forecasting ◽

10.1175/waf-d-20-0218.1 ◽

2021 ◽

Author(s):

J. V. Ratnam ◽

Masami Nonaka ◽

Swadhin K. Behera

Keyword(s):

Neural Networks ◽

Air Temperature ◽

Surface Air Temperature ◽

Ann Model ◽

Anomaly Correlation ◽

The North ◽

Skill Scores ◽

Artificial Neural

AbstractThe machine learning technique, namely Artificial Neural Networks (ANN), is used to predict the surface air temperature (SAT) anomalies over Japan in the winter months of December, January and February for the period 1949/50 to 2019/20. The predictions are made for the four regions Hokkaido, North, Central and West of Japan. The inputs to the ANN model are derived from the anomaly correlation coefficients among the SAT anomalies over the regions of Japan and the global SAT and sea surface temperature anomalies. The results are validated using anomaly correlation coefficient (ACC) skill scores with the observation. It is found that the ANN predictions over Hokkaido have higher ACC skill scores compared to the ACC scores over the other three regions. The ANN predicted SAT anomalies are compared with that of ensemble mean of 8 of the North American Multi-Model Ensemble (NMME) models besides comparing them with the persistent anomalies. The ANN predictions over all the four regions have higher ACC skill scores compared to the NMME model skill scores in the common period of 1982/83 to 2018/19. The ANN predicted SAT anomalies also have higher Hit rate and lower False alarm rate compared to the NMME predicted SAT anomalies. All these indicate that the ANN model is a promising tool for predicting the winter SAT anomalies over Japan.

Evaluation Compressive Strength of Cement-Limestone-Slag Ternary Blended Concrete Using Artificial Neural Networks (ANN) and Gene Expression Programming (GEP)

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.837.119 ◽

2020 ◽

Vol 837 ◽

pp. 119-124

Author(s):

Xiao Yong Wang

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Gene Expression Programming ◽

Concrete Strength ◽

Strength Development ◽

Ann Model ◽

Artificial Neural ◽

Ternary Blended Concrete

Limestone and slag blended concrete is an innovative concrete which belongs to the family of limestone calcined clay cement (LC3) concrete. Strength is an important property of structural concrete. This study shows artificial neural networks (ANN) and gene expression programming (GEP) models for predicting strength development of limestone and slag blended concrete. ANN model consists of an input layer, a hidden layer, and output layer. GEP model consists of the sum of three expression trees. The input parameters of ANN and GEP models are mixtures and ages. The output parameter is a strength. The correlation coefficients of ANN and GEP model are 0.99 and 0.98, respectively. Both ANN and GEP model can produce prediction results of the strength of ternary blended concrete reliably.

Predicting fire resistance ratings of timber structures using artificial neural networks

Journal of Science and Technology in Civil Engineering (STCE) - NUCE ◽

10.31814/stce.nuce2020-14(2)-03 ◽

2020 ◽

Vol 14 (2) ◽

pp. 28-39

Author(s):

Pham Thanh Tung ◽

Pham Thanh Hung

Keyword(s):

Neural Networks ◽

Sensitivity Analysis ◽

Fire Resistance ◽

Input Parameter ◽

Training Algorithms ◽

Ann Model ◽

Wooden Floor ◽

Artificial Neural ◽

Input Variables

This paper describes a method to predict the fire resistance ratings of the wooden floor assemblies using Artificial Neural Networks. Experimental data collected from the previously published reports were used to train, validate, and test the proposed ANN model. A series of model configurations were examined using different popular training algorithms to obtain the optimal structure for the model. It is shown that the proposed ANN model can successfully predict the fire resistance ratings of the wooden floor assemblies from the input variables with an average absolute error of four percent. Besides, the sensitivity analysis was conducted to explore the effects of the separate input parameter on the output. Results from analysis revealed that the fire resistance ratings are sensitive to the change of Applied Load (ALD) and the number of the Ceiling Finish Layer (CFL) input variables. On the other hand, the outputs are less sensitive to a variation of the Joist Type (JTY) parameter. Keywords: artificial neural networks; fire resistance; wooden floor assembly; sensitivity analysis.

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

Journal of Energy Resources Technology ◽

10.1115/1.4049603 ◽

2021 ◽

Vol 143 (11) ◽

Author(s):

Zehua Chen ◽

Daoyong Yang

Keyword(s):

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 Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks

Energies ◽

10.3390/en12112125 ◽

2019 ◽

Vol 12 (11) ◽

pp. 2125 ◽

Cited By ~ 13

Author(s):

Ahmed Abdulhamid Mahmoud ◽

Salaheldin Elkatatny ◽

Abdulwahab Ali ◽

Tamer Moussa

Keyword(s):

Neural Networks ◽

High Accuracy ◽

Well Logs ◽

Ann Model ◽

Validation Data ◽

Static Young’S Modulus ◽

Data Points ◽

Sandstone Formation ◽

Very High

In this study, we used artificial neural networks (ANN) to estimate static Young’s modulus (Estatic) for sandstone formation from conventional well logs. ANN design parameters were optimized using the self-adaptive differential evolution optimization algorithm. The ANN model was trained to predict Estatic from conventional well logs of the bulk density, compressional time, and shear time. The ANN model was trained on 409 data points from one well. The extracted weights and biases of the optimized ANN model was used to develop an empirical relationship for Estatic estimation based on well logs. This empirical correlation was tested on 183 unseen data points from the same training well and validated using data from three different wells. The optimized ANN model estimated Estatic for the training dataset with a very low average absolute percentage error (AAPE) of 0.98%, a very high correlation coefficient (R) of 0.999 and a coefficient of determination (R2) of 0.9978. The developed ANN-based correlation estimated Estatic for the testing dataset with a very high accuracy as indicated by the low AAPE of 1.46% and a very high R and R2 of 0.998 and 0.9951, respectively. In addition, the visual comparison of the core-tested and predicted Estatic of the validation dataset confirmed the high accuracy of the developed ANN-based empirical correlation. The ANN-based correlation overperformed four of the previously developed Estatic correlations in estimating Estatic for the validation data, Estatic for the validation data was predicted with an AAPE of 3.8% by using the ANN-based correlation compared to AAPE’s of more than 36.0% for the previously developed correlations.

Faking Signals to Fool Deep Neural Networks in AMC via Few Data Points

IEEE Access ◽

10.1109/access.2021.3106704 ◽

2021 ◽

pp. 1-1

Author(s):

Hongbin Ma ◽

Shuyuan Yang ◽

Guangjun He ◽

Ruowu Wu ◽

Xiaojun Hao ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Data Points ◽

Few Data

Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Processes ◽

10.3390/pr9061070 ◽

2021 ◽

Vol 9 (6) ◽

pp. 1070

Author(s):

Abdul Gani Abdul Jameel

Keyword(s):

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.