Comparison of RSM and ANN Optimization Techniques and Modeling of Ultrasonic Energy Assisted Trans-esterification of Salviniaceae Filiculoides Oil Blended to Biodiesel

For the transesterification of biodiesel from Azolla oil, the safe and successful use of feed stocks is a very significant prerequisite. It is of high importance to determine the optimal reaction parameters to maximize the yield of low-cost biodiesel generated from Azolla oil. Ultrasonic energy was used in this work for the development of biodiesel from Azolla oil catalyzed by the KOH catalyst under different conditions. The effect on the transesterification of Azolla Oil to biodiesel of four reaction parameters, namely the methanol/Azolla oil molar ratio (A), KOH catalyst concentration (B), reaction time (C) and reaction temperature (D) were considered. In order to optimize the effects of reaction parameters for the transesterification of Azolla oil to biodiesel, response surface methodology (RSM) based on central composite rotatable design (CCRD) is applied. To obtain a good correlation between the input reaction parameters and the output response parameter (FAME yield) from Azolla oil to biodiesel, an artificial neural network (ANN) model with two feed-forward back-propagation neural-network architecture Multilayer Perceptron Network (MLP) and Radial Basis Function Network (RBFN) was developed. With the experimental information obtained from the RSM model, the built ANN models were trained and evaluated. Absolute Average Deviation (AAD), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination were statistically compared with the predictive capacity of both RSM and ANN models (R2). The statistical analysis showed that the measured FAME yield from both the RSM and ANN models was able to predict the FAME yield, and the findings limited the ANN model to the much more reliable FAME yield prediction compared to the RSM model.

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Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Energies ◽

10.3390/en11092216 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2216 ◽

Cited By ~ 4

Author(s):

Ravi Kishore ◽

Roop Mahajan ◽

Shashank Priya

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Electrical Energy ◽

Optimization Techniques ◽

Operating Conditions ◽

Ann Model ◽

Predicting Performance ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

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Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

Scientific Reports ◽

10.1038/s41598-021-81556-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhonghui Thong ◽

Jolena Ying Ying Tan ◽

Eileen Shuzhen Loo ◽

Yu Wei Phua ◽

Xavier Liang Shun Chan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Model ◽

Sensitivity Threshold ◽

Ann Model ◽

Blood Samples ◽

Ann Models ◽

Artificial Neural ◽

The Impact ◽

Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

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Application of artificial neural network to control the coagulant dosing in water treatment plant

Water Science & Technology ◽

10.2166/wst.2000.0410 ◽

2000 ◽

Vol 42 (3-4) ◽

pp. 403-408 ◽

Cited By ~ 8

Author(s):

R.-F. Yu ◽

S.-F. Kang ◽

S.-L. Liaw ◽

M.-c. Chen

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Water Treatment ◽

Regression Model ◽

Treatment Plant ◽

Water Treatment Plant ◽

Ann Model ◽

Coagulant Dosage ◽

Ann Models

Coagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pHin) and conductivity (Conin) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment.

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A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Sensors ◽

10.3390/s20072058 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2058 ◽

Cited By ~ 2

Author(s):

Salaheldin Elkatatny ◽

Ahmed Al-AbdulJabbar ◽

Khaled Abdelgawad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mutual Effect ◽

Percentage Error ◽

Drilling Process ◽

Ann Model ◽

Data Set ◽

Individual Parameter ◽

Rate Of Penetration ◽

Artificial Neural

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

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Development of Neural Network Models for a Crude Oil Distillation Column

Jurnal Teknologi ◽

10.11113/jt.v38.509 ◽

2012 ◽

Author(s):

Khairiyah Mohd. Yusof ◽

Fakhri Karray ◽

Peter L. Douglas

Keyword(s):

Neural Network ◽

Steady State ◽

Crude Oil ◽

Distillation Column ◽

Network Models ◽

Ann Model ◽

Neural Network Models ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Crude Oil Distillation

This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the model is developed for real time optimisation (RTO) applications they are steady state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.

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Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

10.4018/978-1-6684-2408-7.ch062 ◽

2022 ◽

pp. 1287-1300

Author(s):

Balaji Prabhu B. V. ◽

M. Dakshayini

Keyword(s):

Neural Network ◽

Crop Production ◽

Demand Forecasting ◽

Forecasting Model ◽

Ann Model ◽

Demand Prediction ◽

Computational Performance ◽

Societal Needs ◽

Ann Models ◽

Artificial Neural Network Ann

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

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Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

Scientific Reports ◽

10.1038/s41598-020-73931-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Hung Vo Thanh ◽

Yuichi Sugai ◽

Kyuro Sasaki

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Oil Recovery ◽

Early Stage ◽

Computational Time ◽

Residual Oil ◽

Ann Model ◽

Ann Models ◽

Artificial Neural ◽

And Storage

Abstract Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

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Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2020100103 ◽

2020 ◽

Vol 12 (4) ◽

pp. 35-47

Author(s):

Balaji Prabhu B. V. ◽

M. Dakshayini

Keyword(s):

Neural Network ◽

Crop Production ◽

Demand Forecasting ◽

Ann Model ◽

Demand Prediction ◽

Computational Performance ◽

Societal Needs ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Agricultural Food

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Effective Implementation of Value Engineering Using Artificial Neural Network Aid of Optimization Techniques

International Journal of Air-Conditioning and Refrigeration ◽

10.1142/s2010132519500226 ◽

2019 ◽

Vol 27 (03) ◽

pp. 1950022 ◽

Cited By ~ 2

Author(s):

M. Prem Swarup ◽

A. Prabhu Kumar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Research Work ◽

Optimization Techniques ◽

Value Engineering ◽

Training Algorithms ◽

Air Conditioner ◽

Ann Model ◽

Artificial Neural ◽

Artificial Neural Network Ann

Value Engineering (VE) is a method for characterizing the developed requirements of a product, and it is concerned with the selection of less excessive conditions. VE can understand and improve the optimal outcome such as quantity, security, unwavering quality and convertibility of each managerial unit. It is an incredible solving tool that can diminish costs while preserving or improving performance and quality requirements. In this research work, VE is presented to calculate the heating cost and cooling cost of the air conditioner with the assistance of an Artificial Neural Network (ANN) with an optimization model. This ANN model effectively chooses the maximum number of sources obtainable and the source respective method with low functional cost and energy consumption. For improving the prediction accuracy of VE in the ANN model, we have incorporated some training algorithms and optimized the network hidden layer and hidden neuron by Opposition Genetic Algorithm (OGA). From the results, trained ANN with OGA predicts the output with 96.02% accuracy and also minimum errors compared with the existing GA process.

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Neural Network MIMO Model for Production of Isopropyl Myristate in a Semibatch Reactive Distillation Column

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.403 ◽

2013 ◽

Vol 284-287 ◽

pp. 403-408

Author(s):

Nur Alwani Ali Bashah ◽

Mohd Roslee Othman ◽

Norashid Aziz

Keyword(s):

Neural Network ◽

Reactive Distillation ◽

Batch Reactor ◽

Isopropyl Myristate ◽

Ann Model ◽

Validation Data ◽

Testing Data ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Mimo Model

Batch reactive distillation is an integrated unit of batch reactor and distillation. It provides benefits of having higher conversion and yield by continuous removal of side product. The aim of this paper is to develop an artificial neural network (ANN) based model for production of isopropyl myristate in an industrial scaled semibatch reactive distillation. Two cases of the MIMO model were developed. Case 1 does not consider historical data as inputs while case 2 does. The trained ANN for both cases was validated with independent validation data and the best architecture was optimized. Case 1 resulted to 8 inputs, 14 hidden nodes and 2 outputs [8-14-2] ANN while Case 2 resulted to [12-13-2] ANN. The results show that both ANN models have ability to predict the unknown validation and testing data very well. However, the [8-14-2] ANN model produce higher accuracy than [12-13-2] ANN model with MSE of 0.00094 and 0.0013, respectively.

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