scholarly journals KAJIAN PREDIKSI FRAGMENTASI BATUAN HASIL KEGIATAN PELEDAKAN MENGGUNAKAN ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 7 (2) ◽  
pp. 145
Author(s):  
Eko Santoso ◽  
Romla Noor Hakim ◽  
Fransisikus Agung Bimantoro

Kegiatan peledakan banyak digunakan sebagai cara pemberaian batuan yang ekonomis dalam proses penambangan, dan memegang perananan penting dalam kemajuan produksi tambang. Ledakan yang optimal mampu menghasilkan fragmentasi batuan yang diinginkan. Fragmentasi batuan sangat penting karena menentukan sebagian besar penggunakan peralatan dan produktivitas. Oleh karena itu, perlu dilakukan kajian prediksi fragmentasi batuan hasil kegiatan peledakan. Teknik artificial neural network lebih disukai daripada rumus empiris karena mampu menggabungkan berbagai faktor yang mempengaruhi hasil peledakan. Penelitian ini bertujuan membandingkan hasil prediksi menggunakan artificial neural network dengan rumus empiris Kuz-Ram untuk memprediksi fragmentasi batuan dengan memperhatikan nilai error dari data yang dihasilkan. Pengembangan model artificial neural network menggunakan sistem neural network tool berbasis MATLAB R2016a dengan metode backpropagation. Pelatihan, validasi dan pengujian dilakukan dengan 99 total data peledakan yang diambil dari lapangan. Seperti burden, spasi, kedalaman, relative weight strenght bahan peledak, faktor batuan, jumlah bahan peledak per lubang ledak, dan fragmentasi batuan. Berdasarkan hasil yang diperoleh dari penelitian, model artificial neural network dengan arsitektur 15-6-1 lebih optimal dalam memprediksi fragmentasi batuan dibandingkan dengan rumus empiris Kuz-Ram. Nilai error yang dihasilkan dari artificial neural network sum square error 422.60, mean square error 0.60, root mean square error 0.78. Kata-kata kunci: Peledakan, Fragmentasi Batuan, Artificial Neural Network, Rumus Empiris Kuz-Ram.

Food Research ◽  
2021 ◽  
Vol 5 (S1) ◽  
pp. 144-151
Author(s):  
S.E. Adebayo ◽  
N. Hashim

In this study, the application of laser imaging technique was utilized to predict the quality attributes (firmness and soluble solids content) of pear fruit and to classify the maturity stages of the fruit harvested at different days after full bloom (dafb). Laser imaging system emitting at visible and near infra-red region (532, 660, 785, 830 and 1060 nm) was deployed to capture the images of the fruit. Optical properties (absorption ma and reduced scattering ms ʹ coefficients) at individual and combined wavelengths of the laser images of the fruit were used in the prediction and classifications of the maturity stages. Artificial neural network (ANN) was employed in the building of both prediction and classification models. Root mean square error of calibration (RMSEC), root mean square error of crossvalidation (RMSECV), correlation coefficient (r) and bias were used to test the performance of the prediction models while sensitivity and specificity were used to evaluate the classification models. The results showed that there was a very strong correlation between the ma and ms ʹ with pear development. This study had shown that optical properties of pears with ANN as prediction and classification models can be employed to both predict quality parameters of pear and classify pear into different (dafb) non-destructively.


2019 ◽  
Vol 20 (17) ◽  
pp. 4206 ◽  
Author(s):  
Seef Saadi Fiyadh ◽  
Mohamed Khalid AlOmar ◽  
Wan Zurina Binti Jaafar ◽  
Mohammed Abdulhakim AlSaadi ◽  
Sabah Saadi Fayaed ◽  
...  

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10−3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10−3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10−3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.


Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8592
Author(s):  
Sasanka Katreddi ◽  
Arvind Thiruvengadam

Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States of America (USA). The fuel economy of heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle parameters, etc. Although modern vehicles comply with emissions regulations, potential malfunction of the engine, regular wear and tear, or other factors could affect vehicle performance. Predicting fuel consumption per trip based on dynamic on-road data can help the automotive industry to reduce the cost and time for on-road testing. Data modeling can easily help to diagnose the reason behind fuel consumption with a knowledge of input parameters. In this paper, an artificial neural network (ANN) was implemented to model fuel consumption in modern heavy-duty trucks for predicting the total and instantaneous fuel consumption of a trip based on very few key parameters, such as engine load (%), engine speed (rpm), and vehicle speed (km/h). Instantaneous fuel consumption data can help to predict patterns in fuel consumption for optimized fleet operations. In this work, the data used for modeling was collected at a frequency of 1Hz during on-road testing of modern heavy-duty vehicles (HDV) at the West Virginia University Center for Alternative Fuels Engines and Emissions (WVU CAFEE) using the portable emissions monitoring system (PEMS). The performance of the artificial neural network was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The model was further evaluated with data collected from a vehicle on-road trip. The study shows that artificial neural networks performed slightly better than other machine learning techniques such as linear regression (LR), and random forest (RF), with high R-squared (R2) and lower root mean square error.


2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.


2017 ◽  
Vol 76 (9) ◽  
pp. 2413-2426 ◽  
Author(s):  
Seef Saadi Fiyadh ◽  
Mohammed Abdulhakim AlSaadi ◽  
Mohamed Khalid AlOmar ◽  
Sabah Saadi Fayaed ◽  
Ako R. Hama ◽  
...  

Abstract The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10−4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.


2021 ◽  
Vol 11 (4) ◽  
pp. 1885-1904
Author(s):  
Anietie Ndarake Okon ◽  
Idongesit Bassey Ansa

AbstractCalculation of water influx into petroleum reservoir is a tedious evaluation with significant reservoir engineering applications. The classical approach developed by van Everdingen–Hurst (vEH) based on diffusivity equation solution had been the fulcrum for water influx calculation in both finite and infinite-acting aquifers. The vEH model for edge-water drive reservoirs was modified by Allard and Chen for bottom-water drive reservoirs. Regrettably, these models solution variables: dimensionless influx ($$W_{{{\text{eD}}}}$$ W eD ) and dimensionless pressure ($$P_{D}$$ P D ) were presented in tabular form. In most cases, table look-up and interpolation between time entries are necessary to determine these variables, which makes the vEH approach tedious for water influx estimation. In this study, artificial neural network (ANN) models to predict the reservoir-aquifer variables $$W_{{{\text{eD}}}}$$ W eD and $$P_{D}$$ P D was developed based on the vEH datasets for the edge- and bottom-water finite and infinite-acting aquifers. The overall performance of the developed ANN models correlation coefficients (R) was 0.99983 and 0.99978 for the edge- and bottom-water finite aquifer, while edge- and bottom-water infinite-acting aquifer was 0.99992 and 0.99997, respectively. With new datasets, the generalization capacities of the developed models were evaluated using statistical tools: coefficient of determination (R2), R, mean square error (MSE), root-mean-square error (RMSE) and absolute average relative error (AARE). Comparing the developed finite aquifer models predicted $$W_{{{\text{eD}}}}$$ W eD with Lagrangian interpolation approach resulted in R2, R, MSE, RMSE and AARE of 0.9984, 0.9992, 0.3496, 0.5913 and 0.2414 for edge-water drive and 0.9993, 0.9996, 0.1863, 0.4316 and 0.2215 for bottom-water drive. Also, infinite-acting aquifer models (Model-1) resulted in R2, R, MSE, RMSE and AARE of 0.9999, 0.9999, 0.5447, 0.7380 and 0.2329 for edge-water drive, while bottom-water drive had 0.9999, 0.9999, 0.2299, 0.4795 and 0.1282. Again, the edge-water infinite-acting model predicted $$W_{{{\text{eD}}}}$$ W eD and Edwardson et al. polynomial estimated $$W_{eD}$$ W eD resulted in the R2 value of 0.9996, R of 0.9998, MSE of 4.740 × 10–4, RMSE of 0.0218 and AARE of 0.0147. Furthermore, the developed ANN models generalization performance was compared with some models for estimating $$P_{D}$$ P D . The results obtained for finite aquifer model showed the statistical measures: R2, R, MSE, RMSE and AARE of 0.9985, 0.9993, 0.0125, 0.1117 and 0.0678 with Chatas model and 0.9863, 0.9931, 0.1411, 0.3756 and 0.2310 with Fanchi equation. The infinite-acting aquifer model had 0.9999, 0.9999, 0.1750, 0.0133 and 7.333 × 10–3 with Edwardson et al. polynomial, then 0.9865, 09,933, 0.0143, 0.1194 and 0.0831 with Lee model and 0.9991, 0.9996, 1.079 × 10–3, 0.0328 and 0.0282 with Fanchi model. Therefore, the developed ANN models can predict $$W_{{{\text{eD}}}}$$ W eD and $$P_{D}$$ P D for the various aquifer sizes provided by vEH datasets for water influx calculation.


2016 ◽  
Vol 2 (11) ◽  
pp. 555-567 ◽  
Author(s):  
Samaneh Khademikia ◽  
Ali Haghizadeh ◽  
Hatam Godini ◽  
Ghodratollah Shams Khorramabadi

In this study a hybrid estimation model ANN-COA developed to provide an accurate prediction of a Wastewater Treatment Plant (WWTP). An effective strategy for detection of some output parameters tested on a hardware setup in WWTP. This model is designed utilizing Artificial Neural Network (ANN) and Cuckoo Optimization Algorithm (COA) to improve model performances; which is trained by a historical set of data collected during a 6 months operation. ANN-COA based on the difference between the measured and simulated values, allowed a quick revealing of the faults. The method could obtain the fault detection and used in solving continuous and discrete optimization problems, successfully. After constructing and modelling the method, selected performance indices including coefficient of Regression, Mean-Square Error, Root-Mean-Square Error and Aggregated Measure used to compare the obtained results. This analysis revealed that the hybrid ANN-COA model offers a higher degree of accuracy for predicting and control the WWTP.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 53
Author(s):  
Joohwan Sung ◽  
Sungmin Han ◽  
Heesu Park ◽  
Hyun-Myung Cho ◽  
Soree Hwang ◽  
...  

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.


2021 ◽  
Vol 29 (3) ◽  
pp. 368-380
Author(s):  
Cristina Ghinea ◽  
Petronela Cozma ◽  
Maria Gavrilescu

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.


Author(s):  
Husin Ibrahim ◽  
Abdi Hanra Sebayang ◽  
S. Dharma ◽  
A.S. Silitonga

Tulisan ini meneliti kinerja mesin diesel satu silinder menggunakan campuran bahan bakar biodiesel randu dengan solar. Tes dilakukan dengan berbagai perbandingan biodiesel-diesel (B10, B20 dan B30). Sebuah model artificial neural network (ANN) yang didasarkan pada algoritma back-propagasi standar digunakan untuk memprediksi kinerja mesin menggunakan MATLAB. Untuk memperoleh data untuk pelatihan dan pengujian yang diusulkan ANN, kecepatan mesin yang berbeda (1400-2200 rpm) dipilih sebagai parameter masukan, sedangkan kinerja mesin (BSFC dan BTE) dipilih sebagai parameter keluaran untuk ANN pemodelan dari mesin diesel. Kinerja mesin (BSFC dan BTE) ANN telah divalidasi dengan membandingkan hasil prediksi dengan hasil eksperimen. Hasil penelitian menunjukkan bahwa koefisien korelasi BSFC dan BTE masing masing adalah 0,99249 dan 0,99457. Nilai MAPE (mean absolute persentase kesalahan) BSFC dan BTE adalah 0,57467 dan 0,33424 dan root mean square (RSME) nilai di bawah 5% oleh model, yang diterima. Studi ini menunjukkan bahwa pemodelan teknik sebagai pendekatan dalam energi alternatif dapat memberikan peningkatan keuntungan dari kehandalan dalam prediksi kinerja mesin pembakaran dalam. 


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