scholarly journals Effect of plastic oil addition on performance and emission characteristics of biogas-diesel dual fuel engine using taguchi method and prediction of performance parameter using artificial neural network

2021 ◽  
Vol 850 (1) ◽  
pp. 012033
Author(s):  
P. Laxmi Narasimha Raju ◽  
Manas ◽  
Pavan Sai A. ◽  
M B Shyam Kumar ◽  
Ayub Ahmed Janvekar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Taguchi Method ◽  
Research Work ◽  
Dual Fuel ◽  
Emission Characteristics ◽  
Fuel Blend ◽  
Performance And Emission ◽  
Marquardt Algorithm ◽  
Artificial Neural

Abstract Ever increasing usage of fossil fuels and dwindling natural resources led researchers to concentrate on investigating other sources which can satisfy our demands and reduce pollution levels. Present research work aims to investigate the performance and emission characteristics of plastic, diesel and biogas as fuel blend operated in a dual-fuel engine with biogas as a primary fuel and plastic oil – diesel blends as secondary fuel and also predict the output variable using artificial neural network. A modified four-stroke single cylinder CI engine was used for experiments conducted at varying load, percentage of plastic oil percentage in diesel and biogas flow rate. Based on the levels and factors a Taguchi L9 orthogonal matrix was designed to find the optimal combination of input indices. The signal to noise ratios in taguchi method were applied based on the desired output characteristics and according to the respective SNR ratios an ANOVA table was created to determine the major contributor effecting output parameters such as brake thermal efficiency, CO, HC NOx and smoke emissions. ANN model helped to predict BTE with same input parameters but with an increased set of sample data. Based on Gradient descent and Levenberg-Marquardt algorithm the ANN architecture was trained, validated and tested to predict the response with least error. The ANOVA calculated indicates load to be the prime factor effecting BTE and NOx emission

scholarly journals Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 584
Author(s):  
Vinay Arora ◽  
Sunil Kumar Mahla ◽  
Rohan Singh Leekha ◽  
Amit Dhir ◽  
Kyungroul Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Engine ◽  
Activation Mechanism ◽  
Coefficient Of Determination ◽  
Dual Fuel ◽  
Biological Origin ◽  
Research Issues ◽  
Performance And Emission ◽  
Artificial Neural

Biogas is a significant renewable fuel derived by sources of biological origin. One of today’s research issues is the effect of biofuels on engine efficiency. The experiments on the engine are complicated, time consuming and expensive. Furthermore, the evaluation cannot be carried out beyond the permissible limit. The purpose of this research is to build an artificial neural network successfully for dual fuel diesel engine with a view to overcoming experimental difficulties. Authors used engine load, bio-gas flow rate and n-butanol concentration as input parameters to forecast target variables in this analysis, i.e., smoke, brake thermal efficiency (BTE), carbon monoxide (CO), hydrocarbon (HC), nitrous-oxide (NOx). Estimated values and results of experiments were compared. The error analysis showed that the built model has quite accurately predicted the experimental results. This has been described by the value of Coefficient of determination (R2), which varies between 0.8493 and 0.9863 with the value of normalized mean square error (NMSE) between 0.0071 and 0.1182. The potency of the Nash-Sutcliffe coefficient of efficiency (NSCE) ranges from 0.821 to 0.8898 for BTE, HC, NOx and Smoke. This research has effectively emulated the on-board efficiency, emission, and combustion features of a dual-fuel biogas diesel engine taking the Swish activation mechanism in artificial neural network (ANN) model.

scholarly journals Prediction of Grinding Work Roll Demand in a Job Shop Company By using Artificial Neural Network and ARIMA Method

2018 ◽  
Vol 218 ◽  
pp. 04004
Author(s):  
Yusraini Muharni ◽  
Ade Irman ◽  
Muhammad Ilhamsyah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Taguchi Method ◽  
Job Shop ◽  
Industrial Area ◽  
Work Roll ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Good Plan

This study concern about forecasting grinding work roll demand in a job shop company located in industrial area in Cilegon. This factory main production is fabrication, which accepts various orders from other companies especially from the company around. Grinding Work Roll is one of those products that frequently request by customer. Although the order is frequent but the volume is fluctuation month by month. This situation drives the company to face the problem in preparing the resources required in fabrication process specially in scheduling the operators. To cope with this problem, we proposed to apply two robust forecasting methods, Artificial Neural Network and ARIMA to help in prediction the grinding work roll demand so as the company could make a good plan for the production process. The best architecture for ANN is obtained through applying Taguchi Method which applies Levenberg-Marquardt algorithm as Training Function. The best number for hidden layer is 10, while Momentum is 0.9. The Prediction result shows that ANN predicts better than ARIMA Method according to the lower Mean Square Error (MSE). MSE Value for ANN is 0.002 while for ARIMA MSE is 0.0043. From this study, we experienced that by applying Taguchi method could improve the performance of Artificial Neural Network.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

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