Analysis of vibrations in a diesel engine produced by Jatropha biodiesel using heterogeneous catalyst

2022 ◽  
pp. 0958305X2110639
Author(s):  
Aparna Singh ◽  
Akhilesh Kumar Choudhary ◽  
Shailendra Sinha ◽  
Hitesh Panchal ◽  
Kishor Kumar Sadasivuni
Keyword(s):  
Diesel Engine ◽  
Frequency Domain ◽  
Heterogeneous Catalyst ◽  
Root Mean Square ◽  
Compression Ratio ◽  
Optimization Technique ◽  
Molar Ratio ◽  
P Value ◽  
Mean Square ◽  
Input Parameters

Extensive consumption of fossil fuel has contributed to the worldwide decline of its reserves and detrimental effect on the environment. Therefore, it is essential to explore alternative option of fuel for diesel engine. The main objective of this research article is to optimize vibrations in a single-cylinder variable compression ratio diesel engine driven by Jatropha biodiesel blend. The heterogeneous catalyst (calcium oxide) is used to manufacture of biodiesel from Jatropha curcas oil by a process of transesterification. The optimization technique (Response Surface Methodology) has been employed to optimize root mean square acceleration of vibration by taking load, compression ratio (CR), and fuel injection pressure (FIP) as engine input parameters. Experiments were designed according to central composite design. The amplitude of the frequency domain signals is determined using Fast Fourier Transform and the influence of input parameters has been investigated in the frequency domain analysis of the vibration signatures. The adequacy and significance of the models have been checked by p-value and F value tests. Regression coefficients Adj. R2, R2, Pred. R2 were also found in acceptable range. The experimental outcome reveals that biodiesel yield of 81.6% was obtained at methanol-to-oil molar ratio of 12:1, reaction temperature of 65°C, reaction time of 3 h, and catalyst concentration of 5 wt%. Simultaneously, the model obtained a series of solutions based on the desirability criteria and proposed optimum setting of engine input parameters at a load of 2.59 kg, 17.94 CR, and 268.76 bar FIP for B30 blend. B30 blend generated root mean square acceleration of 4.46 m/s2 at above optimized conditions. A validation trial was conducted and the percentage of error for root mean square acceleration was found to be 2.3356% and 1.3039%, respectively, for B0 and B30 blend.

scholarly journals Validitas konstrak instrumen evaluasi outcome lembaga pendidikan guru vokasional

2016 ◽  
Vol 20 (2) ◽  
pp. 221-233 ◽  
Author(s):  
Nurhening Yuniarti ◽  
Soenarto Soenarto
Keyword(s):  
Teacher Education ◽  
Construct Validity ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Outcome Evaluation ◽  
P Value ◽  
Mean Square ◽  
Evaluation Instrument ◽  
Vocational Teacher ◽  
Fit Index

Penelitian ini bertujuan untuk mendapatkan bukti validitas konstrak dari instrumen yang digunakan untuk kegiatan evaluasi outcome dari lembaga pendidikan guru vokasional. Instrumen ini terdiri atas 3 jenis instrumen. Teknik analisis data yang digunakan adalah confirmatory factor analysis (CFA). Kriteria yang digunakan untuk melihat kecocokan model adalah: p-value, normed Chi-square (X2/df), RMSEA (Root Mean Square Error of Approximation), RMR (Root Mean-square Residual), GFI (Goodness-of-Fit Index), NFI (Normed Fit Index), Non-Normed Fit Index (NNFI), dan CFI (Comparative Fit Index). Bukti validitas instrumen dapat dilihat pada besarnya faktor loading dan t-value.  Jika loading factor lebih besar dari 0,3 dengan t-valuelebih besar dari 1,96 maka butir pernyataan dapat dikategorikan valid. Berdasarkan hasil analisis diperoleh hasil: (1) pada instrumen I terdapat 26 butir yang dinyatakan valid; (2) pada instrumen II terdapat 23 butir yang dinyatakan valid; dan (3) pada instrumen III terdapat 18 butir yang dinyatakan valid.Kata kunci: validitas konstrak, evaluasi, lembaga pendidikan guru vokasional CONSTRUCT VALIDITY OF OUTCOME EVALUATION INSTRUMENT IN VOCATIONAL TEACHER EDUCATION INSTITUTIONAbstractThis research aims to get the construct validity evidence of the outcome evaluation instrument of vocational teacher education institution. The instrument consists of 3 kinds of instruments. The analysis technique that is used to test the construct validity is confirmatory factor analysis (CFA). The criteria used to determine the goodness of fit were: p-value, normed Chi-square (X2/df), RMSEA (Root Mean Square Error of Approximation), RMR (Root Mean-square Residual), GFI (Goodness-of-Fit Index), NFI (Normed Fit Index), Non-Normed Fit Index (NNFI), and CFI (Comparative Fit Index). The evidence of the construct validity was based on the standardized loading factor and t-value. If the loading factor > 0.3 with t-value > 1.96, the item is valid. Based on the analysis, the research results are: (1) 26 item of the instrument I are categorized valid; (2) 23 items of instrument II are categorized valid; and (3) 18 items of instrument III are categorized valid.Keywords: construct validity, evaluation, vocational teacher education institution

scholarly journals A Feasible Approach to Detect Pesticides in Food Samples Using THz-FDS and Chemometrics

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qian Chen ◽  
Shengyao Jia ◽  
Jianyuan Qin ◽  
Yong Du ◽  
Zongshan Zhao
Keyword(s):  
Root Mean Square Error ◽  
Frequency Domain ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Density Functional ◽  
Food Samples ◽  
High Spectral Resolution ◽  
Quantitative Detection ◽  
Mean Square ◽  
Terahertz Frequency

The use of pesticides will have an impact on food, organisms, and environment. Specifically, pesticide residues in food will damage human health. Because of its high permeability, low energy, high spectral resolution, and fingerprint characteristics, terahertz frequency-domain spectroscopy has been introduced into the determination of pesticides (imidacloprid, acetamiprid, and triadimefon) residues in food samples (glutinous rice flour, wheat flour, and corn flour) in our present study. These three pesticides exhibit their own absorption peaks in the region of 0.4–1.7 THz. For understanding the origins of these peaks, the experimental data are interpreted by using density functional theory calculations at the level of B3LYP/6-31G (d). It is found that these absorption peaks come from the intramolecular and intermolecular interactions. The absorption peaks of pesticides are still detectable in a mixture of pesticides and food samples when they reach a certain concentration. The results from chemometrics analysis show that quantitative detection of pesticides in food samples is feasible. The partial least squares regression models have high correlation coefficient (>0.99), low root-mean-square error of calibration (<1.5%), low root-mean-square error of cross-validation (<2.4%), and low root-mean-square error of prediction (<2.3%), indicating good quality of prediction for pesticides concentration. Our results prove that the terahertz frequency-domain spectrum combined with chemometrics can be used for the detection of pesticides in food samples.

scholarly journals PENGEMBANGAN MODEL ADOPSI TEKNOLOGI E-VOTING

2015 ◽  
Vol 2 (2) ◽  
Author(s):  
Rahmad Abdilah ◽  
Budi Rahardjo ◽  
Rajesri Govindaraju
Keyword(s):  
Root Mean Square ◽  
Goodness Of Fit ◽  
Electronic Voting ◽  
P Value ◽  
Mean Square ◽  
Root Mean Square Residual ◽  
Fit Index ◽  
Meansquare Error ◽  
Voting Model

Electronic voting atau e-voting adalah suatumetode pengalihan pemilihan dari manual menjadilebih terkoordinir. Hal ini dilakukan dengankombinasi dari hardware dan software. Pemilih akanberpartisipasi di dalam e-voting dengan syaratmereka tidak akan ditipu. Pemilih ingin mengetahuidan percaya bahwa proses tersebut dapat di pantauoleh masyarakat yang memiliki kemampuan untukmemahami bagaimana sistem dapat bekerja.Model penelitian yang digunakan dalampenelitian ini dikembangkan berdasarkanpenelitian Tsuma dan analisis structural equationmodeling untuk menentukan bentuk model. Bentukmodel diuji dengan 19 pengujian yaitu Chi-squareatau p-value, GFI (Goodness of Fit Index), RMR(Root Mean Square Residual), RMSEA (Root MeanSquare Error of Approximation) ECVI (ExpectedCross-Validation Index), TLI/NNFI (Non-NormedFit Index), NFI (Normed Fit Index), PNFI(Parsimony Normed Fit Index), AGFI (AdjustedGoodness of Fit Index), RFI (Relative Fit Index),CFI (Comparative Fit Index), AIC, CAIC, CN(Critical N), IFI(Incremental Fit Index),PGFI(Parsimony Goodness of Fit Index), NCP(Non-centrality Parameter), CMIN/Df dan SRMR(Standardized RMR).Kata kunci : e-voting, model dan structuralequation modeling.

scholarly journals Characterization of Wavelet Decomposition Strain Signal Using the K-Mean Clustering Method

2018 ◽  
Vol 7 (3.17) ◽  
pp. 158
Author(s):  
A A. Rahim ◽  
C H. Chin ◽  
S Abdullah ◽  
S S. K. Singh ◽  
M Z. Nuawi ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Frequency Domain ◽  
Root Mean Square ◽  
Time Domain ◽  
Average Distance ◽  
Industrial Area ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Time Frequency ◽  
Strain Signal

This paper aims to study the characterisation of time-frequency domain to analyse the fatigue strain signal due to weaknesses in time domain and frequency domain approaches. The objectives were to determine the behaviour of strain signal, characterise the fatigue life of strain signal and validate the fatigue life in time-frequency domain. The strain signal was obtained using data acquisition devices and strain gauges on two types of road condition including highway and industrial area. The acquired signals were analysed with time domain, frequency domain and time-frequency domain approaches. In time-frequency domain, the signals were decomposed using 4th Daubechies discrete wavelet transform. To validate the effectiveness of time-frequency approach in characterising vibration fatigue signal, fatigue data was clustered by mapping of the data based on the spectrum energy, root-mean-square and fatigue life obtained. The clustering was performed by comparing the centroid values which both data had five clusters as the optimum data clustering with 0.836 average distance to centroid. From this, the relationship between fatigue life, root-mean-square and spectrum energy can be determined and thus a new fatigue life criterion was developed. 

scholarly journals Long-Term Global Solar Radiation Prediction in 25 Cities in Morocco Using the FFNN-BP Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Brahim Belmahdi ◽  
Mohamed Louzazni ◽  
Mohamed Akour ◽  
Daniel Tudor Cotfas ◽  
Petru Adrian Cotfas ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Mean Square ◽  
Solar Declination ◽  
Input Parameters ◽  
Statistical Metrics

This article presents different combinations of input parameters based on an intelligent technique, using neural networks to predict daily global solar radiation (GSR) for twenty-five Moroccan cities. The collected measured data are available for 365 days and 25 stations around Morocco. Different input parameters are used, such as clearness index KT, day number, the length of the day, minimal temperature Tmin, maximal temperature Tmax, average temperature Taverage, difference temperature ΔT, ratio temperature T-Ratio, average relative humidity RH, solar radiation at the top outside atmosphere TOA, average wind speed Ws, altitude, longitude, latitude, and solar declination. A different combination was employed to predict daily GSR for the considered locations in order to find the most adequate input parameter that can be used in the prediction procedure. Several statistical metrics are applied to evaluate the performance of the obtained results, such as coefficients of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), test statistic (TS), linear regression coefficients (the slope “a” and the constant “b”), and standard deviation (σ). It is found that the usage of input parameters gives highly accurate results in the artificial neural network (FFNN-BP) model, obtaining the lowest value of the statistical metrics. The results showed the best input of 25 locations, 12 inputs for Er-Rachidia, Marrakech, Medilt, Taza, Oujda, Nador, Tetouan, Tanger, Al-Auin, Dakhla, Settat, and Safi, seven inputs for Fes, Ifrane, Beni-Mellal, and Meknes, six inputs for Agadir and Rabat, five inputs for Sidi Ifni, Essaouira, Casablanca and Kenitra, four inputs for Ouarzazate, Lareche, and Al-Hoceima. In terms of accuracy, R2 of the selected best inputs parameters varies between 0.9860% and 0.9920%, the range value of MBE (%) being from −0.1076% to −0.5931%, the RMSE between 0.1990 and 0.4580%, the range value of the NRMSE between 0.0355 and 0.8938, and the lowest value MAPE between 0.0019 and 0.0060%. This technique could be used to predict other parameters for locations where measurement instrumentation is unavailable or costly to obtain.

scholarly journals Application of Weighted Fuzzy Time Series Model to Forecast Epidemic Injuries

2020 ◽  
pp. 79-90
Author(s):  
Hala Ahmed Abdul- Moneim
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Predictive Accuracy ◽  
Epidemiological Data ◽  
Fuzzy Time Series ◽  
P Value ◽  
Suspected Case ◽  
Mean Square

Aims: It is important to predict the amount of COVID-19 injuries. Since the first suspected case of novel coronavirus (2019-nCoV) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the large amounts of daily publicly available epidemiological data and the need to make an accurate prediction of future behavior requires the definition of powerful and effective techniques capable of inferring random dependency between the past and the future from observations. In this paper, we apply a rewarding model to predict injuries in areas where COVID-19 is, especially in the Arab region. This forecast uses epidemic injuries data from   March 2nd, 2020 to   July 20th, 2020 in Saudi Arabia. Methodology: We propose the use of weighted fuzzy time series techniques (WFTS) and weighted non-stationary fuzzy time series techniques (WNSFTS) to be compared with the classical Auto-Regressive Integrated Moving Average (ARIMA) statistical method. The available data is not a stationary and should therefore be converted first to stationary to forecast it with (ARIMA) and (WFTS) techniques. We do a log transform and differencing on our injuries dataset. Results: When we examine the original data by Dickey-Fuller Test (DFT) to get p-value, we find it is equal to 0.646, it is more than 0.05 which implies the non-stationarity. The mean square error (MSE), the root mean square error (RMSE) and normalization root mean square error (NRMSE), are applied to compare the accuracy of the methods. The results show that WFTS methods give good services for predicting epidemic injuries in the territory by COVID-19. Conclusion: The use of Weighted Non Stationary Fuzzy Time Series (WNSFTS) in forecasting epidemic injuries problem can provide significantly better results because it is able to predict the infected cases at the next time and achieve great predictive accuracy.

Mango (Magnifera indica) seed oil grown in Dilla town as potential raw material for biodiesel production using NaOH- a homogeneous catalyst

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Diesel Engine ◽  
Physicochemical Properties ◽  
Seed Oil ◽  
Methyl Esters ◽  
Pollution Prevention ◽  
Raw Material ◽  
Biodiesel Production ◽  
Molar Ratio ◽  
Homogeneous Catalyst ◽  
Minimal Amount

Biodiesel produced by transesterification process from vegetable oils or animal fats is viewed as a promising renewable energy source. Now a day’s diminishing of petroleum reserves in the ground and increasing environmental pollution prevention and regulations have made searching for renewable oxygenated energy sources from biomasses. Biodiesel is non-toxic, renewable, biodegradable, environmentally benign, energy efficient and diesel substituent fuel used in diesel engine which contributes minimal amount of global warming gases such as CO, CO2, SO2, NOX, unburned hydrocarbons, and particulate matters. The chemical composition of the biodiesel was examined by help of GC-MS and five fatty acid methyl esters such as methyl palmitate, methyl stearate, methyl oleate, methyl linoleate and methyl linoleneate were identified. The variables that affect the amount of biodiesel such as methanol/oil molar ratio, mass weight of catalyst and temperature were studied. In addition to this the physicochemical properties of the biodiesel such as (density, kinematic viscosity, iodine value high heating value, flash point, acidic value, saponification value, carbon residue, peroxide value and ester content) were determined and its corresponding values were 87 Kg/m3, 5.63 Mm2/s, 39.56 g I/100g oil, 42.22 MJ/Kg, 132oC, 0.12 mgKOH/g, 209.72 mgKOH/g, 0.04%wt, 12.63 meq/kg, and 92.67 wt% respectively. The results of the present study showed that all physicochemical properties lie within the ASTM and EN biodiesel standards. Therefore, mango seed oil methyl ester could be used as an alternative to diesel engine.

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