Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine

Energy ◽  
2021 ◽  
pp. 122389
Author(s):  
Suleyman Simsek ◽  
Samet Uslu ◽  
Hatice Simsek
Energy ◽  
2020 ◽  
Vol 213 ◽  
pp. 119072
Author(s):  
A. Naresh Kumar ◽  
P.S. Kishore ◽  
K. Brahma Raju ◽  
B. Ashok ◽  
R. Vignesh ◽  
...  

2020 ◽  
Author(s):  
Senthilkumar S. ◽  
Ganesan S. ◽  
Krishana Prasad D. ◽  
Krishana Kumar K. ◽  
Kannan M.
Keyword(s):  

In this work, central composite design(CCD) and desirability approach of Response surface methodology (RSM) has been used for optimization of biodiesel yield produced from mixture of animal waste fat oil and used cooking oil (AWO) in the ratio of 1:1through alkaline transesterification process. In this work, methanol quantity, reaction time and sodium hydroxide concentration are selected as input parameters and yield selected as response. The combined effect of methanol quantity, reaction time and sodium hydroxide concentration were investigated and optimized by using RSM. The second order model is generated to predict yield as a function of methanol quantity, reaction time and sodium hydroxide concentration. A statistical model predicted the maximum yield of 96.9779% at 35ml methanol quantity (% v/v of oil), 75 min. reaction time and 0.6g (% wt./v of oil) of sodium hydroxide. Experimentally, the maximum yield of 97% was obtained at the above optimized input parameters. The variation of 0.02% was observed between experimental and predicted values. In this work, an attempt has also made to use desirability approach of RSM to optimize the input parameters to predict maximum yield. Desirability approach predicts maximum yield (97.075%) at CH3OH (35.832% vol. /vol. of oil), NaOH (0.604 % wt./vol. of oil) and reaction time (79.054min.) was found for the AWO.


2020 ◽  
Vol 3 (3) ◽  
pp. 138-146
Author(s):  
Camilla Matos Pedreira ◽  
José Alves Barros Filho ◽  
Carolina Pereira ◽  
Thamine Lessa Andrade ◽  
Ricardo Mingarini Terra ◽  
...  

Objectives: This study aims to evaluate the impact of using three predictive models of lung nodule malignancy in a population of patients at high-risk for neoplasia according to previous analysis by physicians, as well as evaluate the clinical and radiological malignancy-predictors of the images. Material and Methods: This is a retrospective cohort study, with 135 patients, undergone surgical in the period from 01/07/2013 to 10/05/2016. The study included nodules with dimensions between 5mm and 30mm, excluding multiple nodules, alveolar consolidation, pleural effusion, and lymph node enlargement. The main variables analyzed were age, sex, smoking history, extrathoracic cancer, diameter, location, and presence of spiculation. The calculation of the area under the ROC curve assessed the accuracy of each prediction model. Results: The study analyzed 135 individuals, of which 96 (71.1%) had malignant nodules. The areas under the ROC curves for each prediction model were: Swensen 0.657; Brock 0.662; and Herder 0.633. The models Swensen, Brock, and Herder presented positive predictive values in high-risk patients, corresponding to 83.3%, 81.8%, and 82.9%, respectively. Patients with the intermediate and low-risk presented a high malignant nodule rate, ranging from 69.3-72.5% and 42.8-52.6%, respectively. Conclusion: None of the three quantitative models analyzed in this study was considered satisfactory (AUC> 0.7) and should be used with caution after specialized evaluation to avoid underestimation of the risk of neoplasia. The pretest calculations might not contemplate other factors than those predicted in the regressions, that could present a role in the clinical decision of resection.


2009 ◽  
Vol 55 (No. 1) ◽  
pp. 24-28 ◽  
Author(s):  
A. Prošková ◽  
Z. Kopicová ◽  
J. Kučera ◽  
L. Škarková

Rendering plant fat (RPF) was collected and different conditions were used for transesterification. The course of transesterification of RPF was compared with that of transesterification of lard under the same conditions. Significant diffe-rences were found between transesterification of RPF and that of lard. Optimum methanol excess for lard transesterification was found to be 30-fold, for RPF 10-fold, optimum sulfuric acid concentration was 1% for lard, 2.5% for RPF. Optimum temperature as well as optimum reaction time were similar in both cases. The fatty acid composition is similar but not identical in both fats. RPF contains a higher amount of free acid which could be the reason for the differences observed.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nur Azreen Zulkefly ◽  
Norjihan Abdul Ghani ◽  
Christie Pei-Yee Chin ◽  
Suraya Hamid ◽  
Nor Aniza Abdullah

PurposePredicting the impact of social entrepreneurship is crucial as it can help social entrepreneurs to determine the achievement of their social mission and performance. However, there is a lack of existing social entrepreneurship models to predict social enterprises' social impacts. This paper aims to propose the social impact prediction model for social entrepreneurs using a data analytic approach.Design/methodology/approachThis study implemented an experimental method using three different algorithms: naive Bayes, k-nearest neighbor and J48 decision tree algorithms to develop and test the social impact prediction model.FindingsThe accurate result of the developed social impact prediction model is based on the list of identified social impact prediction variables that have been evaluated by social entrepreneurship experts. Based on the three algorithms' implementation of the model, the results showed that naive Bayes is the best performance classifier for social impact prediction accuracy.Research limitations/implicationsAlthough there are three categories of social entrepreneurship impact, this research only focuses on social impact. There will be a bright future of social entrepreneurship if the research can focus on all three social entrepreneurship categories. Future research in this area could look beyond these three categories of social entrepreneurship, so the prediction of social impact will be broader. The prospective researcher also can look beyond the difference and similarities of economic, social impacts and environmental impacts and study the overall perspective on those impacts.Originality/valueThis paper fulfills the need for the Malaysian social entrepreneurship blueprint to design the social impact in social entrepreneurship. There are none of the prediction models that can be used in predicting social impact in Malaysia. This study also contributes to social entrepreneur researchers, as the new social impact prediction variables found can be used in predicting social impact in social entrepreneurship in the future, which may lead to the significance of the prediction performance.


2013 ◽  
Vol 427-429 ◽  
pp. 124-128
Author(s):  
Qu Li Ma ◽  
Jian Jun Yang ◽  
Yi Liu

In order to deal with the small sample problem in practical reliability analysis of the diesel engine cylinder liner, the method and the steps of using support vector machine to build a regression prediction model to eventually develop the competing failure model of diesel engine cylinder liner are proposed. As proposed, more samples are generated by use of the regression prediction model and the quasi maximum likelihood method is used to separately estimate the occurring patterns of two failure models, including abrasion deterioration and pyrolysis. The scientificity and effectiveness of this method was demonstrated through calculation of examples and comparison of results.


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