Evidence map›Paper›PMID 34370191›Full record

ArticleEnvironmental science and pollution research international2022

Qualitative and quantitative analysis of the influence of biodiesel fatty acid methyl esters on iodine value.

Yundi Huang, Fashe Li, Guirong Bao, Meng Li, Hua Wang

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.9field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 29 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Yundi HuangEngineering Research Center of Metallurgical Energy Conservation and Emission Reduction Ministry of Education, Kunming University of Science and Technology, Kunming, 650093, People's Republic of China.
Fashe LiEngineering Research Center of Metallurgical Energy Conservation and Emission Reduction Ministry of Education, Kunming University of Science and Technology, Kunming, 650093, People's Republic of China. lifashe@kust.edu.cn.
Guirong BaoEngineering Research Center of Metallurgical Energy Conservation and Emission Reduction Ministry of Education, Kunming University of Science and Technology, Kunming, 650093, People's Republic of China. baoguirong@kust.edu.cn.
Meng LiDepartment of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, 32816, USA.
Hua WangEngineering Research Center of Metallurgical Energy Conservation and Emission Reduction Ministry of Education, Kunming University of Science and Technology, Kunming, 650093, People's Republic of China.
Kunming University of Science and Technology · CNUniversity of Central Florida · US

Funding

national natural science foundation of china 51766007National Natural Science Foundation of China-Yunnan Joint Fund U1602272natural science foundation of yunnan province 2018FB092
6 · The paper itself

Abstract

Iodine value (IV) is an indicator to evaluate the degree of unsaturation (DU) of biodiesel. It reflects the biodiesel degradation and oxidation stability (OS) and also has an effect on viscosity, low-temperature flow properties (LTFP), and the combustion performance. To construct a theoretical system for the simultaneous optimization of LTFP and OS of biodiesel using IV, 52 measured experimental data are used to investigate the qualitative and quantitative relationship between IV and biodiesel composition. The relationships between biodiesel physicochemical properties and IV are investigated in this work. The qualitative analysis shows that the poly-unsaturated fatty acid methyl esters (FAMEs) contribute to an increase in IV, whereas saturated and mono-unsaturated FAMEs decrease IV. Multiple linear regression (MLR) and artificial neural network (ANN) are used to estimate IV from FAMEs. The correlation coefficient, root mean squared error (RMSE), and mean absolute percentage error (MAPE) are respectively 0.976, 2.45, and 1.76% for the MLR model and 0.983, 2.14, and 1.57% for the back propagation neural network (BPNN) model; these values indicate the high accuracy of these methods. The performances of the proposed models were compared with three existing IV prediction models and validated by another databank. The results indicate that the application of the developed BPNN model is better and more comprehensive. Additionally, a preliminary conclusion is that biodiesel with a low percentage of both long-chain saturated and poly-unsaturated FAMEs can have solidifying point (SP) and OS in the proper range. Biodiesel with a low IV is generally more combustible and efficient.

Indexed as

BiofuelsIodineEstersFatty AcidsOxidation-ReductionBiofuelsEstersFatty AcidsIodineArtificial neural networksCompositionDegree of unsaturationMultiple linear regressionPhysicochemical propertiesSimultaneous optimization

Identifiers

PMID34370191
OpenAlexW3187914895

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.