The Performance of a Diesel Engine Fueled with Diesel Oil, Biodiesel and Preheated Coconut Oil

Fossil fuel crisis and depletion, environmental pollution and ever-increase in vehicle and transportation means have renewed the scientist\u27s interest in the world in order to look for potential alternative fuels, which are attractive such as biodiesel, bioethanol, DME and vegetable oils. Inedible vegetable oils such as coconut oil, Jatropha oil, linseed oil or animal fat are full of potential for using directly or manufacturing biodiesel. This work is carried out in order to study the four stroke diesel engine D240 performance characteristics fueled with preheated pure coconut oil (PCO), Jatropha oil methyl ester (JOME) and compare with diesel oil (DO). The test diesel engine performance such as power (Ne), torque (Me), specific fuel consumption (ge) and thermal efficiency (ηe) is determined, calculated and evaluated while using JOME, preheated PCO and compared to DO. The results show that, power (Ne), torque (Me) and thermal efficiency (ηe) while engine is fueled with JOME and PCO are lower, otherwise specific fuel consumption (ge) is higher than those of diesel fuel, the test engine performance are gained the best for JOME and PCO100. Keywords: biofuel, biodiesel, preheated vegetable oils, engine performance, efficiency, specific fuel consumption.Article History: Received Dec 9, 2016; Received in revised form January 28, 2017; Accepted February 4, 2017; Available onlineHow to Cite This Article: Hoang, T.A and Le,V. V. (2017). The Performance of A Diesel Engine Fueled With Diesel Oil, Biodiesel and Preheated Coconut Oil. International Journal of Renewable Energy Development, 6(1), 1-7.http://dx.doi.org/10.14710/ijred.6.1.1-

Supplementary figure.2

Supplementary figure.2 Identified lipid metabolites in the negative (left panel) and positive (right panel) ion modes, respectively.</p

Supplementary figure.1

Supplementary figure.1 PCA plot of PC1 versus PC3 and PC2 versus PC3 based on the expression levels of single-copy orthologous PCGs among 15 vertebrate species.</p

Supplementary table 2.

Supplementary table 2. Detailed information on single-copy orthologous PCGs across 15 vertebrate species.</p

Effects of different nitrogen, phosphorus, and potassium fertilization treatments on strawberry phenotypes from 30–120 d after planting.

Effects of different nitrogen, phosphorus, and potassium fertilization treatments on strawberry phenotypes from 30–120 d after planting.</p

Fig 4 -

(a, b) Chao1 index showing bacterial (a) and fungal (b) community structure in the rhizosphere soil of strawberry plants treated with different levels of fertilizer. The horizontal bars within boxes represent medians. (c) Principal coordinate analysis (PCoA) based on the weighted Bray-Curtis distance of the bacterial communities. Permutational multivariate analysis of variance (PERMANOVA) was used to detect statistically significant differences between groups. (d) Heatmap based on the weighted Bray-Curtis distance of the fungal communities. (e) Significant differences in bacterial taxa between the fertilizer treatment groups as identified with linear discriminant analysis (LDA) coupled with effect size (LEfSe) analysis (LDA > 4 and p (f) Significant differences in fungal taxa between the fertilizer treatment groups as identified with LEfse analysis (LDA > 4 and p < 0.05).</p

Bacteria sample information.

Nitrogen (N), phosphorus (P), and potassium (K) exert various effects on strawberry (Fragaria ananassa Duchesne) yields. In this study, we employed an orthogonal experimental design (T1-T9) with three fertilization treatments (N, P, and K) at three levels to identify an optimal fertilization scheme for strawberry cultivation. The effects of fertilizer combinations the rhizosphere soil microbial community were also explored by using bacterial full-length 16S rRNA and fungal ITS (internal transcribed spacer) sequencing (30 samples for each analysis). The results showed that the average plant height and leaf area of the fertilized groups were 24.6% and 41.6% higher than those of the non-fertilized group (T0). After 60 d of planting, the sucrase activity in the T6 group increased by 76.67% compared to the T0 group, with phosphate fertilizer exerting a more significant impact on sucrase activity. The T6 treatment group had the highest alpha diversity index among bacterial and fungal microorganisms, and had a different microbial community structure compared with the control group. The most abundant bacterial taxa in the strawberry rhizosphere soil were Proteobacteria, Bacteroidota, and Acidobacteriota, and the most abundant fungal phyla were Monoblepharomycota, Glomeromycota, and Mucoromycota. Application of the optimal combined fertilizer treatment (T6) significantly increased the abundance of Proteobacteria and altered the abundance of Gemmatimonas compared to other treatment groups. Notably, Gemmatimonas abundance positively correlated with strawberry plant height and soil N, P, and K levels. These findings indicated that the relative abundance of beneficial bacteria could be enhanced by the application of an optimal fertilizer ratio, ultimately improving strawberry agronomic traits.</div

Fig 3 -

(a) Nitrogen content in the rhizosphere soil after fertilizer treatment. AN, total nitrogen; TN, available nitrogen. (b) Phosphorus content in the rhizosphere soil after fertilizer treatment. AP, total phosphorus; TP, available phosphorus. (c) Potassium content in the rhizosphere soil after fertilizer treatment. AK, total potassium; TK, available potassium. (d) Correlation matrix for strawberry phenotypes and soil composition characteristics. Blue and red dots represent negative and positive correlations, respectively. *p p p < 0.001.</p

Phenotypic correlation data.

Nitrogen (N), phosphorus (P), and potassium (K) exert various effects on strawberry (Fragaria ananassa Duchesne) yields. In this study, we employed an orthogonal experimental design (T1-T9) with three fertilization treatments (N, P, and K) at three levels to identify an optimal fertilization scheme for strawberry cultivation. The effects of fertilizer combinations the rhizosphere soil microbial community were also explored by using bacterial full-length 16S rRNA and fungal ITS (internal transcribed spacer) sequencing (30 samples for each analysis). The results showed that the average plant height and leaf area of the fertilized groups were 24.6% and 41.6% higher than those of the non-fertilized group (T0). After 60 d of planting, the sucrase activity in the T6 group increased by 76.67% compared to the T0 group, with phosphate fertilizer exerting a more significant impact on sucrase activity. The T6 treatment group had the highest alpha diversity index among bacterial and fungal microorganisms, and had a different microbial community structure compared with the control group. The most abundant bacterial taxa in the strawberry rhizosphere soil were Proteobacteria, Bacteroidota, and Acidobacteriota, and the most abundant fungal phyla were Monoblepharomycota, Glomeromycota, and Mucoromycota. Application of the optimal combined fertilizer treatment (T6) significantly increased the abundance of Proteobacteria and altered the abundance of Gemmatimonas compared to other treatment groups. Notably, Gemmatimonas abundance positively correlated with strawberry plant height and soil N, P, and K levels. These findings indicated that the relative abundance of beneficial bacteria could be enhanced by the application of an optimal fertilizer ratio, ultimately improving strawberry agronomic traits.</div

S1 Fig -

S1-S2: (1) Microbial composition is presented at phylum and genus levels; (S2): Significant difference analysis was conducted on the bacterial. (PDF)</p