Optimization of the factors affecting performance and emissions in a diesel engine using biodiesel and EGR with Taguchi method

Ayhan, Vezir; Çangal, Çiçek; Cesur, İdris; Çoban, Aslan; Ergen, Gökhen; Çay, Yusuf; Kolip, Ahmet; Özsert, İbrahim

doi:10.1016/j.fuel.2019.116371

Cited by 61 publications

(21 citation statements)

References 31 publications

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“…This likely occurs because not all interactions are reflected when predicting values using the orthogonal array experiment design. For this reason, while orthogonal array experimental design techniques are used in many fields [50][51][52], their experimental predictions are not well utilized. In a study by Chen [53], an optimal process was designed using an orthogonal array table, but a loss function value was used instead of an experimental value.…”

Section: Experimental Evaluation Of the Model Accuracymentioning

confidence: 99%

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

Wie

Lee

et al. 2020

Materials

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The purpose of this study is to experimentally design the drying, calcination, and sintering processes of artificial lightweight aggregates through the orthogonal array, to expand the data using the results, and to model the manufacturing process of lightweight aggregates through machine-learning techniques. The experimental design of the process consisted of L18(3661), which means that 36 × 61 data can be obtained in 18 experiments using an orthogonal array design. After the experiment, the data were expanded to 486 instances and trained by several machine-learning techniques such as linear regression, random forest, and support vector regression (SVR). We evaluated the predictive performance of machine-learning models by comparing predicted and actual values. As a result, the SVR showed the best performance for predicting measured values. This model also worked well for predictions of untested cases.

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Section: Experimental Evaluation Of the Model Accuracymentioning

confidence: 99%

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

Wie

Lee

et al. 2020

Materials

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“…This result is in line with many other research works carried with the effect of varying compression ratio. [45][46][47] Smoke opacity. Figure 10 shows the smoke opacity of the engine when fueled with D70H30 blend at various compression ratio viz., 16:1, 17.5:1 and 19:1 and under EGR rates of 0%, 10%, and 20% respectively at bmep ¼ 4.16 bar and speed ¼ 1500 rpm.…”

Section: Performance Analysismentioning

confidence: 99%

Emission profiling of a common rail direct injection diesel engine fueled with hydrocarbon fuel extracted from waste high density polyethylene as a partial replacement for diesel with some modifications

Duraisamy

Ismailgani

Paramasivam

et al. 2020

Energy & Environment

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A hydrocarbon fuel extracted from waste high-density polyethylene (WHDPE) by catalytic pyrolysis in a batch scale reactor is blended with diesel by 30% vol. (called as D70H30) is tested in a variable compression ratio engine equipped with a common rail system. Experiments were conducted at three compression ratios (16:1, 17.5:1, and 19:1) and exhaust gas re-circulation (EGR) rates (0%, 10%, and 20%) at the engine’s rated power to evaluate its combustion, performance and emission characteristics. The results revealed that, increasing the compression ratio resulted in higher peak cylinder pressure (PCP) and heat release rates (HRR). Introduction of EGR diminished both PCP and HRR peaks. The brake thermal efficiency of D70H30 blend was 4% lower than diesel at same operating conditions which got better at higher compression ratio without EGR. NOx emission was highest when injected at compression ratio 19:1 and at 0% EGR rate which was 6% and 3% higher than diesel and D70H30 blend operated at engine stock settings. In comparison with baseline diesel smoke opacity remained lower at all operating conditions, where lowest smoke emission was recorded at CR19 and at 0% EGR rate. UHC and CO emission followed the similar trend of smoke opacity. Whereas CO2 emission increased with compression ratio and reduced with induction of EGR. It can be concluded from the study that at higher compression ratio and low EGR rates D70H30 blend can be effectively utilized in a CRDi engine.

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“…Some studies reported that the viscosity and volatility of biodiesel can be improved by adding oxygenated additives. (Ayhan et al, 2020) Use of these methods improves the thermal efficiency. The major disadvantage of biodiesel is its higher NOx emission.…”

Section: Introductionmentioning

confidence: 99%

Impact of alumina and cerium oxide nanoparticles on tailpipe emissions of waste cooking oil biodiesel fuelled CI engine

Dinesha

Mohan

2021

Cogent Engineering

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Biofuels are one of the most sought-after renewable energy sources to overcome the current energy crisis in the world. Diesel blended with esters derived from oils has shown appreciable results for its use in compression ignition engines. In this work, B20 (20% biodiesel + 80% diesel by volume) fuel blend comprising 80% diesel and 20% biofuel extracted from waste cooking oil is used as a fuel. Two different metallic oxide nanoparticles (NPs), namely, cerium oxide (CeO 2 ) and aluminium oxide (Al 2 O 3 ) each having size of 30 nm, are dispersed in B20 biodiesel blend and used to investigate their combined influence on engine combustion. Experiments are conducted to study the engine exhaust emissions of a single cylinder four-stroke diesel engine running at constant speed of 1,500 rpm with varying engine loads. Experiments are carried out with B20 fuel and adding mixtures of CeO 2 and Al 2 O 3 nanoparticles in 50-30, 40-40 and 30-50 ppm, respectively, to B20 biodiesel blend. Responses recorded are the emissions of CO, HC, CO 2 , NOx and smoke. The results reveal that, on adding the nanoparticles, the engine observed a smooth combustion with reduction in the exhaust emissions compared to neat B20 biodiesel operation. B20 blend with 50 ppm of CeO 2 and 30 ppm of Al 2 O 3 nanoparticles showed lower exhaust emissions compared to other fuel blends. For the best fuel blend, CO, HC, NOx and smoke are reduced by 57.3%, 22.3%, 24.3% and 7.36%, respectively, compared to neat B20 operation.

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Optimization of the factors affecting performance and emissions in a diesel engine using biodiesel and EGR with Taguchi method

Cited by 61 publications

References 31 publications

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

Emission profiling of a common rail direct injection diesel engine fueled with hydrocarbon fuel extracted from waste high density polyethylene as a partial replacement for diesel with some modifications

Impact of alumina and cerium oxide nanoparticles on tailpipe emissions of waste cooking oil biodiesel fuelled CI engine

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