Evidence map›Paper›PMID 31487009›Full record

ArticleEnvironmental science and pollution research international2020

Forcasting of an ANN model for predicting behaviour of diesel engine energised by a combination of two low viscous biofuels.

Krishnamoorthy Ramalingam, Annamalai Kandasamy, Dhinesh Balasubramanian, Moulik Palani, Thiyagarajan Subramanian, Edwin Geo Varuvel, Karthikeyan Viswanathan

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Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Review
  2. Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NOJournal of geophysical research. Atmospheres : JGR · 2022
    Article
  3. Article
  4. Article
  5. Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction.Environmental science and pollution research international · 2021
    Article
  6. 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

7 authors.

Krishnamoorthy RamalingamDepartment of Mechanical Engineering, CK college of Engineering and Technology, Jayaram Nagar, Chellangkuppam, Cuddalore, 607003, India.
Annamalai KandasamyDepartment of Automobile Engineering, Madras Institute of Technology (MIT) Campus, Anna University, Chromepet, Chennai, Tamil Nadu, 600044, India.
Dhinesh BalasubramanianDepartment of Mechanical Engineering, Mepco Schlenk Engineering College, Mepco Nagar, Mepco Engineering College Post, Sivakasi, Virudhunagar, Tamil Nadu, 626005, India.
Moulik PalaniDepartment of Industrial Engineering, Texas A & M University, 400 Bizzell St, College Station, TX, 77843, USA.
Thiyagarajan SubramanianGreen Vehicle Technology Research Centre, Department of Automobile Engineering, SRM Institute of Science and Technology, Kattankulathur, India. prashanth.86.1@gmail.com.
Edwin Geo VaruvelGreen Vehicle Technology Research Centre, Department of Automobile Engineering, SRM Institute of Science and Technology, Kattankulathur, India.
Karthikeyan ViswanathanDepartment of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641 008, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study is focused on artificial neural network (ANN) modelling of non-modified diesel engine keyed up by the combination of two low viscous biofuels to forecast the parameters of emission and performance. The diesel engine is energised with five different test fuels of the combination of citronella and Cymbopogon flexuous biofuel (C50CF50) with diesel at precise blends of B20, B30, B40, B50 and B100 in which these numbers represent the contents of combination of biofuel and the investigation is carried out from zero to full load condition. The experimental result was found that the B20 blend had improved BTE at all load states compared with the remaining biofuel blends. At 100% load state, BTE (31.5%) and fuel consumption (13.01 g/kW-h) for the B20 blend was closer to diesel. However, the B50 blend had minimal HC (0.04 to 0.157 g/kW-h), CO (0.89 to 2.025 g/kW-h) and smoke (7.8 to 60.09%) emission than other test fuels at low and high load states. The CO

Indexed as

BiofuelsGasolineNeural Networks, ComputerSmokeVehicle EmissionsBiofuelsGasolineSmokeVehicle EmissionsArtificial neural networkBio-fuelCitronella oilDiesel engineEmissionSimulation

Identifiers

PMID31487009

What OpenQuestion holds

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Registered trials

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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.