Machine learning-driven optimization of performance and emissions in a butanol/diesel CI engine.

K Siva Prasad, T R Vijaybabu, G Kiran Kumar, S Srinivasa Rao, V R K Raju, Sunnam Nagaraju, A Bhowmik, Ahmed Jemal

Abstract read

In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

K Siva PrasadDepartment of Mechanical Engineering, GMR Institute of Technology (GMRIT)- Deemed to be University, Rajam, Andhra Pradesh, India. ksivaprasad286@gmail.com.

T R VijaybabuDepartment of Mechanical Engineering, GMR Institute of Technology (GMRIT)- Deemed to be University, Rajam, Andhra Pradesh, India. vijaybabu.tr@gmrit.edu.in.

G Kiran KumarDepartment of Mechanical Engineering, Aditya University, Surampalem, Andhra Pradesh, India.

S Srinivasa RaoDepartment of Mechanical Engineering, National Institute of Technology Warangal, Warangal, Telangana, India.

V R K RajuDepartment of Mechanical Engineering, National Institute of Technology Warangal, Warangal, Telangana, India.

Sunnam NagarajuDepartment of Mechanical Engineering, MLR Institute of Technology, Hyderabad, India.

A BhowmikDepartment of Additive Manufacturing, Mechanical Engineering, SIMATS, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, 602105, India.

Ahmed JemalFaculty of Mechanical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia. ahmed.jemal@ju.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A hybrid modeling framework integrating experiments, CONVERGE CFD, and Optuna-optimized ensemble machine learning is developed to predict and optimize performance and emission characteristics of a DI-CI engine fueled with butanol/diesel blends. Machine learning was employed to model and predict key combustion and emission characteristics under varying engine operating conditions. This work presents a numerical and modeling examination of a DI-CI engine's performance and emission characteristics. CONVERGE CFD software was used to do the numerical analysis. Four distinct engine parameters were examined in this study: Start of Injection (SOI) (17 to 29° CA bTDC), Compression Ratio (CR) (14.5 to 19.5), Exhaust Gas Recirculation (EGR) (0 to 30%), and Fuel Injection Pressure (FIP) (200 to 280 bar). The Optuna-optimized ensemble Meta Model achieved the most accurate predictions, exhibiting the lowest RMSE and highest R² values for ISFC and NOₓ, along with reasonable accuracy for Soot estimation. The ensemble Meta Model achieved the best performance with R² values of 0.98 (ISFC) and 0.97 (NOx) and RMSE below 5%, demonstrating strong generalization.

Indexed as

CI engine optimizationCONVERGE CFDEnsemble learningHybrid modelingMachine learning

Identifiers

PMID42177365

DOI10.1038/s41598-026-54687-7

PMCPMC13424130

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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.