ArticleScientific reports2025
Artificial neural network based optimization of engine performance using domestic biomass as a clean fuel.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
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Who cites it
1 citing paper in PubMed.
- Machine learning-driven optimization of performance and emissions in a butanol/diesel CI engine.Scientific reports · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing demand for sustainable energy has encouraged the development of innovative approaches combining renewable biomass with advanced technologies. This work investigates poultry waste sourced from chicken shops as a potential raw material for oil generation. The optimal conditions, which resulted in the highest biodiesel yield and lowest Environmental Factor (E-factor), were achieved consuming a 9:1 methanol-to-chicken-oil ratio, 1% catalyst by weight, a 1-h reaction time, and a mixing speed of 500 rpm. Additionally, various nanofluid formulations containing Zinc sulfide (ZnS) nanopowder were prepared at concentrations ranging from 50 to 150 ppm, in increments of 50 ppm, and blended with a C20D80 fuel mixture. Among the tested blends, the C20D80 + 100ppm formulation demonstrated superior performance, reducing HC emissions by 29.23%, CO by 37%, and NOx by 5.7% while maintaining comparable engine performance to standard diesel. The experimental findings were substantiated through AI-based neural network, achieving a high R
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Registered trials
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.