ArticleACS omega2025
Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Energy plays a key role in the socioeconomic development of society, and most of its global demand is provided by conventional resources (e.g., fossil fuels). Utilizing renewable energy is significantly growing since it can meet global energy demand while minimizing the adverse impacts of carbon emissions on climate change. Biomass is an appealing option among the emerging alternatives (e.g., wind and solar). Torrefaction is a mild pyrolysis process, and this research aims to analyze the torrefaction process of lignocellulosic biomass. The methodology proposed involves employing hybrid models of artificial neural network-particle swarm optimization (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and coupled simulated annealing-least-squares support vector machine (CSA-LSSVM). In addition to the machine learning algorithms, a correlation is developed using gene expression programming (GEP) to interrelate the biomass properties, including moisture content, volatile matter, fixed carbon, ash, sample size, and the contents of oxygen, carbon, hydrogen, and nitrogen along with the process operating condition encompassing residence time, temperature, and the concentration of CO
Identifiers
What OpenQuestion holds
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.