Evidence map›Paper›PMID 31254194›Full record

ArticleEnvironmental science and pollution research international2020

Study of biological and thermo-chemical pretreatment of organic fraction of municipal solid waste for enhanced biogas yield.

Renu Bala, Monoj Kumar Mondal

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In one paragraph

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. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Renu BalaDepartment of Chemical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, 221005, India.
Monoj Kumar MondalDepartment of Chemical Engineering and Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, 221005, India. mkmondal13@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biogas production from organic fraction of municipal solid waste (OFMSW) not only helps in solid waste management but also combat the food vs fuel dilemma. The presence of lignocellulosic material and other complex compounds in OFMSW hinder biogas production. Therefore, pretreatment is an essential step to increase the hydrolysis rate by converting complex compounds to simpler ones. This work was aimed at effective pretreatment of OFMSW by biological and thermo-chemical means. For biological pretreatment lignin degrading fungal strains, Phanerochaete chrysosporium and Pleurotus ostreatus were employed. Thermo-chemical treatment resulted in higher solubilisation yield in terms of sCOD and VFA making it a more effective method as compared with biological pretreatment. The optimisation of thermo-chemical pretreatment was done by the Box-Behnken design of response surface methodology (RSM). The interactive effect of influencing factors NaOH dose, temperature and time were studied on the response of sCOD, VFA and phenolic content. The sCOD and VFA values were significantly increased by increasing the NaOH concentration, temperature and time to a certain limit. The optimised condition from RSM for maximum solubilisation yield in terms of sCOD, VFA and phenolic content was found to be NaOH dose of 4.72 g/L, temperature 180 °C and time 30.3 min. Biogas production was increased by 169.5% after pretreatment at RSM optimised conditions as compared with untreated OFMSW.

Indexed as

Refuse DisposalAnaerobiosisBiofuelsBioreactorsFoodMethaneSolid WasteBiofuelsMethaneSolid WasteBox-Behnken designMunicipal solid wasteResponse surface methodologySoluble chemical oxygen demandVolatile fatty acid

Identifiers

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.