An Artificial Neural Network and Genetic Algorithm Optimized Model for Biogas Production from Co-digestion of Seed Cake of Karanja and Cattle Dung

Barik, Debabrata; Murugan, S.

doi:10.1007/s12649-015-9392-1

Cited by 54 publications

(11 citation statements)

References 41 publications

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“…One direct effect of grazing is through production of dung and urine McNaughton et al, 1997). Our study showed that the relative abundance of Bacteroidia, an obligate anaerobic gut microbe in animals (Eckburg, 2005;Winter & Bäumler, 2014), was higher in WG, likely due to accumulation of dung and urine by intensive grazing in WG (Barik & Murugan, 2015). Accumulation of dung and urine may also decrease the C/N of substrates, facilitating decomposition but reducing soil C accumulation (Bagchi, Roy, Maitra, & Sran, 2017;Fontaine et al, 2011).…”

Section: Direct Impacts Of Grazing On Microbes Through Altering Soimentioning

confidence: 71%

Grazing practices affect the soil microbial community composition in a Tibetan alpine meadow

et al. 2018

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Grazing is the primary land‐use activity on the Tibetan Plateau and can affect soil microbes and their function through aboveground vegetation removal, animal trampling, and manure deposition. Two distinct grazing systems (i.e., winter grazing [WG] and annual grazing [AG]) dominate on the Tibetan Plateau, but their effects on soil microbes have rarely been assessed. Taking advantage of a 5‐year field experiment that controlled timing and density of grazers via fence exclosures, we examined impacts of different grazing practices on the biomass, diversity, and composition of the soil microbial community in a Tibetan alpine meadow. On the basis of high‐throughput sequencing, we found that grazing had no significant effects on bacterial and fungal α‐diversities but altered their community compositions. Although total soil carbon (TC), total soil nitrogen (TN), and carbon/nitrogen (C/N) were related to both bacterial and fungal community compositions, plant shoot biomass only correlated with bacteria, and soil pH and moisture significantly influenced fungi under grazing. Also, grazing altered plant community composition but did not lead to corresponding changes in bacterial or fungal community composition. Moreover, grazing practices affected the relative abundance of specific bacterial and fungal taxa, reducing Actinobacteria but increasing Basidiomycete fungi in WG. Soil TC and TN were higher, and the soil microbial community was more stable in AG than WG, likely due to more stable litter inputs in AG. Together, these results showed that AG was less disruptive to soil microbes, suggesting that AG may provide a viable option for sustainable utilization and conservation of these fragile alpine systems.

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Section: Direct Impacts Of Grazing On Microbes Through Altering Soimentioning

confidence: 71%

Grazing practices affect the soil microbial community composition in a Tibetan alpine meadow

et al. 2018

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“…Correspondingly, the biogas from the sample had a heating value of 27.5 MJ/kg, with an energy content of 6–6.5 kW/m 3 . Barik and Murugan (2015) further studied modeling of the process for prediction and optimisation of biogas production using artificial neural network (ANN) and the genetic algorithm (GA) [ 93 ]. The GA optimised results based upon ANN developed model for pH, digestion time and the C/N ratio of the samples were correlated with the experimental results.…”

Section: Biogas Productions From Non-edible Oil Cakesmentioning

confidence: 99%

A critical review on biogas production from edible and non-edible oil cakes

Mohanty

Rout

Dubey

et al. 2021

Biomass Conv. Bioref.

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The circular economy is at the core of sustainable development. The generation of biogas from the massive quantity of agricultural waste biomass is one of the critical drivers of the circular economy. Biogas has enormous renewable energy potential and has multitudes of applications in today’s energy-intensive society. Oil cakes, a known Agri-waste, are the by-product of oil processing, and are rich in nutrients. The edible oil cakes mostly have been used as a cattle feed; however, non-edible oil cakes do not find many applications. Their production is continuously escalating as non-edible oils are increasingly used in biodiesel production. Recently, there is a lot of emphasis on biogas production from these oil cakes. This paper reviews in detail biogas production from both edible and non-edible oil cakes. Chemical composition and various other applications of the cakes are also reviewed in brief. The survey illustrates that multiple parameters such as inoculum sources, co-digestion and reactor design affect the biogas production. All those factors, along with biogas upgrading and the economy of the process, are reviewed. Finally, future research opportunities are suggested to improve the viability of the biogas production from oil cakes.

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“…Dibaba et al [87] determined that the best performance of Upflow Anaerobic Contactor (UAC) with 87% COD removal, and hydraulic retention time of 16.67 days where an increase of 7.4% in biogas production was realized. Barik and Murugan [88] used ANN and GA to estimate and optimize the yield of biogas from cattle dung and seed cake of Karanja in co-digestion. The product quality using co-digestion of cake of Karanja and cattle dung mixture was higher than when using cattle dung samples for a mixing ratio of 1 cake of Karanja to 3 cattle dung.…”

Section: Optimization Of Quality and Yieldmentioning

confidence: 99%

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

et al. 2021

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Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

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An Artificial Neural Network and Genetic Algorithm Optimized Model for Biogas Production from Co-digestion of Seed Cake of Karanja and Cattle Dung

Cited by 54 publications

References 41 publications

Grazing practices affect the soil microbial community composition in a Tibetan alpine meadow

Grazing practices affect the soil microbial community composition in a Tibetan alpine meadow

A critical review on biogas production from edible and non-edible oil cakes

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

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