Response surface methodology and artificial neural network modelling for enhancing maturity parameters during vermicomposting of floral waste

Sharma, Dayanand; Pandey, Ashutosh Kumar; Yadav, Kunwar D.; Kumar, Sunil

doi:10.1016/j.biortech.2021.124672

Cited by 28 publications

(8 citation statements)

References 45 publications

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“…Here it was only observed in the EP5, which is initially 4.41 mg/g and, at the end of the process, 7.35 mg/g (Figure . 9). The increase in the concentration of nitrogen was observed because of decomposing of organic matter and also the contribution of nitrogen-fixing bacteria [1,13]. On the other hand, the 70:20:10 ratio of flower waste to cow dung manure to sawdust showed an increase in TKN from 2.75 mg/g to 3.34 mg/g.…”

Section: Total Kjeldahl Nitrogenmentioning

confidence: 96%

“…Flower waste is a significant source of phosphorous and also valuable for crop production. Phosphorous is a needed nutrient for plant growth and cell structure metabolism in plants [1]. An earthen pot filled with only flower waste showed an increase in phosphorous from 4.6 mg/g to 8.8 mg/g (Figure . 8).…”

Section: Phosphatementioning

confidence: 99%

“…For solid waste disposal, various methods are useful, like landfilling, incineration, composting, etc. In India, around 500 to 800 grams of solid waste is generated per capita daily, with 1,33,760 tons/day of average total solid waste [1,2,3,4].…”

Section: Introductionmentioning

confidence: 99%

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Biotransformation of Flower Waste: Effect of Bulking Agent and Microbes

Prajapati

Makwana

Nair³

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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In India, the flower or temple waste engendered from various sources also gets commingled with the municipal/urban solid waste or directly disposed of in the river. India is in the second position after China for flower waste generation worldwide. The high organic content of flower waste can be used to develop nutrient-rich compost that can further be used for the growth of plants. However, bulking agents (dry leaves/sawdust/coco-peat) and microbes (culture/manure) can affect composting. Thus, the prime objective of this study was to understand the effect of sawdust and cow dung manure on composting process. Two earthen pots were prepared with a 70%:10%:20% ratio of flower waste: cow dung manure: and sawdust, respectively. The other two pots with 70%:20%:10% ratio of flower waste: cow dung manure: and sawdust, respectively. An earthen pot with 100 % flower waste (control experiment) was also analysed throughout the study. The variation in temperature, moisture content, pH, conductivity, volatile content, ash, CO2 evolution, phosphate, and Total Kjeldahl Nitrogen (TKN) was monitored to achieve the objective of the whole study. The presence of sawdust was observed to be important by arresting leachate and maintaining aeration in the waste mixture, but cow dung manure also played a significant role in making more microbes and nutrients available for speeding up the process. The control experiment showed lump formation in the waste mixture due to excessive moisture development. Stable, mature, and nutrient-rich compost was obtained at the end of the composting process.

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Section: Total Kjeldahl Nitrogenmentioning

confidence: 96%

Section: Phosphatementioning

confidence: 99%

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Biotransformation of Flower Waste: Effect of Bulking Agent and Microbes

Prajapati

Makwana

Nair³

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Waste composting is widely used as a waste treatment method for organic matter. However, some problems in actual operation exist, such as maturity, heavy metals, and carbon dioxide emissions (Guo et al 2022 ; Li et al 2022 ; Sharma et al 2021 ; Yang et al 2023c ). Therefore, artificial intelligence-based solutions are developed.…”

Section: Illegal Dumping and Waste Disposalmentioning

confidence: 99%

“…Therefore, artificial intelligence-based solutions are developed. Sharma et al ( 2021 ) proposed using artificial neural network modeling to improve the maturity parameters of flower waste and cow manure in vermicomposting. After experiments, Sharma et al ( 2021 ) found that the fertilizer optimized by artificial neural network modeling has enough nutrients to benefit the growth of plants.…”

Section: Illegal Dumping and Waste Disposalmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

Fang

Chen

et al. 2023

Environ Chem Lett

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The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

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Effect of plant density and hydraulic retention time on phytoremediation of greywater using water hyacinth and validation of its optimized result using artificial neural network

Prasad

Sharma

Pandey³

et al. 2023

Can J Chem Eng

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Water scarcity is a global issue and finding alternative ways to meet our water needs within available resources is becoming increasingly important. Repurposing greywater for non‐potable uses, such as irrigation and car washing, can help alleviate the demand for drinking water. Greywater recycling and reuse are viable options to combat water scarcity. This study investigated the treatment of greywater using phytoremediation, specifically focusing on the effect of water hyacinth density and hydraulic retention time (HRT). An artificial neural network was used to optimize these parameters in the treatment system. The experiment spanned over 7 weeks and consisted of two phases. In phase I, different water hyacinth densities (ranging from 1.0 to 4.0 kg/m2) were tested, while phase II examined various HRTs (ranging from 12 to 48 h). The results indicated that the optimal conditions for greywater treatment were a water hyacinth density of 2 kg/m2 and an HRT of 48 h. Under these optimal conditions, the treatment system achieved high removal efficiencies for turbidity (98.02 ± 0.75%), chemical oxygen demand (59.42 ± 5.64%), ammonium‐nitrogen (87.45 ± 7.29%), and phosphate (94.50 ± 2.19%). However, the removal of total suspended solids was relatively low at 43.98 ± 9.20%. These findings were confirmed using an artificial neural network, showing a strong correlation (R > 0.99). The study concludes that phytoremediation using water hyacinth can be a viable option for greywater recycling and reuse, effectively addressing water scarcity. The recommended optimal conditions include a water hyacinth density of 2 kg/m2 and an HRT of 48 h.

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Response surface methodology and artificial neural network modelling for enhancing maturity parameters during vermicomposting of floral waste

Cited by 28 publications

References 45 publications

Biotransformation of Flower Waste: Effect of Bulking Agent and Microbes

Biotransformation of Flower Waste: Effect of Bulking Agent and Microbes

Artificial intelligence for waste management in smart cities: a review

Effect of plant density and hydraulic retention time on phytoremediation of greywater using water hyacinth and validation of its optimized result using artificial neural network

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