Optimization of hydrothermal gasification process through machine learning approach: Experimental conditions, product yield and pollution

Gopirajan, P. V.; Gopinath, Kannappan Panchamoorthy; Sivaranjani, G.; Arun, Jayaseelan

doi:10.1016/j.jclepro.2021.127302

Cited by 43 publications

(6 citation statements)

References 39 publications

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“…The created model yielded encouraging results when selecting and screening catalysts to maximize hydrogen production and decrease carbon dioxide production during hydrothermal gasification of discarded biomass. Comparable research used machine learning and a technology decision support system to optimize heterogeneous catalyst input during hydrothermal gasification (Gopirajan et al 2021 ). The model demonstrated that catalyst addition had a favorable impact on hydrogen generation.…”

Section: Chemical Analysis Of Waste Using Artificial Intelligencementioning

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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Section: Chemical Analysis Of Waste Using Artificial Intelligencementioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

Fang

Chen

et al. 2023

Environ Chem Lett

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“…Other studies collect data from several works in the literature and usually focus on the effect of the process operating parameters on the gasication outputs. 34,[39][40][41][42][43][44] We focus on the detailed study of the effect of biomass properties on the gasication process performance, which is still unclear in the literature. From a machine-learning perspective, this is an interesting question as we have to deal with a relatively small dataset.…”

Section: Introductionmentioning

confidence: 99%

Biomass to energy: a machine learning model for optimum gasification pathways

et al. 2023

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“…It's the most prescribed stage in light of its market propagation, simplicity of improvement, reconciliation, and limited ability to focus on advancement. Machine learning algorithms will identify the geochemical properties (Gopirajan et al, 2021a(Gopirajan et al, , 2021bShu & Ye, 2022;Balaji et al, 2021;Vesselinov et al, 2018) Machine learning models learn from the dataset and act according to its training (Dey, 2016;George & Hautier, 2020;Jordan & Mitchell, 2015;Ren et al, 2020). It is evident from recent studies that machine learning models are predicting better results in various domains.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for determining the water quality of freshwater lakes: A case study on selected lakes of Chennai, India

Venkatesh,

Tharanikumar,

Ashokkumar

et al. 2023

Environmental Quality Mgmt

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Monitoring freshwater quality in urban like Chennai is indispensable as the chief drinking water source for the city originates from freshwater lakes. This study portrays an economical, user‐friendly, economical method to detect water quality. A machine learning‐based portable water quality check test kit was developed along with a user interface. To validate the efficiency of the portable kit, this study focussed on the ten major freshwater lakes of Chennai City. Basic quality parameters of water, such as the potential of hydrogen (pH), level of ammonia, nitrite, nitrate, and total phosphate (T.P.), were considered when designing this mobile phone‐compatible user interface application. The experiment used and tested water samples from ten different lakes with the commercially available freshwater master test kit. The images of color developed during the test were captured, and corresponding Red, Green, Blue (RGB) values were calculated. These values were used in Igray, Iavg, and Euclidean distance algorithms to predict the pollution level of the samples. Based on the models, the Euclidean model fitted well with the calibration data with a correlation value of more than 0.98 for all the parameters. Percent accuracy values of the methods used in this study were compared, and the Euclidean model showed more than 98% accuracy for all parameters. The result of this study is alarming that all the ten freshwater lakes considered in this study were hypereutrophic.

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Optimization of hydrothermal gasification process through machine learning approach: Experimental conditions, product yield and pollution

Cited by 43 publications

References 39 publications

Artificial intelligence for waste management in smart cities: a review

Artificial intelligence for waste management in smart cities: a review

Biomass to energy: a machine learning model for optimum gasification pathways

Machine learning approach for determining the water quality of freshwater lakes: A case study on selected lakes of Chennai, India

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