Generalized models to predict the lower heating value (LHV) of municipal solid waste (MSW)

Wang, Dan; Yang, Fu‐Mei; Robinson, Darren

doi:10.1016/j.energy.2020.119279

Cited by 44 publications

(10 citation statements)

References 38 publications

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“…Machine learning techniques, such as random forests and support vector machines, can uncover latent connections and forecast the properties of solid refuse groups. The carbon content of biological sources and fossils can be calculated in terms of mass using infrared spectroscopy and machine learning, allowing researchers to evaluate the effect of solid refuse burning on decreasing carbon emissions and saving substantial labor and reagents Guo et al ( 2021 ); Schwarzböck et al ( 2018 ); Wang et al ( 2021 ); Yuan et al ( 2021 ) In biological processing, machine learning algorithms can be used to separate impurities from raw materials, compost, and solid digests. This can help reduce possible environmental risks and improve the profitability of compost and anaerobic digestion products Waste-to-bioenergy Porous carbon produced from biomass refuse is a complex material extensively used in sustainable waste management and carbon capture.…”

Section: Chemical Analysis Of Waste Using Artificial Intelligencementioning

confidence: 99%

“…Moreover, many studies have focused on only one or, at most, two types of models, providing limited data for comparing the prediction accuracy of various models based on the same waste dataset (Wang et al 2021 ). In the case of separately weighted garbage, the reduced total reflection infrared spectra to determine the mass-based amounts of biogenic and fossil carbon, Fourier-transform infrared, can be used with a machine learning method.…”

Section: Chemical Analysis Of Waste Using Artificial Intelligencementioning

confidence: 99%

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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%

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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“…Furthermore, most studies pay attention to one 17 or at best utilize two types of models. 18 Therefore, information about the comparative analysis of the prediction performance using different models based on the same waste data set is limited.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning (ML), a data mining tool for pattern discovery, has been proven to be promising for solving complicated environmental problems . Using established databases (e.g., the Phyllis2 Database for the physiochemical composition of biomass and waste) and manually extracted data from the literature, ML algorithms, such as random forest (RF) and support vector machine (SVM), have emerged as powerful tools for uncovering hidden relationships to predict solid waste categories and their properties . However, the lack of algorithm popularity is rooted in three possible reasons: (1) the limited size and low quality of data hamper the performance; , (2) poor interpretability affords a difficult information extraction process; , and (3) burdensome computation causes a long computation time.…”

Section: Introductionmentioning

confidence: 99%

“…However, the lack of algorithm popularity is rooted in three possible reasons: (1) the limited size and low quality of data hamper the performance; , (2) poor interpretability affords a difficult information extraction process; , and (3) burdensome computation causes a long computation time. Furthermore, most studies pay attention to one or at best utilize two types of models . Therefore, information about the comparative analysis of the prediction performance using different models based on the same waste data set is limited.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste

Lan

et al. 2023

Anal. Chem.

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Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R 2 = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R 2 value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.

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Artificial intelligence model of fuel blendings as a step toward the zero emissions optimization of a 660 MWe supercritical power plant performance

Amjad¹,

Ashraf

Uddin

et al. 2023

Energy Science & Engineering

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Accurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning‐based models for predicting the LHV of various fuel blends. Coal of two different ranks and two types of biomass is used in this study. One was the South African imported bituminous coal, and the other was lignite thar coal extracted from the Thar Coal Block‐2 mine by Sind Engro Coal Mining Company, Pakistan. Two types of biomass, that is, sugarcane bagasse and rice husk, were obtained locally from a sugar mill and rice mill located in the vicinity of Sahiwal, Punjab. Bituminous coal mixture with other coal types and both types of biomass are used with 10%, 20%, 30%, 40%, and 50% weight fractions, respectively. The calculation and model development procedure resulted in 91 different AI‐based models. The best is the Ridge Regressor, a high‐level end‐to‐end approach for fitting the model. The model can predict the LHV of the bituminous coal with lignite and biomass under a vast share of fuel blends.

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Generalized models to predict the lower heating value (LHV) of municipal solid waste (MSW)

Cited by 44 publications

References 38 publications

Artificial intelligence for waste management in smart cities: a review

Artificial intelligence for waste management in smart cities: a review

Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste

Artificial intelligence model of fuel blendings as a step toward the zero emissions optimization of a 660 MWe supercritical power plant performance

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