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Ethylene industrial emitters seen from space.

ABSTRACT: Volatile organic compounds are emitted abundantly from a variety of natural and anthropogenic sources. However, in excess, they can severely degrade air quality. Their fluxes are currently poorly represented in inventories due to a lack of constraints from global measurements. Here, we track from space over 300 worldwide hotspots of ethylene, the most abundant industrially produced organic compound. We identify specific emitters associated with petrochemical clusters, steel plants, coal-related industries, and megacities. Satellite-derived fluxes reveal that the ethylene emissions of the industrial sources are underestimated or missing in the state-of-the-art Emission Database for Global Atmospheric Research (EDGAR) inventory. This work exposes global emission point-sources of a short-lived carbonated gas, complementing the ongoing large-scale efforts on the monitoring of inorganic pollutants.

SUBMITTER: Franco B

PROVIDER: S-EPMC9616823 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Ethylene industrial emitters seen from space.

Franco Bruno B Clarisse Lieven L Van Damme Martin M Hadji-Lazaro Juliette J Clerbaux Cathy C Coheur Pierre-François PF

Nature communications 20221028 1

Volatile organic compounds are emitted abundantly from a variety of natural and anthropogenic sources. However, in excess, they can severely degrade air quality. Their fluxes are currently poorly represented in inventories due to a lack of constraints from global measurements. Here, we track from space over 300 worldwide hotspots of ethylene, the most abundant industrially produced organic compound. We identify specific emitters associated with petrochemical clusters, steel plants, coal-related ...[more]

PMID: 36307450

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Project description:Light olefins, as the backbone of the chemical and petrochemical industries, are produced mainly via steam cracking route. Prediction the of effects of operating variables on the product yield distribution through the mechanistic approaches is complex and requires long time. While increasing in the industrial automation and the availability of the high throughput data, the machine learning approaches have gained much attention due to the simplicity and less required computational efforts. In this study, the potential capability of four powerful machine learning models, i.e., Multilayer perceptron (MLP) neural network, adaptive boosting-support vector regression (AdaBoost-SVR), recurrent neural network (RNN), and deep belief network (DBN) was investigated to predict the product distribution of an olefin plant in industrial scale. In this regard, an extensive data set including 1184 actual data points were gathered during four successive years under various practical conditions. 24 varying independent parameters, including flow rates of different feedstock, numbers of active furnaces, and coil outlet temperatures, were chosen as the input variables of the models and the outputs were the flow rates of the main products, i.e., pyrolysis gasoline, ethylene, and propylene. The accuracy of the models was assessed by different statistical techniques. Based on the obtained results, the RNN model accurately predicted the main product flow rates with average absolute percent relative error (AAPRE) and determination coefficient (R2) values of 1.94% and 0.97, 1.29% and 0.99, 0.70% and 0.99 for pyrolysis gasoline, propylene, and ethylene, respectively. The influence of the various parameters on the products flow rate (estimated by the RNN model) was studied by the relevancy factor calculation. Accordingly, the number of furnaces in service and the flow rates of some feedstock had more positive impacts on the outputs. In addition, the effects of different operating conditions on the propylene/ethylene (P/E) ratio as a cracking severity factor were also discussed. This research proved that intelligent approaches, despite being simple and straightforward, can predict complex unit performance. Thus, they can be efficiently utilized to control and optimize different industrial-scale units.

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Ethylene industrial emitters seen from space.

Publications

Ethylene industrial emitters seen from space.

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