Carbon-efficient production planning for long-chain integrated refinery-petrochemical processes: A material-energy-carbon optimization perspective

Zhang, Tingwei; Shen, Feifei; Peng, Xin; Li, Zhi; Zhong, Weimin

doi:10.1016/j.jclepro.2023.138916

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…Process parameters are essential in the process industry, as they significantly impact the product quality and production efficiency in different units and process steps [36]. These parameters include temperature, pressure, flow rate, catalyst feed rate, hydrogen usage, and ratios.…”

Section: Process Parameters In the Process Industrymentioning

confidence: 99%

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

Wang,

Zhang

et al. 2023

Processes

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In order to diagnose abnormal trends in the process parameters of industrial production, the Expert System based on rolling data Kernel Principal Component Analysis (ES-KPCA) and Support Vector Data Description (ES-SVDD) are proposed in this paper. The expert system is capable of identifying large-scale trend changes and abnormal fluctuations in process parameters using data mining techniques, subsequently triggering timely alarms. The system consists of a rule-based assessment of process parameter stability to evaluate whether the process parameters are stable. Also, when the parameters are unstable, the rolling data-based KPCA and SVDD methods are used to diagnose abnormal trends. ES-KPCA and ES-SVDD methods require adjusting seven threshold parameters during the offline parameter adjustment phase. The system obtains the adjusted parameters and performs a real-time diagnosis of process parameters based on the set diagnosis interval during the online diagnosis phase. The ES-KPCA and ES-SVDD methods emphasize the real-time alarms and the first alarm of process parameter abnormal trends, respectively. Finally, the system validates the experimental data from UniSim simulation and a chemical plant. The results show that the expert system has an outstanding diagnostic performance for abnormal trends in process parameters.

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Section: Process Parameters In the Process Industrymentioning

confidence: 99%

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

Wang,

Zhang

et al. 2023

Processes

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“…new challenges [1][2][3]. Molecular-level understanding and interpretations of the complex refining process have received increasing attention, which render molecular management a focus of petrochemical industry development [4].…”

Section: Introductionmentioning

confidence: 99%

MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation

Wei,

Qiu

2024

J. Phys.: Condens. Matter

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In the context of carbon neutrality and carbon peaking, molecular management has become a focus of the petrochemical industry. The key to achieving molecular management is molecular reconstruction, which relies on rapid and accurate calculation of oil properties. Focusing on naphtha, we proposed a novel property prediction model construction procedure (MDs-NP) employing molecular dynamics simulations for property collections and gamma distribution from real analytical data for calculating mole fractions of simulation mixtures. We calculated 348 sets of mixture properties data in the range of 273K to 300K by molecular dynamics simulations. Molecular feature extraction was based on molecular descriptors. In addition to descriptors based on open-source toolkits (RDKit and Mordred), we designed 12 naphtha knowledge (NK) descriptors with a focus on naphtha. Three machine learning algorithms (support vector regression, extreme gradient boosting and artificial neural network) were applied and compared to establish models for the prediction of the density and viscosity of naphtha. Mordred and NK descriptors + support vector regression algorithm achieved the best performance for density. The selected RDKFp and NK descriptors + artificial neural network algorithm achieved the best performance for viscosity. Using ablation studies, T, P_w and CC(C)C are three effective descriptors in NK that can improve the performance of the property prediction models. MDs–NP has the potential to be extended to more properties as well as more-complex petroleum systems. The models from MDs–NP can be used for rapid molecular reconstruction to facilitate construction of data-driven models and intelligent transformation of petrochemical processes.

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Synergizing low-carbon planning and operation for sustainable integrated refinery-petrochemical processes under arrival time uncertainty: A large-scale hierarchical energy-efficiency optimization perspective

Zhang,

Shen,

et al. 2025

Applied Energy

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Carbon-efficient production planning for long-chain integrated refinery-petrochemical processes: A material-energy-carbon optimization perspective

Cited by 5 publications

References 34 publications

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters

MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation

Synergizing low-carbon planning and operation for sustainable integrated refinery-petrochemical processes under arrival time uncertainty: A large-scale hierarchical energy-efficiency optimization perspective

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