Modeling the Hydrocracking Process with Deep Neural Networks

Song, Wenjiang; Mahalec, Vladimir; Long, Jian; Yang, Minglei; Qian, Feng

doi:10.1021/acs.iecr.9b06295

Cited by 28 publications

(15 citation statements)

References 31 publications

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“…In the present work, the deep neural network model termed SOM-CNN proposed in our previous work 21 is employed as the DDM. The structure of SOM-CNN is demonstrated in Figure 4.…”

Section: Data-driven Modelmentioning

confidence: 99%

“…The main hyperparameters of SOM-CNN include the number of SOMs, dropout, activation function, pooling method, and the network's structure. Systematic experiments have been conducted in our previous work 21 to fully analyze these hyperparameters, based on which practical guidance of its application has been provided. In this work, a brief summary of the training method of SOM-CNN is also provided in the Supporting Information.…”

Section: Data-driven Modelmentioning

confidence: 99%

“…The previous experiment results have demonstrated that SOM-CNN has better fitting and extrapolation ability compared with the conventional feedforward neural network. One can refer to the previous work 21 for more details.…”

Section: Data-driven Modelmentioning

confidence: 99%

“…20 Song et al developed a deep learning framework integrating the self-organizing map (SOM) and the convolutional neural network (CNN) for modeling the industrial HCUs to obtain better fitting and extrapolation performances. 21 More applications of DDMs in chemical engineering can be found elsewhere. 22,23 The third category is hybrid models (HMs), which integrate FPMs and DDMs to obtain better simulation and/or optimization performance.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Weighted Hybrid Modeling of Hydrocracking Process and Its Operational Optimization

Song

Fan

et al. 2021

Ind. Eng. Chem. Res.

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Hybrid modeling, aiming to integrate the advantages of both first-principles models and data-driven models, is an important technology for refinery process simulation and optimization. The commonly used hybrid models include series models, parallel models, and series−parallel models. Many studies have reported the operational optimization results based on these models. However, it is unknown whether the results obtained based on these models are consistent with the actual plant optimal operation. Moreover, the hybrid models that have been used in operational optimization are of traditional structures and cannot ensure competent performance. To fill these gaps, we investigate the modeling and operational optimization of a typical refinery unit hydrocracking unit. First, we establish a first-principles model, a data-driven model, and three hybrid models and analyze the strengths and weaknesses of these models. Next, we propose the concepts of "mechanism-dominated models" and "data-dominated models" to classify the existing models according to the prediction abilities instead of the structural features. Furthermore, a novel type of hybrid model termed adaptive weighted hybrid model (AWHM) is proposed to gain a better balance between extrapolation and interpolation capabilities. Then, the performance of the operational optimization based on these models is compared under four optimization scenarios. Experimental results demonstrate that the two variants of the proposed AWHM have the best optimization performance and their optimization results are the most consistent with the actual process.

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“…In the present work, the deep neural network model termed SOM-CNN proposed in our previous work 21 is employed as the DDM. The structure of SOM-CNN is demonstrated in Figure 4.…”

Section: Data-driven Modelmentioning

confidence: 99%

Section: Data-driven Modelmentioning

confidence: 99%

Section: Data-driven Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Weighted Hybrid Modeling of Hydrocracking Process and Its Operational Optimization

Song

Fan

et al. 2021

Ind. Eng. Chem. Res.

Self Cite

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“…In another study, a deep neural network model for an industrial hydrocracker was investigated. A convolutional neural network (CNN) was compared with a feed-forward neural network [58]. The CNN model showed better prediction accuracy.…”

Section: Data-driven Modelsmentioning

confidence: 99%

Hydrocracking: A Perspective towards Digitalization

2020

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In a world of fast technological advancements, it is increasingly important to see how hydrocracking applications can benefit from and adapt to digitalization. A review of hydrocracking processes from the perspective of modeling and characterization methods is presented next to an investigation on digitalization trends. Both physics-based and data-based models are discussed according to their scope of use, needs, and capabilities based on open literature. Discrete and continuous lumping, structure-oriented lumping, and single event micro-kinetic models are reported as well as artificial neural networks, convolutional neural networks, and surrogate models. Infrared, near-infrared, ultra-violet and Raman spectroscopic methods are given with their examples for the characterization of feed or product streams of hydrocracking processes regarding boiling point curve, API, SARA, sulfur, nitrogen and metal content. The critical points to consider while modeling the system and the soft sensor are reported as well as the problems to be addressed. Optimization, control, and diagnostics applications are presented together with suggested future directions of interdisciplinary studies. The links required between the models, soft sensors, optimization, control, and diagnostics are suggested to achieve the automation goals and, therefore, a sustainable operation.

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Dynamic simulation‐based quantitative hazard and operability process hazard analysis for a hydrocracking unit

Yi,

Wang,

Zhang

2023

Process Safety Progress

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A hydrocracking unit operates at high temperature, high pressure, and near hydrogen, which can be susceptible to fire and explosions accidents due to equipment failure. Therefore, it is essential to identify and analyze the risk factors. The dynamic simulation‐based quantitative hazard and operability (HAZOP) study can effectively and purposefully quantify deviations and consequences as well as reduce the redundancy of safety analysis results. Furthermore, the time parameter is introduced through dynamic simulation, which reflects more realistically the dynamic characteristics of the system in the event of a fault. In this paper, we take the process flow of the absorbing‐stabilizing system of an actual hydrocracking unit in a refining company as an example and carry out steady‐state and dynamic simulations for the deviations of pressure, temperature, and liquid level with the help of Aspen Plus V11 and Aspen Plus Dynamics V11 software to complete the dynamic simulation‐based quantitative HAZOP analysis.

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Modeling the Hydrocracking Process with Deep Neural Networks

Cited by 28 publications

References 31 publications

Adaptive Weighted Hybrid Modeling of Hydrocracking Process and Its Operational Optimization

Adaptive Weighted Hybrid Modeling of Hydrocracking Process and Its Operational Optimization

Hydrocracking: A Perspective towards Digitalization

Dynamic simulation‐based quantitative hazard and operability process hazard analysis for a hydrocracking unit

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