Hydrocracking Process Research Articles

Molecular transformation of heavy oil during slurry phase hydrocracking process: influences of operational conditions

The influences of reaction temperature, duration, pressure, and catalyst concentration on the molecular transformation of residual slurry phase hydrocracking process were investigated. The molecular composition of the heteroatom compounds in the residue feedstock and its upgrading products were characterized using high-resolution Orbitrap mass spectrometry coupled with multiple ionization methods. The simultaneous promotion of cracking and hydrogenation reactions was observed with increasing of the reaction temperature and time. Specifically, there was a significant increase in the cracking degree of alkyl side chain, while the removal of low-condensation sulfur compounds such as sulfides and benzothiophenes was enhanced. In particular, the cracking reactions were more significantly facilitated by high temperatures, while an appropriately extended reaction time can result in the complete elimination of the aforementioned sulfur compounds with a lower degree of condensation. Under conditions of low hydrogen pressure and catalyst concentration, the products still exhibit a high relative abundance of easily convertible compounds such as sulfoxides, indicating a significant deficiency in the effectiveness of hydrogenation. The hydrogen pressure exhibits an optimal value, beyond which further increments have no effect on the composition and performance of the liquid product but can increase the yield of the liquid product. At significantly high catalyst concentration, the effect of desulfurization and deoxidation slightly diminishes, while the aromatic saturation of highly condensed compounds was notably enhanced. This hydrogenation saturation effect cannot be attained through manipulation of other operational parameters, thereby potentially benefiting subsequent product processing and utilization. This present study demonstrates a profound comprehension of the molecular-level residue slurry phase hydrocracking process, offering not only specific guide for process design and optimization but also valuable fundamental data for constructing reaction models at the molecular level.

Preparation of presulfided oil-soluble NiMo catalyst for slurry bed hydrocracking of vacuum residue

Here, a method is proposed for constructing presulfided oil-soluble NiMo catalysts using tetrathiomolybdate nickel ammonium as a precursor via a chelating ligand exchange strategy. Leveraging the close chemical bonding between Ni and Mo atoms, the NiMo catalyst can undergo in-situ self-sulfidation to generate active NiMoS sites and a composite structure of Ni3S2 and MoS2 nano phase in tight contact, thus exhibiting strong synergistic effects. The NiMo catalyst exhibited outstanding performance in hydrocracking of vacuum residue (VR) achieving a 64.7 wt% VR conversion while maintaining a coke yield of 0.51 wt%, as well as the excellent hydrogenation activity of polycyclic aromatic hydrocarbons. Combined with density functional theory calculations, it was further revealed that the presence of Ni reduces the energy barrier for H2 dissociation on MoS2, thereby enhancing the hydrogenation activity of the catalyst and its ability to suppress coke formation. This work validates the efficient bimetallic synergistic catalytic effect of presulfided oil-soluble NiMo catalysts in the slurry bed hydrocracking process.

Transferable preference learning in multi-objective decision analysis and its application to hydrocracking

Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality of products within the oil refining process. Consequently, the development of efficient surrogate models for simulating the hydrocracking process and identifying appropriate solutions for multi-objective oil refining is now an important area of research. In this study, a novel transferable preference learning-driven evolutionary algorithm is proposed to facilitate multi-objective decision analysis in the oil refining process. Specifically, our approach involves considering user preferences to divide the objective space into a region of interest (ROI) and other subspaces. We then utilize Kriging models to approximate the sub-problems within the ROI. In order to enhance the robustness and generalization capability of the Kriging models during the evolutionary process, we transfer the mutual information between the sub-problems in the ROI. To validate the effectiveness as well as efficiency of our proposed method, we undertake a series of experiments on both benchmarks and the oil refining process. The experimental results conclusively demonstrate the superiority of our approach.

Activation of carbon sorbents used in the hydrocracking process of vacuum oil distillation residue

Activation of carbon sorbents used in the hydrocracking process of vacuum oil distillation residue

Conversion of Sunan Candlenut Oil to Aromatic Hydrocarbons with Hydrocracking Process Over Nano-HZSM-5 Catalyst

In this paper, the catalysts (Nano-HZSM-5 and Fe-La/nano HZSM-5) were prepared with incipient wetness impregnation and applied for hydrocracking of Sunan candlenut oil. The hydrocracking process was conducted in a batch reactor with a pressure of 20-30 bar H2 gas for 2 h under various temperatures. The results demonstrated that hydrocracking of Sunan candlenut oil using nano HZSM-5 and Fe-La/NHZ catalysts could be converted into aromatic hydrocarbons, and the reaction temperature affected hydrocarbon production. The aromatic compounds, such as propyl-benzene, 1-ethyl-3-methylbenzene, heptyl-benzene, 2-ethyl-naphthalene, etc., reached 35.51% over the Fe-La/NHZ_2 catalyst. In all cases, the zeolite-based catalysts are the most suitable to produce aromatic hydrocarbons. Metal impregnated (Fe and La) on nano HZSM-5 catalyst could improve the aromatics compounds due to increased metal and acid sites. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

ANN and RSM Modelling and Optimization of Paraffins and Aromatics in Crude Oil Distillation Products’ Properties in Iraq

Back-Propagation neural networks, as well as RSM-DOE techniques, were used to predict the properties of various compositions of Iraqi oil, which were presented in this study. Paraffin and Aromatics’ effect on petroleum properties, e.g., yield, density, calorific value, and other essential properties, were studied. The input-output data to the neural networks were obtained from existing local refineries in Iraq. Several network activation functions to simulate the hydrocracking process were tested and compared. the network function that gave satisfactory results in terms of convergence time and accuracy was adopted. The data were divided into training and testing parts. The results of the trained artificial neural network models for each one of the tested functions have been cross-validated with the experimental data. The network that compared well against this new set of data (i.e. testing data), with an average percent error always less than 3% for the various products of the hydrocracking unit were chosen for the study. Aromatics showed to have more profound effect on the Octane number at low concentrations of paraffin, while, for specific gravity and calorific value they have similar effects. As for boiling points and sulfur contents, aromatics have almost no effect at lower levels of paraffin.

Low pressure hydrocracking process for the production of a high yield of middle distillates from a high boiling hydrocarbon feedstock

Low pressure hydrocracking process for the production of a high yield of middle distillates from a high boiling hydrocarbon feedstock

Stable and selective multi-porous nickel nanoparticles catalysts for hydrocracking of dibenzyl ether supported by DFT studies

Preparation of clean fuel from biomass has a crucial significance on the development and utilization of renewable clean energy. In this study, a multi-stage porous mordenite supported nickel nanoparticles catalyst (Nix%/HMOR) was prepared, which showed that its acid content, acidity, porosity, specific surface area and metal dispersion were significantly improved while the crystallization strength was basically the same compared with microporous mordenite. In order to study the conversion mechanism of biomass energy, dibenzyl ether was selected as a typical lignin model compound. Under the condition of Ni10%/HMOR in n-hexane at 160 °C for 2 h under 5 MPa of initial hydrogen pressure, the conversion of dibenzyl ether was 100 % and the selectivity of methyl cyclohexane was 84.4 %, which manifested that Ni10%/HMOR had good activity in the selective hydrocracking process under mild conditions. Furthermore, density functional theory (DFT) was selected to study the activation energies of different reaction sites on dibenzyl ether bridge bonds and the recombination process of free radicals in the reaction process. It comes to a conclusion that the O atom on the C-O bridge bond is easier to be attacked by H*, and the C-O bond of the unsaturated aromatic ring is easier to break. During the recombination of free radicals, the two C-O bond breaking energy barriers of OMD are different, and H free radicals are more likely to attack the O atom in cyclohexane-methanol. It is conducive to the formation of stable compound 1 - (cyclohexylmethyl) − 1-methylcyclohexane and its isomers.

Development of chromium-impregnated sulfated silica as a mesoporous catalyst in the production of biogasoline from used cooking oil via a hydrocracking process

Development of chromium-impregnated sulfated silica as a mesoporous catalyst in the production of biogasoline from used cooking oil via a hydrocracking process

Hierarchical Self-Attention Network for Industrial Data Series Modeling With Different Sampling Rates Between the Input and Output Sequences.

For industrial processes, it is significant to carry out the dynamic modeling of data series for quality prediction. However, there are often different sampling rates between the input and output sequences. For the most traditional data series models, they have to carefully select the labeled sample sequence to build the dynamic prediction model, while the massive unlabeled input sequences between labeled samples are directly discarded. Moreover, the interactions of the variables and samples are usually not fully considered for quality prediction at each labeled step. To handle these problems, a hierarchical self-attention network (HSAN) is designed for adaptive dynamic modeling. In HSAN, a dynamic data augmentation is first designed for each labeled step to include the unlabeled input sequences. Then, a self-attention layer of variable level is proposed to learn the variable interactions and short-interval temporal dependencies. After that, a self-attention layer of sample level is further developed to model the long-interval temporal dependencies. Finally, a long short-term memory network (LSTM) network is constructed to model the new sequence that contains abundant interactions for quality prediction. The experiment on an industrial hydrocracking process shows the effectiveness of HSAN.

Quality-Driven Regularization for Deep Learning Networks and Its Application to Industrial Soft Sensors.

The growth of data collection in industrial processes has led to a renewed emphasis on the development of data-driven soft sensors. A key step in building an accurate, reliable soft sensor is feature representation. Deep networks have shown great ability to learn hierarchical data features using unsupervised pretraining and supervised fine-tuning. For typical deep networks like stacked auto-encoder (SAE), the pretraining stage is unsupervised, in which some important information related to quality variables may be discarded. In this article, a new quality-driven regularization (QR) is proposed for deep networks to learn quality-related features from industrial process data. Specifically, a QR-based SAE (QR-SAE) is developed, which changes the loss function to control the weights of the different input variables. By choosing an appropriate inductive bias for the weight matrix, the model provides quality-relevant information for predictive modeling. Finally, the proposed QR-SAE is used to predict the quality of a real industrial hydrocracking process. Comparative experiments show that QR-SAE can extract quality-related features and achieve accurate prediction performance.

Boosting the catalytic performance of metal–zeolite catalysts in the hydrocracking of polyolefin wastes by optimizing the nanoscale proximity

Loading Pt exclusively on the surface of USY constructs a consecutive hydrocracking process of polyolefin wastes.

Soft analyzer for sulfur content in tail oil of hydrocracking process based on adaptively regularized dynamic polynomial PLS

AbstractHydrocracking process is an essential and widely used means to remove impurities such as sulfur, nitrogen, and oxygen from heavy oil in refineries. The sulfur content in the tail oil of the hydrogenation process is a key quality index that must be strictly monitored and controlled. However, the traditional measurement ways of the sulfur content suffer from either large delays or high investment and maintenance costs. To this end, a predictive model named “adaptively regularized dynamic polynomial partial least squares (ARDPPLS)” is proposed to develop soft analyzer for sulfur content estimation. The ARDPPLS takes complicated characteristics of the hydrogenation process into consideration, including process dynamics, nonlinearities, and transportation delays of materials and energies. Specifically, the dynamics and nonlinearities are modeled by the finite impulse response paradigm and polynomial fitting, respectively, and the delays of explanatory variables are automatically determined using differential evolution. In particular, a Bayesian inference‐based adaptive regularization scheme for the polynomial partial least squares is designed to tackle overfitting that results from high‐order variable augmentation and high‐order polynomials. The performance of the ARDPPLS is evaluated by a real‐world industrial wax oil hydrocracking process, showing the effectiveness of the ARDPPLS.

Numerical Analysis of Gas Hold-Up of Two-Phase Ebullated Bed Reactor

Due to the significant increase in heavy feedstocks being transported to refineries and the hydrocracking process, the significance of adopting an ebullated bed reactor has been reemphasized in recent years. The predictive modelling of gas hold-up in an ebullated two-phase reactor was performed using 10 machine learning methods based on support vector machine (SVM) and Gaussian process regression (GPR) in this study. In an ebullated bed reactor, the impacts of three features, namely liquid velocity, gas velocity, and recycling ratio, on the gas hold-up were examined. The liquid velocity has the most impact on the predicted gas hold-up, according to the feature significance analysis. The rotational-quadratic, squared-exponential, Matern 5/2, and exponential kernel functions integrated with the GPR models and the linear, quadratic, cubic, fine, medium, and coarse kernel functions integrated with the SVM model performed well during training and testing, with the exception of the fine SVM model, whose R2 is very low. According to the R2 > 0.9 and low RMSE and MAE values, the rotational-quadratic, squared-exponential, and Matern 5/2 GPR models performed the best.

Data-driven prediction of product yields and control framework of hydrocracking unit

In this study, the relationship between the operating conditions and the product yields and a control framework of the hydrocracking process was developed. The data were collected from a hydrocracking unit in a Chinese refinery. Principal component analysis was used to decrease the number of input variables. Then support vector machine, Gaussian process regression (GPR), and decision tree regression models were developed to establish the relationship above. The best model is GPR, whose Pearson correlation coefficient between the prediction value and the actual value is greater than 0.97 for all the product yields. Shapley additive explanations were performed to interpret the results of the GPR models. A control framework of the hydrocracking unit was then proposed based on the results above. The results show that the machine learning method is a valuable tool for predicting the yield of hydrocracking products, and the control framework proposed helps optimize hydrocracking product yields.

Hydrocracking of heavy oil residues with the participation of Ni-bentonite

In the article, Ni-bentonite catalyst was synthesized by exchange method. Natural bentonite mineral and synthesized catalyst were studied by XRD, XRF, TGA/TDA, TEM, EDX-mapping (map), nitrogen adsorption-desorption at 196 °C, H2-TPD and NH3-TPD analysis methods. A new processing technology was introduced at the «Hydrocracking of heavy oil residues» (SPR-1) facility with the participation of fuel oil bentonite and Ni-bentonite catalysts. The catalytic efficiency of dispersed nickel catalyst in the hydrocracking process of fuel oil under optimal conditions (430 °C, 4.0 MPa) was studied. The results of the experiments are 47% from catalyst-free hydrocracking of fuel oil; 54.7% with bentonite addition; The yield of light colored petroleum products showed a corresponding increase to 67.1% when Ni-bentonite was used. Keywords: fuel oil; hydrocracking; bentonite; gasoline fraction; diesel fraction.

Hydrocracking of polyethylene to hydrocarbon fuels over Pt/USY catalysts: Assessment of the hydrogen donors

Hydrocracking is an effective approach to convert waste polyolefins into value-added fuels. Efficient hydrogen transfer from the hydrogen donors to the polyolefins is crucial to the hydrocracking process. In this work, we evaluated the hydrocracking effectiveness of different hydrogen donors (H2, tetralin and H2+tetralin) in polyethylene hydrocracking over a Pt/USY catalyst by analyzing and comparing the product distribution under varied H circumstances. The hydrocracking pathway using gaseous (H2) and liquidous (tetralin) hydrogen donors was proposed. The results show that the most effective H-donor for fuel oil production is H2, which yielded 65.8% oil abundant in C6–C12 range after reaction at 350 °C for 60 min. Tetralin can be dehydrogenated by Pt/USY catalyst to provide active H for PE hydrocracking, but the hydrocracking effectiveness is inferior to that using H2 because of catalyst deactivation caused by the by-products such as naphthalene from tetralin dehydrogenation. For the same reason, using H2+tetralin as H-donors is not as efficient for oil production as using sole H2 as H-donor. The reaction pathway of PE hydrocracking over Pt/USY catalyst using H2 and tetralin as the H-donor is different. Hydrocracking with H2 follows the hydrogenolysis pathway. When tetralin is presented, the hydrogenolysis pathway is cut off, followed by the thermal reactions between tetralin and PE, which inhibits PE cracking and decreases oil production.

The Effect of Carbon Nanofibers on the Hydrocracking of Vacuum Residue in the Presence of Formic Acid

This study was devoted to the processing of vacuum residue to produce lighter oil fractions, such as gasoline and diesel fuel. The hydrocracking and catalytic hydrocracking of vacuum residue in the presence of formic acid (FA) were performed in the temperature range of 250–550 °C. Carbon nanofibers (CNFs) were used as catalytic additives. In contrast to conventional hydrocracking, an important stage in the catalytic hydrocracking of vacuum residue is the decomposition of formic acid. Experimental studies on the effect of CNFs on the decomposition of FA demonstrated that CNFs pre-treated in a NaOH solution (CNF (NaOH)s) had the highest activity and selectivity for the production of H2 and CO2. The maximum yield of liquid products in the catalytic hydrocracking process, equal to 34 wt.%, was observed at 300 °C in the presence of CNF (NaOH)s. The characterization of the fractional compositions of the liquid products showed that the ratios of the fractions changed with an increase in the reaction temperature. The maximum concentrations of the light fractions (gasoline and diesel) in the liquid products of the catalytic hydrocracking of vacuum residue were observed at 300–350 °C in the presence of CNF (NaOH)s.

Numerical Investigation on Heat Transfer and Flow Resistance Characteristics of Superheater in Hydrocracking Heat Recovery Steam Generator

The heat recovery steam generator (HRSG) was utilized to recover the waste heat resources of the catalyst’s regenerated gas with the objective to reduce the energy consumption of the hydrocracking process. In this study, the flow resistance and heat transfer performance of the superheater tube bundles in the hydrocracking HRSG were investigated via numerical simulation. The performance evaluation criterion (PEC1) was applied to characterize the comprehensive heat transfer performance of superheater tube bundles. The results showed that as the transverse tube pitch increased, the Nusselt number (Nu) showed a monotonically increasing trend, the Euler number (Eu) showed a monotonically decreasing trend, and PEC1 showed a monotonically increasing trend. As the longitudinal tube pitch increased, Nu exhibited a monotonically increasing trend, Eu showed a monotonically decreasing trend, and PEC1 showed a monotonically increasing trend. In the scope of the simulated results, as the transverse and longitudinal tube pitches were 110 mm and 95 mm, respectively, PEC1 reached the maximum value. Compared with the primary structural parameters, PEC1 increased by 2.32% and 8.50%, respectively. Finally, a new correlation was proposed to predict Nu and Eu of the superheater tube bundles in the hydrocracking HRSG.

Coprocessing Partially Hydrodeoxygenated Hydrothermal Liquefaction Biocrude from Forest Residue in the Vacuum Gas Oil Hydrocracking Process

Coprocessing Partially Hydrodeoxygenated Hydrothermal Liquefaction Biocrude from Forest Residue in the Vacuum Gas Oil Hydrocracking Process