Dynamic assessment and optimization of catalytic hydroprocessing process: sensitivity analysis and practical tips
Bibliographic record
Abstract
Among the processes in petrochemical industry, hydroprocessing is an imperative process to produce clean fuels. This process is still being improved despite its 70-year maturity. Catalyst deactivation is a key aspect in the design and operation of catalytic processes in petrochemical industry. In this research, a dynamic heterogeneous model is presented to evaluate the performance of an industrial hydropurification/hydrotreating process in the purified terephthalic acid (PTA) production plant. This process includes a trickle-bed reactor (TBR) packed with palladium supported on carbon (0.5wt.% Pd/C) catalyst. In fact, this chemical production unit represents a three-phase catalytic system where some chemical reactions take place. Therefore, an accurate and meticulous analysis is required to develop a proper mathematical model, taking into account all transport phenomena occurring in the system. The model considers the axial back-mixing, flow non-ideality, and the catalyst deactivation. Model development leads to a set of partial differential equations consisting of nonlinear equations of the reaction rates, nonlinear expression of the catalyst deactivation rate, mass balance of each component in the reaction mixture, and energy balance of each phase. The model parameters are calculated using suitable correlations. The set of partial differential equations is solved using proper numerical techniques, including method of lines and finite difference method, in MATLAB software environment. First, the model reliability is assessed through the comparison of the model results with the industrial data. The validation phase confirms that the model results are accurate, and the developed model can be used for further process evaluation. A sensitivity analysis is then implemented to assess the effects of different operating parameters on the performance of the hydropurification/hydrotreating process. The results reveal that axial dispersion model is more accurate than the plug flow model. Moreover, 4- carboxybenzaldehyde (4-CBA) impurity in the reactor feed is the most detrimental parameter, affecting the catalytic performance. It is found that reduction in the catalyst particle size can improve the catalyst performance by about 16%, and an increase in the catalyst particle porosity can enhance the catalyst lifetime by around 8%. In this condition, the catalyst bed pressure drop is maintained at an acceptable level. In addition, 13% increase in the hydrogen partial pressure enhances the catalyst lifetime by about 20%. It should be noted that pressure increase might lead to the reactor pressure fluctuation, leading to an increase in the PTA powder turbidity. Therefore, reactor operation control is a critical factor. Considering other hydrodynamic parameters, a decrease in liquid hourly space velocity and the catalyst bed porosity improves the system performance in terms of catalyst lifetime and product quality. An increase in the liquid-solid mass transfer and contacting efficiency has a slight positive impact on the catalytic system performance. Product quality control can be carried out more properly if the feed impurity concentration is managed/controlled efficiently. In this research, a practical strategy is presented to effectively mix the feed streams having varying concentrations of the impurities (e.g., high and low concentrations of 4-CBA). This can be achieved by suggesting a proper ratio control, keeping the feed composition and flowrate at normal operating conditions. This strategy can also be employed to deal with the off-spec PTA powder product. In addition, the effect of temperature on the sintering mechanism of the Pd/C catalyst deactivation is investigated. The results reveal that temperature increase can accelerate the decline rate of the Pd/C catalyst surface area. The reduced activity of Pd/C catalyst is in an acceptable agreement with the normalized ratio of reduction in the surface area of pure Pd with increasing temperature. In the last phase, an efficient methodology is proposed to assess the hydroprocessing process in terms of energy and exergy performance. The process simulation and exergy analysis are conducted using Aspen Plus® and MATLAB software packages. The results are in a satisfactory agreement with the industrial data. It is concluded that the optimal operating conditions result in 15% reduction in the exergy destruction; the optimal scenario can also reduce the operation costs and the carbon tax at 9.96% ($20.5/h) and 14.75% ($14.54/h), respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".