Hydroprocessing Catalyst Additive Selection via Complexation Energy
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Solution Overview
Problem
Current methods for selecting organic additives for hydroprocessing catalysts are inefficient, often relying on trial and error, and the large number of potential additives makes it difficult to choose the appropriate one for achieving high catalytic activity.
Innovation Solution
A method involving the use of a correlation model to select organic additives based on their complexation energy, which is incorporated into the catalyst composition to enhance catalytic activity, specifically using amine compounds with complexation energies greater than 490 kcal/mol, and supported on porous refractory oxides like alumina.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial and error method is used to select organic additives, then catalyst activity may be improved, but time and resources are wasted
Solution Approach 1:
The patent applies parameter changes by using complexation energy as a selection criterion for organic additives. Instead of trial and error, the method calculates and compares complexation energy values of different additives to predict their effectiveness, thereby reducing selection time while maintaining catalyst activity improvement.
Solution Approach 2:
The patent replaces the mechanical trial-and-error approach with a computational method. By using correlation models and complexation energy calculations, the selection process transitions from physical experimentation to theoretical prediction, significantly reducing time and resource consumption.
2Reliability
If a large group of organic compounds is considered, then the chance of finding a high activity additive increases, but the difficulty of selection becomes impossible
Solution Approach 1:
The patent reduces selection complexity by changing the selection parameter from a broad qualitative assessment to a specific quantitative metric: complexation energy. This single parameter effectively narrows down the large group of organic compounds to those most likely to provide high catalyst activity.
Solution Approach 2:
The patent extracts the key determining factor (complexation energy) from the complex selection process. By focusing only on this critical parameter, the method simplifies the selection of organic additives from a large compound group while maintaining the ability to identify high-activity candidates.
3Reliability
If more organic additive is incorporated into the catalyst, then catalytic activity increases, but the complexity of catalyst preparation increases
Solution Approach 1:
The patent applies preliminary action by selecting the appropriate organic additive based on complexation energy before the catalyst preparation process begins. This pre-selection ensures that the additive incorporated will provide the desired catalytic activity, avoiding the need for complex optimization during preparation.
Solution Approach 2:
The patent uses complexation energy as a guiding parameter to determine the optimal organic additive and its incorporation level. This parameter-based approach simplifies the preparation process by providing clear criteria for additive selection and dosage, reducing preparation complexity while maintaining high catalytic activity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for the efficient selection and incorporation of organic additives that significantly enhance the catalytic activity of hydroprocessing catalysts, particularly in hydrodesulfurization processes, achieving ultra-low sulfur levels in petroleum-derived hydrocarbon products.
Implementation Method 1
selecting an organic additive from a group of organic additives, wherein said organic additive has a complexation energy
Data Source
AI summary
A composition and method of making such a composition that has application in the hydroprocessing of hydrocarbon feedstocks. The method comprises selecting an organic additive by the use of a correlation model for predicting catalytic activity as a function of a physical property that is associated with the organic additive and incorporating the organic additive into a support material to provide the additive impregnated composition.