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Molecular optimization in fluid catalytic cracking process: cartesian coordinate analysis for enhancing efficiency and quality in crude oil refining

Optimizaci&oac= ute;n molecular en el proceso de fluid catalytic cracking: análisis de coordenadas cartesianas para mejora de eficiencia y calidad en la refinación de crudo

¹=	Sandra Elizabeth Través Osorio		ht= tps://orcid.org/0000-0002-4546-4541
	Independent researcher sandt197@gmail.com=
²=	Nancy Orlheni Nacimba Rivera=		ht= tps://orcid.org/0009-0007-7623-2421
	Independent researcher nacimba.nancygr12@gmail.com
³=	Milton Javier Robalino Cacuango &nbs= p; &= nbsp; https://orcid.org/0009-0005-0958-1117<= o:p>		https://orcid.org/0000-0002-1611-5801
	Departm= ent of Energy and Mechanical Sciences, Petrochemistry Department, Campus Académico General Guillermo Rodríguez Lara, Universidad de = las Fuerzas Armadas-ESPE, Latacunga, Peru. mjrobalino1@espe.edu.ec
⁴	Alex Santiago Moreno Corrales &nbs= p; &= nbsp; https://orcid.org/0009-0002-2284-3052 Independ= ent researcher asmor= eno354@gmail.com	&nb= sp;	&nb= sp;

Artículo de Investigación Científica y Tecnológica

Enviado: 15/04/2024

Revisado: 10/05/2024

Aceptado: 10/06/2024

Publicado:05/07/2024

DO= I: https://doi.org/10.33262/= cienciadigital.v8i3.3079 <= span style=3D'mso-spacerun:yes'>

Cítese:

Través Osorio, S. E., Nacimba Rivera, = N. O., Robalino Cacuango, M. J., & Moreno Corrales, A. S. (2024). Optimización molecular en el proceso de fluid catalytic cracking: análisis de coordenadas cartesianas para mejora de eficiencia y ca= lidad en la refinación de crudo. Ciencia Digital, 8(3), 47-63. https://doi.org/10.33262/cienciadigital.v8i3.3079=

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Palabras claves:

formación vocac= ional, orientación profesional, carreras técnicas, estudiantes de media superior.

Resum= en

Introducción: En el refinado de petróleo crudo, el proceso de craqueo catalítico fluido (FCC) convierte el petróleo crudo en productos petroquímicos de alta calidad. Comprender las interacciones moleculares en FCC es crucial para fines de optimización, eficiencia y calidad. Este estudio cuantitativo y descriptivo analiza las coordenadas cartesianas de compuestos clave, empleando química computacional para este propósito. Met= odología: Cuantitativa y descriptiva. A través de una revisión de la literatura, se identificaron compuestos químicos típicos que alimentan el proceso de FCC, incluidas parafinas, olefinas, aromát= icos y naftenos, entre otros. Estos compuestos se procesaron mediante química computacional para obtener sus coordenadas 3D, optimizando= su geometría molecular para representar la estructura real, garantiza= ndo una precisión confiable de los datos en simulaciones y anál= isis posteriores. Análisis y discusión de resultados: Las coordenadas cartesianas ayudan a comprender e identificar las condiciones operativas óptimas, mejorando la comprensión de las interacciones moleculares en tiempo real y facilitando la predicció= ;n de comportamientos de separación. Estas coordenadas están previstas para optimizar los procesos de refino de crudo en FCC, mediante= la modelización y visualización de movimientos y colisiones a nivel atómico. Conclusiones: La optimización de la geometría molecular utilizando el campo de fuerza apropiado es cru= cial para obtener coordenadas cartesianas precisas. Estas coordenadas permiten= la simulación de interacciones moleculares a nivel atómico, el diseño de catalizadores más eficientes y la optimizaci&oacu= te;n de procesos de refinado. Además, el monitoreo en tiempo real con d= atos moleculares precisos podría garantizar una calidad constante del producto en FCC.

Keywords: =

Cartesian coordinates, Fluid Catalytic Cracking (FCC), refinery, computational chemistry, optimization energy. <= /o:p>

AbstractIntroduction: In crude oil refining, the Fluid Catalytic Cracking (FCC) process converts crude o= il into high-quality petrochemical products. Understanding molecular interactions in FCC is crucial for optimization, efficiency, and quality purposes. This quantitative and descriptive study analyzes Cartesian coordinates of key compounds, employing computational chemistry for this purpose. Methodology: Quantitative and descriptive. Through a literature review, typical chemical compounds feeding into the FCC process were identified, including paraffins, olefins, aromatics, and naphthenes among others. These compounds were processed using computational chemistry to obtain their 3D coordinates, optimizing their molecular geometry to repre= sent the real structure, ensuring reliable data accuracy in subsequent simulat= ions and analysis. Analysis and Discussion of Results: Cartesian coordinates a= id in understanding and identifying optimal operating conditions, enhancing = the comprehension of molecular interactions in real time and facilitating the prediction of separation behaviors. These coordinates are envisaged to optimize crude oil refining processes in FCC, through modeling and visualization of atomic-level movements and collisions. Conclusions: Optimizing molecular geometry using the appropriate force field is crucial for obtaining precise Cartesian coordinates. These coordinates enable the simulation of molecular interactions at the atomic level, design of more efficient catalysts, and optimization of refining processes. Additionally, real-time monitoring with accurate molecular data could ensure consistent product quality in FCC.

Introduct= ion:

Crude oil refining is a complex process that involves transforming crude oil into high-quality petrochemical products. At the core of this refining process l= ies Fluid Catalytic Cracking (FCC), which enables the production of high-demand fuels and chemicals. However, understanding the molecular interactions with= in this process is crucial for optimizing efficiency and product quality.=

In this context, there is a growing need to increase the yield of refinery products, such as LPG, gasoline, and other high-octane products, in both national and international markets. Consequently, there is a demand to contribute to scientific development through theoretical-computational studies of chemical species reported in various bibliographic sources.

How can t= he Cartesian coordinates of molecules influence the optimization of crude oil refining processes?

The objec= tive of this article is to analyze the Cartesian coordinates of key compounds in= the FCC process and explore their implications for process optimization and understanding molecular interactions. Understanding the Cartesian coordinat= es of molecules is crucial for comprehending how molecules interact in the FCC process, thereby enabling the optimization of efficiency and product qualit= y. Additionally, molecular dynamics simulations allow us to predict how molecu= les interact with each other, facilitating the modeling and visualization of th= ese atomic-level movements and collisions. This knowledge is vital for understanding how molecules transform during catalytic processes, allowing = for precise simulations of the chemical reactions that occur during crude oil refining.

The justification for this study lies in the importance of understan= ding the Cartesian coordinates of molecules to comprehend how they interact in t= he FCC process, thereby enabling the optimization of efficiency and product quality. The methodology used in this study is quantitative and descriptive, relying on the compilation and analysis of data through a comprehensive literature review and subsequent analysis using computational chemistry techniques.

This approach aims to yield relevant results for identifying and analyzing the Cartesian coordinates of key compounds in the FCC process, th= us generating information on how to optimize efficiency and the quality of the final product. The aim is to contribute to the existing knowledge in the fi= eld of crude oil refining by presenting a detailed analysis of the Cartesian coordinates of key compounds in the FCC process.

Methodolo= gy

Chemical Compound Identification

Through bibliographic research, typical chemical components found in the feed strea= ms to the FCC process have been identified. According to the blend of light and heavy gas oil known as Vacuum Gas Oil (VGO), it is used as the feed stream = to FCC catalytic plants to produce high-octane gasoline. Additionally, the feed includes paraffins, olefins, aromatics, and naphthenes, each of which is fe= d to its corresponding E-Cat D catalyst at the appropriate temperature. (Sadeghbeigi, 2020) . <= /span>

The raw materials for feeding the FCC unit for vacuum gas oil and atmospheric resid= ue consist of sulfur, nitrogen, nickel, and vanadium residues, each in differe= nt weight percentages. The feed stream to the FCC process, Vacuum Gas Oil B (VGB-B), with E-Cat D catalyst at a temperature of 500°C, includes paraffins ranging from methane (CH4) to dodecane (C12H26), iso-butane, iso-= pentane, iso-hexane, iso-heptane, iso-octane, iso-nonane, and iso-decane, as well as paraffins from carbon 20-27 in various weight percentages. Olefins range fr= om ethylene (C2H4) to decene (C10H20). Aromatics such as benzene, toluene, and C8H8 aromatics are present, each in different weight percentages. For the f= eeds of FCC for Light Cycle Oil (LCO) and VGO, benzene, decalin, tetralin, naphthalene, 1-phenyloctane, biphenyl, fluorene, 9,10-dihydrophenanthrene, phenanthrene, pyrene, and benzo(a)anthracene are present, all ranging betwe= en 98 and 99% in weight (Chiluisa Cando, 2021) (Nazarova y otros, 2022).

Similarly, naphthenes with feed positions to the FCC unit include cyclopentane, cyclohexene, 1,1,2-trimethylcyclopentane, phenanthrene, cyclopentyl methyl ether, cyclohexane, and methylcyclohexane (Stratiev y otros, 2023). Additionally,= the author (Chiluisa Cando, 2021) also reports bromobenzene, fluorobenzene, methylbenzene, nitrobenzene, hydroxybenzene, vinylbenzene, tert-butylbenzene, 1,2-dichlorobenzene, 1-Bromo-3-nitrobenzen= e, 1-ethyl-4-isopropylbenzene, 1-ethyl-2-methylbenzene, 1-ethyl-3-methylbenzen= e, 1,4-dimethylbenzene, 1-bromo-2,3-dimethylbenzene, and 1,2,4-trinitrobenzene= .

Computational chemistry informati= on processing

Through computational chemistry data processing, each molecule was processed to individually obtain the 3D coordinates of the chemical compounds. Initially, these coordinates are unoptimized. This was achieved using Avogadro, which = is an advanced molecule editor and visualizer software designed for multi-plat= form use in computational chemistry (Avogadro, 2024).

Initially, the generation of chemical structures in 2D was carried out using (ChemDraw, 2024) , which in computational chemistry is known for its efficiency, precision, and aesthet= ic appeal in chemical drawing. This program was chosen because it reduces erro= rs in drawing, analyzing, and documenting the complex chemical structures presented in this study. To generate a chemical structure in 2D, the follow= ing steps were followed: using "Convert Name to Structure" within the Structure menu of ChemDraw, the name of the chemical compound in English was entered. As a result, the molecule was generated in line-angle format along with its English name. Once saved from ChemDraw in .mol format, the document was then opened in the Avogadro software.<= o:p>

&nbs= p;

&nbs= p;

&nbs= p;

&nbs= p;

&nbs= p;

&nbs= p;

&nbs= p;

Figure = 1

Processing of styrene molecule in computational ch= emistry.

(Avogadro, 2024) It is an open-= source and free-to-use software that allows for the creation and visualization of molecular structures in 3D. Since this study was conducted using computatio= nal chemistry, this program proved to be a useful tool. Additionally, it enables the visualization of chemical structures from any angle. To obtain the Cartesian coordinates of each chemical compound, the Gaussian extension tool was selected from the Avogadro toolbar. This provided the unoptimized Carte= sian coordinates (3D) of the analyzed chemical compound.

= Figure 2

<= span lang=3DEN-US style=3D'font-size:12.0pt;line-height:115%;font-family:"Times = New Roman",serif; mso-ansi-language:EN-US'>Non-optimised 3D octane coordinates

Why should the molecule be optimised?=

Gaussian calculations have various applications, including single-point energy calculations, optimization, and stability, among others (Chaurand Padilla y otros, 2022). Since this study will focus on molecule optimization, it delves into said optimization prior to obtaining their Cartesian coordinates. The importance lies in the fact that molecules are not always in the most stable geometry,= so it is crucial to ensure that their spatial arrangement is accurate and repr= esents the real molecular geometry (Paniagua & Mota, 2008) . On the other= hand (San Fabián, 2023) mentions that molecule optimization involves adjusting the positions of ato= ms to minimize the total energy of the molecule, ensuring that the geometry is= the most stable and representative of the analyzed chemical compound.

The equilibrium geometry is the configuration in which the molecule's energy is minimized, and this is considered the real geometry of the molecule. Without optimizing the molecule, the Cartesian coordinates may not reflect the real geometry, leading to errors in the calculations and subsequent analysis of = the analyzed chemical compound   (Chaurand Padilla y otros, 2022) (= San Fabián, 2023) (Paniagua & Mota, 2= 008). Therefore, in= the Avogadro toolbar, the AutoOptimization Setting was selected, and the force field was chosen to initiate this calculation, which may take seconds or a = few minutes depending on the molecule being analyzed. For instance, in the case= of cyclopentane, this analysis took several minutes to complete.

Why the choice of force field?

The choic= e of a force field for energy optimization in molecules is crucial in computatio= nal chemistry, considering that force fields are mathematical models that descr= ibe the forces and energy interactions between atoms and molecules in a system = (Grabowski, 2020). Furthermore <= w:Sdt Citation=3D"t" ID=3D"1196735210">(Jorgensen & Tirado-Rives, 2005), selecting the appropriate force field ensures accuracy and realism in representing molecu= lar interactions, optimizes molecular geometry, enables realistic dynamic simulations, facilitates the calculation of energetic and thermodynamic properties, and effectively handles the complexity of large systems ( Vangunsteren & Berendsen, 1990).

What is t= he force field for hydrocarbon analysis??

he choice= of the appropriate force field depends on the chemical nature of the molecule being optimized. For hydrocarbon analysis, the Merck Molecular Force Field (MMFF94) is commonly used (Avogadro, 2024), as this model optimizes the geomet= ry of organic compound molecules. The MMFF94 has been specifically parameteriz= ed for alkanes, alkenes, alcohols, phenols, ethers, aldehydes, ketones, etc. Although the Universal Force Field (UFF) reproduces the most important structural characteristics across the periodic table and optimizes geometry= for all elements, including inorganic and organometallic materials. In this stu= dy, the MMFF94 was utilized for the analysis of the mentioned hydrocarbons.

Furthermo= re, the General AMBER Force Field (GAFF) model is employed for optimizing the geometries of drugs, while Assisted Model Building with Energy Refinement (AMBER) is used for protein analysis. (Avogadro, 2024).

Figure = 3

Optimized Styrene Spatial Arrangement

RESULTS

For the paraffins fed into the FCC process, the coordinates of isobutane and octane= are presented, respectively.

Table = 1

Optimized molecule of isobutane and octane

Optimised isobutane molec= ule

       &n= bsp; Atom     Axis X       &n= bsp;   Axis Y       &n= bsp;   Axis Z    &= nbsp;

C       &n= bsp; 0.88820        0.00508    =     0.02995

C       &n= bsp; 2.41498       -0.00393        0.00094

C       &n= bsp; 2.93829       -1.40974       -0.28513

C       &n= bsp; 2.93813        0.98548    =    -1.03797

Table 1

Optimized molecule of isobutane and octane (continuation)

Optimised isobutane molecul= e

       &nbs= p; Atom     Axis X       &nbs= p;   Axis Y       &nbs= p;   Axis Z    &nb= sp;

H       &nbs= p; 0.51003       -0.68214        0.79416

H       &nbs= p; 0.46936       -0.29844       -0= .93561

H       &nbs= p; 0.50991        1.00590        0.26358

H       &nbs= p; 2.78062        0.30618    &n= bsp;   0.98749

H       &nbs= p; 2.60076       -1.76937       -1= .26318

H       &nbs= p; 2.58859       -2.11661        0.47471

H       &nbs= p; 4.03328       -1.42567       -0= .27793

H       &nbs= p; 2.60059    &n= bsp;   0.72086       -2= .04589

H       &nbs= p; 4.03312        1.00281    &n= bsp; -1.04123

H       &nbs= p; 2.58831        1.99991    &n= bsp; -0.81917

Optimized Octane Molecule

= Atom       &nbs= p;

= Axis x

= Axis y

= Axis z

C       &nbs= p; 0,84430       -0,00360        0,02940

C       &nbs= p; 2,36450        0,01060        0,01190

C       &nbs= p; 2,90350        0,91290    &n= bsp; -1,09790

C       &nbs= p; 4,43290        0,92440    &n= bsp; -1,11200

C       &nbs= p; 4,97240        1,82730    &n= bsp; -2,22260

C       &nbs= p; 6,50180        1,83890    &n= bsp; -2,23670

C       &nbs= p; 7,04080        2,74110       -3= ,34660

C       &nbs= p; 8,56100        2,75540    &n= bsp; -3,36400

H       &nbs= p; 0,48060       -0,65480        0,83030

H       &nbs= p; 0,44460       -0,37600       -0= ,91920

H       &nbs= p; 0,44450        1,00100    &n= bsp;   0,20030

H       &nbs= p; 2,73210       -1,01260       -0,12840<= /o:p>

H       &nbs= p; 2,73200        0,35690    &n= bsp;   0,98490

Table 1

Optimized molecule of isobutane and octane (continuation)

Optimized Octane Molecule

= Atom       &nbs= p;

= Axis x

= Axis y

= Axis z

H       &nbs= p; 2,52960        1,93410    &n= bsp; -0,95520

H       &nbs= p; 2,52960        0,56470       -2= ,06850

H       &nbs= p; 4,80660       -0,09680       -1= ,25490

H       &nbs= p; 4,80660        1,27280    &n= bsp; -0,14140

H       &nbs= p; 4,59860        2,84860    &n= bsp; -2,07980

H       &nbs= p; 4,59870        1,47900    &n= bsp; -3,19320

H       &nbs= p; 6,87580    =     0,81770       -2= ,37940

H       &nbs= p; 6,87570        2,18710    &n= bsp; -1,26620

H       &nbs= p; 6,67320        3,76430    &n= bsp; -3,20630

H       &nbs= p; 6,67330        2,39480    &n= bsp; -4,31960

H       &nbs= p; 8,96070        3,12790    &n= bsp; -2,41540

H       &nbs= p; 8,92480        3,40650       -4= ,16500

H       &nbs= p; 8,96080        1,75080    &n= bsp; -3,53480

From t= he olefin stream feeding into the catalytic cracking process, propylene has be= en selected.

Table = 2

Cartesian coordinates of propylene.

= Molécula optimizada de propeno

Atom

Axis x

Axis y

Axis z

C       &nbs= p; 0,98040        0,01740    &n= bsp;   0,10550

C       &nbs= p; 2,47390        0,01880    &n= bsp;   0,11420

C       &nbs= p; 3,20030        0,20230    &n= bsp;   1,22710

C       &nbs= p; 4,69390        0,20760    &n= bsp;   1,25910

H       &nbs= p; 0,61420       -0= ,15040       -0= ,91250

H       &nbs= p; 0,58670        0,97920    &n= bsp;   0,44800

Table 2

Cartesian coordinates of propylene (continuaction)=

Molécula optimiza= da de propeno

Atom<= /span>

Axis x

Axis y

Axis z

H       &nbs= p; 0,58670       -0,78360        0,73860

H       &nbs= p; 2,97070       = -0,13860       -0= ,84040

H       &nbs= p; 2,69680        0,35920    &n= bsp;   2,17880

H       &nbs= p; 5,12990        0,04430     <= /span>   0,26850

H       &nbs= p; 5,05490       -0,58170        1,92550

H       &nbs= p; 5,05490        1,16910    &n= bsp;   1,63680

Represent= ative of aromatic compounds is presented in the Cartesian coordinates of benzene = and styrene.

Table 3

3D Cartesian coordinates of benzene and styrene

= Optimised molecule

= Benzene

= Styrene

Atom

Axis x

Axis y

Axis z

Atom

Axis x

Axis y

Axis z

C       &nbs= p; -0.76191       1.17879     -0.00445

C       &nbs= p; -1.39352    &= nbsp; -0.06483     -0.00378

C       &nbs= p; -0.63232       -1.23364   -0.00582<= /p>
C       &nbs= p; 0.76049    &n= bsp; -1.15882     -0.00852

C       &nbs= p; 1.39211        0.08480    -0.00919

C       &nbs= p; 0.63091        1.25361    -0.00716

H       &nbs= p; -1.35497        2.08942    -0.00287

H       &nbs= p; -2.47868       -0.12313    -0.00168=

H       &nbs= p; -1.12442       -2.20257    -0.00529

H       &nbs= p; 1.35356       -2.06946    -0.01011

C       &nbs= p; 1.07396       -0.39151    &= nbsp; 0.14829

C       &nbs= p; 2.17773        0.27468     <= /span>-0.21451

C       &nbs= p; 2.89018        0.08040     -1.48472

C       &nbs= p; 3.53569        1.17648     -2.07260

C       &nbs= p; 4.24873        1.02908     -3.26537

C       &nbs= p; 4.33389       -0.21939    -3.87721

C       &nbs= p; 3.71074       -1.32081    -3.29475

C       &nbs= p; 2.99694       -1.17280    -2.10221

H       &nbs= p; 0.59741       -0.18466     1.10148

H       &nbs= p; 0.60680       -1.13713    -0.48637

Table 3

3D Cartesian coordinates of benzene and styrene (c= ontinuation)

= Optimised molecule

= Benzene

= Styrene

Atom

Axis x

Axis y

Axis z

Atom

Axis x

Axis y

Axis z

H       &nbs= p; 2.47727        0.14309     -0.01130

H       &nbs= p; 1.12301        2.22253     -0.00768

H       &nbs= p; 2.58357       1.03246    &n= bsp; 0.45192

H       &nbs= p; 3.48122        2.15971    -1.61084

H       &nbs= p; 4.73772        1.88947    = -3.71450

H       &nbs= p; 4.88933       -0.33505    -4.80411

H       &nbs= p; 3.78350       -2.29819    -3.76442

H       &nbs= p; 2.53569       -2.05050    -1.65687

Cyclopentane has been chosen for the naphthenes

= Table 4

<= span lang=3DEN-US style=3D'font-size:12.0pt;line-height:115%;font-family:"Times = New Roman",serif; mso-ansi-language:EN-US'>Optimised coordinates of the optimised cyclopentane molecule

Molécula optimizada de ciclopentano=

= Atom       &nbs= p;

= Axis x

= Axis y

= Axis z

C       &nbs= p; -0.79462       -0.99232       -0= .13821

C       &nbs= p; 0.72550       -1.05298        0.00185

C       &nbs= p; 1.20335        0.39253    &n= bsp;   0.12994

C          0.0= 0632        1.24828       -0= .25510

C       &nbs= p; -1.17664        0.43179        0.23498

H       &nbs= p; -1.29800       -1.73278        0.49065

H       &nbs= p; -1.07619       -1.19392       -1= .17871

H       &nbs= p; 1.17051       -1.54138       -0= .87185

H       &nbs= p; 1.01893       -1.63169        0.88443

H       &nbs= p; 2.07563        0.59714    &n= bsp; -0.49812

H       &nbs= p; 1.48803        0.59476    &n= bsp;   1.16947

H       &nbs= p; -0.03909        1.36684       -1= .34416

H       &nbs= p; 0.04387        2.24343    &n= bsp;   0.19695

H       &nbs= p; -2.11907        0.74349       -0= .22408

H       &nbs= p; -1.27706        0.52371        1.32293

The raw materials for the feed to the FCC unit for vacuum gas oil and atmospheric residue are presented in 3D coordinates of nitrogen and nickel and vanadium= .

= Table 5

<= span lang=3DEN-US style=3D'font-size:12.0pt;line-height:115%;font-family:"Times = New Roman",serif; mso-ansi-language:EN-US'>3D coordinates of nitrogen, nickel and vanadium.

Optimised nitrogen molecule=

= Atom       &nbs= p;

= Axis x

= Axis y

= Axis z

N       &nbs= p; 0,86180       -0,03060        0,08760

N       &nbs= p; 2,28180       -0,03060        0,08760

Optimised nickel molecule

= Atom

= Axis x

= Axis y

= Axis z

Ni       &nbs= p; 0.96824       -0.02527       -0= .09139N

Optimised vanadium molecule=

= Atom       &nbs= p;

= Axis x

= Axis y

= Axis z

V       &nbs= p; 0.97576        0.08384    &n= bsp; -0.04882

Analysis and discussion

Due to the FCC process with VGO-B and E-Cat D catalyst, the key compounds are paraffin= s (C₁-C₁₂), olefins (C₂-C₁₀), aromatics such as benzene, toluene, styrene, naphthenes such as cyclopentane, cyclohexene, as well as heavy compounds such as sulphides, nitrogen, nickel and vanadium. They have been considered as the most relevant due to their reactivity, coke formation, influence on catalyst selection and final product quality. (Nazarova y otros, 2022) (Stratiev y otros, 2023).

In crude = oil refining processes, understanding the Cartesian coordinates of molecules ca= n be important in several respects due to the complexity and accuracy needed to optimise processes and maximise efficiency. In molecular dynamics simulatio= ns, foresee how molecules interact with each other. Cartesian coordinates allow modelling and visualisation of these motions and collisions at the atomic level. On the other hand, coordinates provide knowledge to understand how molecules are transformed during catalytic processes, which would facilitate simulations of the chemical reactions that occur during crude oil refining = (Zhang y otros, 2020).

The FCC u= nit operates by passing a zeolite catalyst along with feed vapor through a vert= ical reactor for a few seconds. The cracking products are separated from the sol= ids and directed to a distillation column to be divided into 25 desired product= s. The catalyst is recirculated to the regenerator, where accumulated coke is burned off, and the catalyst is regenerated for reuse (Fahim y otros, 2009). Therefore, understanding the positions of atoms in catalysts and reactants can help de= sign more efficient catalysts. Similarly, analyzing how crude oil molecules inte= ract with catalysts at the atomic level would be useful for improving reaction r= ates and selectivity (Zhang y otros, 2020).

Cartesian coordinates enable a precise representation of how molecules are distributed and move, along with their corresponding optimization energy for process optimization. This could identify optimal operating conditions (temperature, pressure, etc.) by better understanding molecular interactions and movement= s (Zhang y otros, 2020). Therefore, understanding molecular geometry helps predict and control separation behaviors, as it improves purification and separation techniques through detailed analysis of the involved molecules (Borges y otros, 2007) Furthermore, real-time monitoring could be enhanced using precise molecular data to adju= st process parameters and maintain product quality (Yan & Duan, 2022).

Conclusiones

·         The optimization of molecular geometry using the appropriate force field is a crucial step before obtaining Cartesian coordinates, as it ensures that the= se coordinates accurately represent the real molecular structure and minimize = the molecule's energy. Moreover, a well-chosen force field enhances the reliabi= lity and validity of results obtained in computational studies.

·         Utilizing Cartesian coordinates in molecular dynamics simulations allows for the prediction and visualization of how molecules interact at the atomic level = in a realistic manner. Consequently, they can be employed to understand molecular transformations during catalytic processes in crude oil refining, such as F= CC, thereby facilitating the simulation of chemical reactions and process optimization.

·         Having a precise understanding of atomic positions in catalysts and reactants thro= ugh molecular geometry aids in designing more efficient catalysts. In the case = of FCC, the interaction between the feedstock and catalysts is crucial for pro= cess efficiency. Analyzing interactions between crude oil molecules and catalyst= s at the atomic level can enhance reaction rates and selectivity, resulting in a more effective fluid catalytic cracking process.

·         Lastly, delving into computational chemistry allows for detailed knowledge of molec= ular geometry and motion, facilitated by Cartesian coordinates. This leads to identifying optimal operating conditions to improve purification and separa= tion techniques, thus optimizing refining processes. Additionally, real-time monitoring using precise molecular data enables the adjustment of process parameters to maintain the quality of refined products.
Conflict of interests

There is = no conflict of interest in relation to the article presented.

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El artículo que se publica es de exclu= siva responsabilidad de los autores y no necesariamente reflejan el pensamiento = de la Revista Ciencia Digital.

= ;

El artículo queda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autorizado por el director de la Revista Ciencia Digital.

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= Ciencia & Genética &= nbsp; Pági= na 47 | 63

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	Artículo de Investigación Científica y Tecnológica Enviado: 15/04/2024 Revisado: 10/05/2024 Aceptado: 10/06/2024 Publicado:05/07/2024 DO= I: https://doi.org/10.33262/= cienciadigital.v8i3.3079 <= span style=3D'mso-spacerun:yes'>

Cítese:		Través Osorio, S. E., Nacimba Rivera, = N. O., Robalino Cacuango, M. J., & Moreno Corrales, A. S. (2024). Optimización molecular en el proceso de fluid catalytic cracking: análisis de coordenadas cartesianas para mejora de eficiencia y ca= lidad en la refinación de crudo. Ciencia Digital, 8(3), 47-63. https://doi.org/10.33262/cienciadigital.v8i3.3079=
		CIENCIA DIGITAL, es una revista multidisciplinaria, trimestral, que se publicará en soporte electrónico tiene como misión contribuir a la formación de profesionales competentes con visión humanística y crítica que sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos en la sociedad. https://cienciadigita= l.org = La revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registrada en la Cámara Ecuatorian= a de Libro con No de Afiliación 663) www.celibro.org.ec
		Esta revista est&aacut= e; protegida bajo una licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 International. Copia de = la licencia: https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es<= /span>
Palabras claves: formación vocac= ional, orientación profesional, carreras técnicas, estudiantes de media superior.		Resum= en Introducción: En el refinado de petróleo crudo, el proceso de craqueo catalítico fluido (FCC) convierte el petróleo crudo en productos petroquímicos de alta calidad. Comprender las interacciones moleculares en FCC es crucial para fines de optimización, eficiencia y calidad. Este estudio cuantitativo y descriptivo analiza las coordenadas cartesianas de compuestos clave, empleando química computacional para este propósito. Met= odología: Cuantitativa y descriptiva. A través de una revisión de la literatura, se identificaron compuestos químicos típicos que alimentan el proceso de FCC, incluidas parafinas, olefinas, aromát= icos y naftenos, entre otros. Estos compuestos se procesaron mediante química computacional para obtener sus coordenadas 3D, optimizando= su geometría molecular para representar la estructura real, garantiza= ndo una precisión confiable de los datos en simulaciones y anál= isis posteriores. Análisis y discusión de resultados: Las coordenadas cartesianas ayudan a comprender e identificar las condiciones operativas óptimas, mejorando la comprensión de las interacciones moleculares en tiempo real y facilitando la predicció= ;n de comportamientos de separación. Estas coordenadas están previstas para optimizar los procesos de refino de crudo en FCC, mediante= la modelización y visualización de movimientos y colisiones a nivel atómico. Conclusiones: La optimización de la geometría molecular utilizando el campo de fuerza apropiado es cru= cial para obtener coordenadas cartesianas precisas. Estas coordenadas permiten= la simulación de interacciones moleculares a nivel atómico, el diseño de catalizadores más eficientes y la optimizaci&oacu= te;n de procesos de refinado. Además, el monitoreo en tiempo real con d= atos moleculares precisos podría garantizar una calidad constante del producto en FCC.
Keywords: = Cartesian coordinates, Fluid Catalytic Cracking (FCC), refinery, computational chemistry, optimization energy. <= /o:p>		AbstractIntroduction: In crude oil refining, the Fluid Catalytic Cracking (FCC) process converts crude o= il into high-quality petrochemical products. Understanding molecular interactions in FCC is crucial for optimization, efficiency, and quality purposes. This quantitative and descriptive study analyzes Cartesian coordinates of key compounds, employing computational chemistry for this purpose. Methodology: Quantitative and descriptive. Through a literature review, typical chemical compounds feeding into the FCC process were identified, including paraffins, olefins, aromatics, and naphthenes among others. These compounds were processed using computational chemistry to obtain their 3D coordinates, optimizing their molecular geometry to repre= sent the real structure, ensuring reliable data accuracy in subsequent simulat= ions and analysis. Analysis and Discussion of Results: Cartesian coordinates a= id in understanding and identifying optimal operating conditions, enhancing = the comprehension of molecular interactions in real time and facilitating the prediction of separation behaviors. These coordinates are envisaged to optimize crude oil refining processes in FCC, through modeling and visualization of atomic-level movements and collisions. Conclusions: Optimizing molecular geometry using the appropriate force field is crucial for obtaining precise Cartesian coordinates. These coordinates enable the simulation of molecular interactions at the atomic level, design of more efficient catalysts, and optimization of refining processes. Additionally, real-time monitoring with accurate molecular data could ensure consistent product quality in FCC.

= Optimised molecule
= Benzene	= Styrene
Atom	Axis x	Axis y	Axis z	Atom	Axis x	Axis y	Axis z
C &nbs= p; -0.76191 1.17879 -0.00445 C &nbs= p; -1.39352 &= nbsp; -0.06483 -0.00378 C &nbs= p; -0.63232 -1.23364 -0.00582<= /p> C &nbs= p; 0.76049 &n= bsp; -1.15882 -0.00852 C &nbs= p; 1.39211 0.08480 -0.00919 C &nbs= p; 0.63091 1.25361 -0.00716 H &nbs= p; -1.35497 2.08942 -0.00287 H &nbs= p; -2.47868 -0.12313 -0.00168= H &nbs= p; -1.12442 -2.20257 -0.00529 H &nbs= p; 1.35356 -2.06946 -0.01011	C &nbs= p; 1.07396 -0.39151 &= nbsp; 0.14829 C &nbs= p; 2.17773 0.27468 <= /span>-0.21451 C &nbs= p; 2.89018 0.08040 -1.48472 C &nbs= p; 3.53569 1.17648 -2.07260 C &nbs= p; 4.24873 1.02908 -3.26537 C &nbs= p; 4.33389 -0.21939 -3.87721 C &nbs= p; 3.71074 -1.32081 -3.29475 C &nbs= p; 2.99694 -1.17280 -2.10221 H &nbs= p; 0.59741 -0.18466 1.10148 H &nbs= p; 0.60680 -1.13713 -0.48637

Optimised nitrogen molecule=
= Atom &nbs= p;	= Axis x	= Axis y	= Axis z
N &nbs= p; 0,86180 -0,03060 0,08760 N &nbs= p; 2,28180 -0,03060 0,08760
Optimised nickel molecule
= Atom	= Axis x	= Axis y	= Axis z
Ni &nbs= p; 0.96824 -0.02527 -0= .09139N
Optimised vanadium molecule=
= Atom &nbs= p;	= Axis x	= Axis y	= Axis z
V &nbs= p; 0.97576 0.08384 &n= bsp; -0.04882