Evaluación de riesgos en la gesti= ón de trasvase de combustibles. Una revisión sistemática de literatura<= b>

Risk assessment in fuel backpass management. A systematic literature review

= ¹

Fernando Alberto Sáenz Olaya

https://orcid.org/0009-0006-9087-4220

Universidad Católi= ca de Cuenca, Cuenca, Ecuador.

fernando.saenz.70@est.ucacue.edu.ec<= span style=3D'font-size:10.0pt;font-family:"Times New Roman",serif;mso-fareast= -font-family: "Times New Roman";color:black'>

= ²

Diego Marcelo Cordero Guzman =

https://orcid.org/0000-0003-2138-2522

Universidad Católica de Cuenca, Cuenca, Ecuador. =

dcordero@ucacue.edu.ec

= ³

Daniel Jacobo Andrade Pesantez

https://orcid.org/0000-0003-0586-4038

Universidad Católica de Cuenca, Cuenca, Ecuador.

dandradep@ucacue.edu.ec<= /o:p>

=

Artículo de Investigación Científica y Tecnológica

Enviado: 16/03/2024

Revisado: 14/04/2024

Aceptado: 08/05/2024

Publicado:05/06/2024

DOI: https://doi.org/10.33262/= ap.v6i2.3.491

<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",= serif; mso-fareast-font-family:Calibri;color:blue'>

Cítese:

Sáenz Olaya, F. A., Cordero Guzman , D. M., & Andrade Pesant= ez, D. J. (2024). Evaluación de riesgos en la gestión de trasvase de combustibles. Una revisión sistemática de literatura. AlfaPublicaciones, 6(2.3), 30–51. https:/= /doi.org/10.33262/ap.v6i2.3.491

ALFA PUBLICACIONES, es una revista multidisciplinar, trimestral, que se public= ará en soporte electrónico tiene como misión contribuir a la &nbs= p; formación de profesionales competentes con visión humanística y crítica q= ue sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos e= n la sociedad. https://alfapublicaciones.= com

La revista es editada por la Editorial Ciencia Digital (Editoria= l de prestigio registrada en la Cámara Ecuatoriana de Libro con No de Afiliaci= ón 663) www.celibro.org.ec

Esta revista está protegida bajo una licencia Creative Commons Attribution Non Commercial No Derivatives 4.0 International. Copia de la licencia: http://creativecommons.org= /licenses/by-nc-nd/4.0/

Pala= bras claves:=

Combustible, evaluación, inteligencia artificial riesgo, marítimo

Resumen

Introducción. El trasvase de combustible es una actividad cotidiana en la mayoría de los países. A pesar de que a nivel internacional las operaciones están estand= arizadas por protocolos para la realización de la mismas, la ocurrencia de riesgos= aún sigue siendo reportadas. Objetivo. El objetivo de la presente investigación es conocer los métodos cuantitativos más eficientes para la gestión de riesgos e identificar los riesgos que ocurren con mayor frecue= ncia durante la gestión de combustibles a nivel global; para lo cual es necesa= rio conocer los protocolos empleados para la identificación de riesgos durante la ges= tión de trasvase combustible. Metodología. Se realizó una revisión sistemática mediante la metodología PRISMA. La búsqueda se realizó en revistas digitales a través de bases de datos como Scopus, Scielo, Latind= ex, Google scholar y Redalyc de artículos sobre la evaluación de riesgos en la gestión de trasvase de combustibles mediante la estrategia PICO. Resul= tados. De 85 artículos revisados con re= lación a la evaluación de riesgos en la gestión de combustibles se seleccionaron= 24, los cuales abordan los riesgos que ocurren durante el trasvase de combustible, los protocolos para su identificación y los impactos económi= cos y ambientales generados. Conclusión. Los modelos matemáticos y el = uso de la inteligencia artificial para la evaluación de riesgos en la gestión= de trasvase de combustibles son herramientas que permiten la identificación y cuantificación de los riesgos de tal manera de cumplir los protocolos para minimizar el impacto económico, sanitario y ambiental por su ocurrencia.<= o:p>

Keywords:

Fuel, assessment, artificial intelligence risk, marine

Ab= stract

Introduction. Fuel backloa= ding is a daily activity in most countries Despite the fact that international= ly operations are standardized by protocols for the realization of the same,= the occurrence of risks still continues to be reported. Objective. The objective of knowing the most efficient quantitative methods for risk management, to identify the risks occurring most frequently during fuel management at global level, for which it is necessary to know the protoco= ls employed for risk identification during management of fuel and assess the impact of risks occurring during fuel backpass management and the importa= nce of compliance with international regulation to mitigate the same. Meth= odology. A systematic review is conducted using the PRISMA methodology. Based = on the search was conducted in digital journals through databases such as Scopus, Scielo, Latindex, Google scholar and Redalyc of articles on risk = evaluation in fuel backwash management through the PICO strategy. Results. Ou= t of 85 reviewed articles relating to risk assessment in fuel management 24 we= re selected, from which they address the risks occurring during fuel transfe= r, the protocols for their identification and the economic and environmental impacts

Conclusion. Mathematical models and the use of artificial intelligence for risk assessment in fuel backwash management are tools th= at allow the identification and quantification of risks in such a way as to = meet protocols to minimize the economic, health and environmental impact by its occurrence.

Introducción<= /b>

Aunque dentro de la gest= ión de riesgo existen protocolos para la operación de trasvase de combustibles entre barcos, la falta de difusión de los procedimientos de la transferenci= a de custodia, es quizás uno de los aspectos que ha incidido en que sigan ocurri= endo riesgos dentro de la operación, con el impacto económico y de seguridad que esto tiene (Arici et al., 2020). Esto se debe a la débil incorporación de normas en los procesos de inspección, lo cual requiere un proceso de vigila= ncia y control de las mismas, sin embargo a pesar de que se cumpla esto, la exposición al combustible durante esta operación es un factor altamente riesgoso (Aydin et al., 2022), por lo cual se debe monitorear la aparición = de los mismos, para lo cual es necesario desarrollar la tecnología apropiada p= ara ello, donde se destaca la falta de uso de la misma, lo cual se agrava con la poca planificación y la inexist= encia de guías al momento de realizar los procesos, para aplicar los protocolos de gestión de combustible, previamente establecidos.

Estos protocolos toman c= omo referencia los documentos normativos para cantidad: American Petroleum Institute (API) y Manual de Estándares de Medición de Petróleo (MPMS) y se basan en realizar mediciones de la carga antes y después de la descarga, pa= ra el cálculo de la misma usando equipos debidamente calibrados, cuyo objetivo= es asegurar que todas las actividades referentes a las inspecciones de cargas y descargas de petróleo crudo y sus derivados, sean confiables en relación a = los parámetros de calidad y cantidad establecidos y negociados por las partes involucradas en estas operaciones. Es decir, el operario marítimo, el vende= dor de combustible y el cliente, pueden evitar pérdidas con el consecuente impa= cto económico o accidentes que afecten la seguridad de los trabajadores y al me= dio ambiente.

El trasvase de combustib= le es una actividad cotidiana en la mayoría de los países, ya sea por su importación o para el traslado desde la refinería a los centros de utilizac= ión. Este trasvase ocurre generalmente por el traslado del combustible almacenad= o en los tanqueros petroleros a barcos más pequeńos, para lo cual se requiere un protocolo exigente de tal manera que se cumplan con las normas de seguridad, que minimicen las pérdidas de combustible (Kamal et al., 2021), así como los riegos de accidentes laborales para los trabajadores que están expuestos a = esta operación (Dos Santos et al., 2020).

A pesar de que a nivel internacional las operaciones están estandarizadas por protocolos para la realización de la mismas, la ocurrencia de riesgos aún sigue siendo reporta= das (Jing et al., 2023), las cuales pueden tener implicaciones económicas y de seguridad importantes, por lo tanto dentro de la gestión de combustible un aspecto de importancia es la identificación de los riesgos (Fan et al., 202= 0), para ello se han desarrollado herramientas basadas en modelos matemáticos (Virdi et al.,2021) e inteligencia artificial, como los algoritmos automatizados (Gürgen et al.,2023), cuyas aplicaciones son relevantes porque identificando los riesgos, se pueden tomar medidas para su ocurrencia y aju= star la gestión a los protocolos ya establecidos para el mejoramiento de las operaciones.

Para garantizar la segur= idad durante el trasvase de combustible, el factor de riesgo debe mantenerse en = un nivel aceptable. Por este motivo, la evaluación de riesgos desempeńa un pap= el fundamental en el aumento de la seguridad en la industria marítima (Akyuz y Celik, 2015). En particular, debido a que las cargas transportadas en camio= nes cisterna son inflamables, explosivas y contaminantes al ambiente (Wang et a= l, 2022), las actividades de los camiones cisterna como la manipulación de car= ga, el atraque y desatraque, la limpieza y el mantenimiento de tanques, la liberación de gases y la transferencia de carga conllevan grandes riesgos. Aunque la evaluación de riesgos es de suma importancia para las operaciones= de los petroleros, hasta la fecha se han realizado pocas investigaciones centr= adas en el tema

Dentro del campo de la industria marítima, existe una amplia gama de estudios sobre el tema de la evaluación de riesgos, como la colisión marítima (Chai et al., 2017), varada (Mazaheri et al., 2016), e incendio o explosión (Ahn et al., 2021), que pue= dan causar dańos graves, lesiones y contaminación ambiental. A pesar de que se = han estandarizados protocolos, inclusive en países con controles muy rigurosos = como los de la Unión Europea aún siguen produciéndose accidentes importantes. En este sentido en los últimos ańos, la Agencia de Seguridad Marítima (EMSA) informó de más de 3.200 incidentes al ańo que provocó casi 8.000 heridos, 7= 00 víctimas mortales, 65 incidentes de contaminación y 230 buques perdido (Vid= mar y Perkovič 2023).

Considerando lo expuesto anteriormente es necesario encontrar nuevos métodos para identificar, monitorear, predecir y luego mitigar estos riesgos, lo que contribuiría a reducir aún más la frecuencia de estos accidentes, destacando entre ellos l= os análisis cuantitativos, basados en modelos matemáticos y sistemas de inteli= gencia artificial para predecir la probabilidad de accidentes y sus consecuencias.=

En cuanto al uso de las aplicaciones tecnológicas para la gestión de riesgo de combustible, se han utilizado, diferentes técnicas como aplicación del análisis de riesgo de co= njuntos difusos al transporte marítimo, para analizar los riesgos de colisión de un barco durante la operación de trasvase de combustible (Kuzu, 2023). Otros autores como Yu et al., (2021), han reportado el uso de modelos de redes bayesiana basado en datos para la predicción de la ocurrencia de derrames de petróleo utilizando accidentes de buques tanque, cuya importancia está en la prevención de riesgos que amanecen la calidad de las aguas y de los ecosist= emas marinos.

Asimismo, se han realiza= dos análisis de riesgo cuantificado para derrames de petróleo durante la operac= ión de carga de petróleo crudo en un buque cisterna bajo un enfoque mejorado de= red bayesiana basado en el número, también con el propósito de mitigar el impac= to económico y ambiental. Otra de las herramientas usadas son los algoritmos de XGBoost y Random Forest (He et al., 2021), que permiten la construcción de mapas de riesgos potenciales resultantes, que proporcionan a los responsabl= es de la toma de decisiones e información útil para aplicar medidas de mitigac= ión de riesgos en las regiones con mayores necesidades.

La relación entre los accidentes y sus factores causales es muy compleja, lo que hace que la predicción sea inherentemente desafiante. Muchos han argumentado que el aprendizaje automático, no requieren supuestos a priori sobre la relación subyacente entre variables dependientes y variables exploratorias (Jin et a= l., 2019), por lo tanto, estos métodos son muy adecuados para tratar con relaci= ones complejas, no lineales y múltiples de conjuntos de datos dimensionales, que= a menudo son características inherentes a la seguridad marítima (Jing et al., 2023; Qiao et al., 2022).

El objetivo de conocer los métodos cuantitativos más eficiente para la gestión de riesgos, para identificar los riesgos que ocurren con mayor frecuencia durante la gestión de combustibles a nivel global, para lo cual = es necesario conocer los protocolos empleados para la identificación de riesgos durante la gestión de combustib= le y evaluar el impacto de los riesgos que ocurren durante la gestión de combust= ible y la importancia del cumplimiento de la normativa internacional para mitigar los mismos, de manera de reducir los incidentes que pongan en riesgo la seguridad de los trabajadores y que puedan impactar negativamente el ambien= te, en particular los ecosistemas marinos. La evaluación de riesgos en la gesti= ón de trasvase de combustibles mediante una revisión sistemática de literatura= estará enfocada en determinar el papel de la gestión de combustibles sobre la ocur= rencia de riesgos durante el proceso de trasvase de combustibles.

Metodología

Este estudio corresponde a la categoría de investigación secundaria, una revisión sistemática que utilizo un enfoque narrativo, y se guio por la declaración PRISMA que proporciona un marco para presentar de manera transparente los resultados de este estudio. Este proceso de investigación tiene tres fases = que son: la identificación de artículos mediante la búsqueda en las bases datos establecidas, el análisis para la exclusión de los artículos no pertinentes= y la selección de los artículos que serán descritos y analizados en la investigación.

Previo a la búsqueda de los artículos y al establecimiento de los criterios de inclusión se utilizó la metodología denominada PICO para construir la pregu= nta de investigación cuyo propósito es identificar cuáles son los riesgos que ocurren con mayor frecuencia durante la gestión de combustibles a nivel glo= bal y cuáles son los protocolos se emplean para la identificación de riesgos durante la gestión de combustible. A continuación, se describen los element= os de la pregunta:

P (problema) =3D protocolos basados en modelos matemáticos y aplicaciones de inteligencia artificial para la identificación de riesgos que ocurren en la gestión de combustible.

I (intervenciones) =3D identificación de los riesgos de explosiones, incendio= s, derrame de combustible que ocurren con mayor frecuencia en la gestión de combustible.

C (comparación) =3D descripción de los dańos que ocurren por la aparición de riesgos durante la gestión de combustible, expresado en el volumen de combustible perdido, impacto económico, número de personas afectadas y superficie de ecosistemas afectados en caso de considerar el impacto ambien= tal.

O (resultados) =3D observación de la aplicación de los protocolos de gestión = de la mitigación de riesgos durante la gestión de combustibles. A partir de estas consideraciones de la estrategia PICO se formuló la siguiente pregunta de investigación:

żCómo los protocolos basados en modelos matemáticos e inteligencia artificial permiten identificar los riesgos que ocurren con mayor frecuencia durante la gestión de combustibles a nivel global, de tal manera de minimizar su ocurrencia y los dańos económicos, al ambiente y las personas asociados a su ocurrencia?

Búsqueda de artículos

Las estrategias de búsqueda utilizadas en esta revisión sistemática se centraro= n en el uso de lenguaje controlado a través de los tesauros Maritime Navigation,= Risk, safety, Accident, Machine Learning, deep Learning, Logistic Regression, Support Vector Random Forest, Artificial Neu= ral Networks; incluyendo términos lingüísticos. Se utilizaron operadores booleanos como "AND" y "OR" para obtener los resultados, teniendo en cuenta los criterios de la literatura científica en inglés y espańol

Se consideraron trabajos que incluyeron texto con referencia al título del trabajo reseńado, cuyas publicaciones no sólo cumplieron con las condiciones del estudio, sino que además se realizaron dentro del plazo establecido. Durante la búsqueda de información se tuvo en cuenta la terminología, limitándo la búsqueda a los resultados esperados y artículos que permitan la identificación de los riesgos que ocurren con may= or frecuencia durante la gestión de combustibles a nivel global y que protocol= os se emplean para la identificación de riesgos durante la gestión de combusti= ble. Esto fue analizado en revistas digitales dentro de bases de datos como Scop= us, Scielo, Latindex, Google scholar y Redalyc. Los artículos relacionados con estos contenidos abarcaron desde enero de 2018 hasta marzo de 2024.

Criterios de inclusión

Se consideraron para su inclusión artículos que inclu= ían texto con referencia al título del artículo revisado. Estas publicaciones no sólo debian cumplir con las condiciones del estudio, sino que además debian haber sido realizadas dentro del plazo establecido desde el 2018 al 2024. Durante la búsqueda de información se tuvo en cuenta la terminología: navegación marítima, riesgo, seguridad, accidentes, aprendizaje automático, aprendizaje profundo, regresión logística, bosque aleatorio de vectores de soporte, Redes neuronales artificiales, restringiendo la búsqueda a resulta= dos esperados y resultados previos en artículos relacionados con identificación= de los riesgos que ocurren con mayor frecuencia durante la gestión de combusti= bles a nivel global y que protocolos se emplean para la identificación de riesgos durante la gestión de combustible.

Criterios de exclusión

No se tomaron en cuenta publicaciones que no responde= n a la pregunta PICO, ańo de publicación antes del 2018, duplicados, resúmenes, comunicaciones de congresos y aquellos que carecían de base científica y ba= ses de datos de referencia derivadas de fuentes no reconocidas, aquellos artícu= los relacionados con la ocurrencia de riesgos en la industria petrolera o marít= ima pero no relacionadas al trasvase de combustible.

Proceso de selección de estud= io

Para la selección de los artículos se consideraron aquellos que cumplian con los criterios de inclusión que se consideran en l= as palabras claves de búsqueda y que estuvieron presentes en el título, subtít= ulo y/o resumen del trabajo, las cuales fueron: navegación marítima riesgo, seguridad, Accidentes, Machine Learning, deep Learning, Logistic Regression, Support Vector Random Forest, redes neuronales y que fueran correspondiente= a los ańos 2018-2023.

La recopilación de la información se realizó de manera minuciosa para garantiz= ar la fiabilidad de la extracción de datos, resolver conflictos potenciales a través de la discusión y el consenso de literatura científica. El hallazgo = del estudio sobre identificación de los riesgos que ocurren con mayor frecuencia durante la gestión de combustibles a nivel global y que protocolos se emple= an para la identificación de riesgos durante la gestión de combustible ayudará= a presentar los resultados de forma coherente y transparente en el informe de evaluación final de acuerdo con las directrices PRISMA. Este proceso de recopilación de datos basado en PRISMA garantiza la precisión y la transparencia en la recopilación y el informe de datos importantes de estud= ios incluidos en revisiones sistemáticas integradas.

Análisis de la información

La investigación se desarrolló en dos etapas: la primera relacionada al proceso de selección de artículos para = la revisión sistemática, durante la cual se identificaron los artículos con información relevante para el desarrollo de esta investigación, y la etapa hermenéutica, que consiste en el análisis e interpretación de los hallazgos de los artículos seleccionados s= obre identificación de los riesgos que ocurren con mayor frecuencia durante la gestión de combustibles a nivel global y que protocolos se emplean p= ara la identificación de riesgos durante la gestión de combustible.

Durante la fase hermenéutica se sintetizo la información y se generaron comentarios basados en fundamentos teóricos. Se detallaron las diversas fuentes que per= mitieron recopilar información bibliográfica. Se explicaron las similitudes y diferencias entre diversos autores con características similares en sus tem= as de investigación los que serán tabulados en una hoja Excel para el correspondi= ente análisis, tomando en cuenta que cada artículo además de cumplir con los criterios de inclusión también cumple con el nivel de evidencia requerido q= ue la información analizada corresponde a la respuesta de la pregunta PICO establecida. Se utilizó el gestor de referencias bibliográficas y documento= s de investigación Zotero, para almacenar la información de trazabilidad de los artículos seleccionados, incluyendo autor/es, ańo de publicación, revista científica y tipo de diseńo.

Resultados

De los 85 artículos revisados con relación a la = evaluación de riesgos en la gestión de combustibles se seleccionaron 24, de los cuales= en los 6 primeros se abordan los riesgos que ocurren con mayor frecuencia dura= nte la gestión de combustibles a nivel global (Tabla 1).

Tabla 1

Riesgos que ocurren con mayor frecuencia durante la gestión de combustibles a nivel global.

Titulo

Autores y ano

Revista

Riesgos

Analyzing human error contributions to maritime environmental risk in oil/chemical tanker ship

Ayidin et al., 2021

Human and Ecological Risk Assessment: An International Journal,

Ambiental<= span lang=3DEN-US style=3D'font-size:10.0pt;font-family:"Times New Roman",seri= f; mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US'>

Study of maritime accidents with hazardous substances involved: comparison of H= NS and oil behaviours in marine environment.

Galieriková, et al., 2021

Transportation research proceding

QuímicoAssessing the risk of potential oil spills in the Arctic due to shipping. I

Afenyo et al., 2020

Maritime transport and regional sustainability

Ambiental<= span lang=3DEN-US style=3D'font-size:10.0pt;font-family:"Times New Roman",seri= f; mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US'>

Oil spill environmental risk assessment and mapping in coastal China using automatic identification system (AIS) data

Zhu et al., 2022

Sustainability

AmbientalProbability analysis and prevention of offshore oil and gas accidents: Fire as a cause and a consequence.

Brkić y Praks 2021

Fire

Incendio

Evaluation of the risk of pollution caused by ship operations through bow-tie-based fuzzy Bayesian network

Bayazit y Kaptan 2023

Journal of Cleaner Production=

Económico

Ayidin et al., (2021) destaca que los accidentes= de tanqueros ocurren por errores humanos, porque es importante identificar los mismos para minimizar los riesgos de contaminación por fugas químicas y mej= orar la seguridad operativa en los procesos de carga y descarga de productos petroleros.

De acuerdo a Galieriková, et al (2021), el incremento de los riesgos de accidentes es debido al continuo aumento del número de sustancias peligrosas transportadas, lo que ha llevado a que las autoridades competentes se centraran en la preparación y respuesta ante un = posible riesgo de liberación de dichas sustancias, cuyo riesgo se ha minimizado deb= ido a los altos estándares de seguridad.

Dado que los riesgos aun ocurren es importante su identificación, la cual se dificulta según Afenyo et al., (2020) por la limitación, variabilidad e incertidumbre de los datos, por lo que este autor plantea el uso de modelos matemáticos como el enfoque bayesiano para la simulación del escenario y que sean capaces de abordar los problemas de evaluación del riesgo de derrames de petróleo. En este mismo orden de ideas= Zhu et al., (2022) destacan que los sistemas automatizados son útiles para ayud= ar a los tomadores de decisiones a mapear el área de influencia de los derrames = de petróleo y adoptar estrategias importantes y contramedidas efectivas de ges= tión y conservación para accidentes de barcos en las zonas costeras.<= /span>

El uso de estos modelos para la identificación de riesgos ha sido efectivo como lo demuestran Brkić y Praks (2021), cuyos resultados de la prueba estadística basada en intervalos de confianza permi= ten describir las principales causas de los accidentes de petróleo y gas en alta mar, siendo el fuego una de las principales causas de accidentes entre las = 24 analizadas, cuya ocurrencia puede llevar a juicio de Bayazit y Kaptan (2023= ) procuran mejorar el alcance de la operación de respuesta, considerando que el nivel = de contaminación marina mediante el enfoque Bow-tie, el cual es una manera esquemática de describir y evaluar la ruta de un riesgo, desde las causas iniciadoras hasta las consecuencias finales. Resulta una combinación de dos técnicas diferent= es: un árbol de fallas analizando las causas de un evento (representado por el = nudo de una corbata de lazo) y un árbol de eventos analizando las consecuencias y como resultado, los procedimientos que provocan emisiones de gases en embudo son los más riesgosos, por lo que los profesionales de la gestión ambiental= y de seguridad en el transporte marítimo deberán tomar decisiones prácticas y estratégicas.

Los siguientes 6 artículos destacan Protocolos empleados basado en modelos matemáticos para la identificación de riesgos d= urante la gestión de combustible (Tabla 2), cuyos principales hallazgos se describ= en a continuación:

Tabla 2

Protocolos empleados basado en modelos matemáticos para la identifica= ción de riesgos durante la gestión de combustible.

Marine oil spill control based on discrete mathematical model

Liu 2020Journal of Coastal Research

Modelización matemática

A bi-objective mathematical model to deter= mine risk-based inspection programs.

Javid 2021

Modelización matemática

A quantified risk analysis for oil spill during crude oil loading operation on tanker ship under improved Z-number based Bayesian Network approachSezet et al., 2023

Marine Pollution Bulletin

Estadístico

Integrated optimization of marine oil spill response and liquid oily waste management using mathematical programming = and evolutionary metaheuristic techniques

Mohammadiun et al. 2024

Modelización matemática

Tabla 2

Protocolos empleados basado en modelos matemáticos para la identifica= ción de riesgos durante la gestión de combustible (continuación)

Protocolos

Marine insurance claims analysis using the Weibull and log-normal models: Compensation for oil spill pollution due to tanker accidents

Ching y Yip 2022

Maritime Transport Research

Modelo

Logarítmico

Modelling of discharges from Baltic Sea shipping.

Jalkanen et al 2021

,<= span style=3D'font-size:10.0pt;font-family:"Times New Roman",serif;mso-bidi-fo= nt-family: "Times New Roman";mso-bidi-theme-font:minor-bidi;mso-bidi-font-weight:bol= d'>

Modelización matemática

El primer protocolo considerado fue el analizado= por Liu (2020) basado en un módulo de visualización dinámica en tiempo real bas= ado en el algoritmo de partición triangular para realizar el control de derrame= s de petróleo, que muestran que el sistema podría monitorear eficazmente el derr= ame de petróleo en la superficie del mar, simular el proceso de movimiento del derrame de petróleo marino en condiciones ambientales complejas y tener una buena respuesta escalonada, para el control del mismo.

Por su parte Javid (2021) evaluó dos modelos uno multi objetivo y otro bi-objetivo, determinando de que, de estos dos algoritmos, el bi-objetivo fue más eficiente y se implementó para construir= 7 índices (impacto operativo, eficiencia, fallas, flexibilidad operativa, costo de mantenimiento, impactos ambientales y de seguridad) en dos categorías: núme= ro y tipo de inspección que permite la identificación de riesgos de derrames petroleros de manera oportuna, minimizando el dańo económico y ambiental.

Aunque Sezer et al., (2023) muestran que la probabilidad de ocurrencia de riesgo de derrame de petróleo en buques ciste= rna de crudo es de 2.90E-02 , por lo cual es muy poco probable que durante la operación de carga, es necesario desarrollar protocolos de identificación de los riesgos, con los que se espera que la tripulación de los buques, los inspectores de seguridad, los propietarios de buques y los gerentes de terminales en la toma de decisiones de gestión de riesgos, mejorando la seguridad operativa, tomando acciones de control y minimizando los derrames= de petróleo.

En este sentido, Mohammadiun et al (2024) seńalan que la implementación de la optimización multiobjetivo evolutiva conduce a = una reducción considerable en el tiempo de respuesta en alrededor del 13,6 %; d= el costo en 1,4 % y del volumen de petróleo derramado en 9,2 %, por lo que el sistema multiagente desarrollado puede facilitar la toma de decisiones en tareas complejas de respuesta a derrames de petróleo marino para maximizar = la eficiencia de la respuesta.

Mientras que Ching y Yip (2022) seńalan que al c= omparar los modelos de Weibull y el modelo log-normal, los cuales permiten mediante= el método de simulación Markov Chain Monte Carlo (MCMC), obtener las propiedad= es estadísticas de los derrames de petróleo en series temporales, encontraron = que el log normal era el que mejor se ajustaba según las estadísticas R-cuadrad= o y los errores cuadráticos medios.

Jalkanen et al (2021) considera además de determ= inar los factores asociados a las probabilidades de petrolero, recopilar y utili= zar datos disponibles sobre agua de sentina, aceite de bocina, agua de depurado= r, agua de lastre, pinturas anti incrustantes, desechos de alimentos, aguas ne= gras y aguas grises para evaluar la entrada de diversos factores estresantes de = los barcos al medio marino y que pueden aumentar los riesgos de accidentes.

Los próximos 6 artículos describen los protocolos empleados basado en inteligencia artificial para la identificación de riesg= os durante la gestión de combustible (Tabla 3), cuyos principales hallazgos se presentan a continuación:

Tabla 3

Protocolos empleados bas= ado en inteligencia artificial para la identificación de riesgos durante la ges= tión de combustible

Ti= tulo

Au= tores y Ańo

Re= vista

Pr= otocolos

Intelligent computational techniques in marine= oil spill management: A critical review

Mohammadiun, et al., 2021

Inteligencia artificial

An optimized metamodel for predicting damage and oil outflow in tanker collision accidents. =

Das et al. 2022,

,

MetamodeloOil spill detect= ion based on machine learning and deep learning: a review.

Huby et al., 2022

5th International Conference on Engineering Technology and its Applications

Machine learning

Optimization of daily operations in the marine industry using ant colony optimization (ACO)-An artificial intelligence (AI) approach

Sardar et al., 2023

=

Inteligencia artificial

Application of bayesian network in the maritime industry: omprehensive literature review=

Animah 2024Ocean Engineering

Redes neuronales

Ocean oil spill detection from SAR images base= d on multi-channel deep learning semantic segmentation

Hasimoto et a 2023l.,

Ma= rine Pollution Bulletin

De= ep learning

A pesar de las potencialidades de la inteligencia artificial para la identificación de derrames petroleros Mohammadiun, et al= ., (2021), reportan que los hallazgos reportados en la literatura para: detecc= ión, caracterización y monitoreo de derrames; y gestión de derrames y optimizaci= ón de la respuesta, muestran que se han identificado posibles lagunas en la aplicación de técnicas computacionales y se ha sugerido un marco holístico basado en un modelo computacional que sea más eficiente.<= /p>
Sin embargo Das (2022) en su investigación muest= ra que los resultados evidencian la sólida capacidad del mismo para proporcion= ar estimaciones precisas y computacionalmente eficientes de la extensión de los dańos y el volumen de salida de petróleo, dado que este modelo se puede utilizar en contextos de análisis de riesgos marítimos, particularmente en = la preparación estratégica y la gestión de respuesta a la contaminación y que puede vincularse a la respuesta operativa DSS para estimaciones rápidas y razonablemente precisas del tamańo de los derrames.

Por su parte Huby et al., (2022) explican especialmente el uso de métodos de machine Learning (ML) y Deep Learning (D= L) para abordar el problema a través de la presentación y el análisis usando e= stas herramientas para la detección de derrames de petróleo, destacando el uso potencial de estos mé= todos de inteligencia artificial, en este mismo orden de ideas Sardar et al., (20= 23) propusieron un algoritmo basado en= la optimización de colonias de hormigas (ACO) para diseńar y validar un conjun= to verificado de instrucciones para realizar cada tarea operativa diaria de ma= nera estandarizada. Este enfoque basado en IA puede optimizar el camino para tar= eas complejas, proporcionar instrucciones claras y secuenciales, mejorar la eficiencia y reducir la probabilidad de error humano al minimizar las preferencias personales y las suposiciones falsas.

Mientras que Animah et al., (2024) evaluaron el = uso de redes bayesiana (BN), cuyos hallazgos revelaron una tendencia creciente = en la aplicación de BN en la industria marítima en diversas áreas de evaluació= n de riesgos, análisis de errores humanos, estimación de confiabilidad e investigación de accidentes, por su parte Hasimoto et al., (2023) muestran = una mayor precisión de clasificación a nivel de píxeles cuando se utilizan 2 o 3 canales en M-DNN usando un método de Deep Learning, alcanzando una precisión del 98,56 %, lo que demuestra la efectividad del método basado en DNN para la clasificación de derrames de petról= eo a diferentes escala.

Los últimos 6 artículos describen el impacto de = los riesgos que ocurren durante la gestión de combustible y la importancia del cumplimiento de la normativa internacional para mitigar los mismos (Tabla 4= ), cuyos principales hallazgos se presentan a continuación

Tabla 4

Impacto de los riesgos q= ue ocurren durante la gestión de combustible y la importancia del cumplimiento= de la normativa internacional para mitigar los mismos

Titulo

Autores y Ańo

Revista

Impacto

An analysis of severity of oil spill caused by vessel accidents.

Cakir et al., 2021

Transportation

Ambiental

Consequence analy= ses of collision-damaged ships—damage stability, structural adequacy and oil spills

Kuznecovs et al., 2023

Ships and Offshore Structures

Económico

Oil spill damage: a collision scenario and financial liability estimations for the Northern Sea Route area

Bambulyak et al., 2020

Ship technology research

Económico

Estimating offshore exposure to oil spill impacts based on a statistical forecast model

Wang et al., 2020

Marine pollution bulletin,

Laboral

The impact of shipping accidents on marine environment in Albanian seas.

Sinanaj 2020

Journal of Shipping and Ocean Engineering

Ambiental

The impact of shipping 4.0 on controlling shipping accidents: A systematic literature review.

Sepehri et al. 2022,

Ocean engineering

Laboral

=

En primer lugar Cakir et al= ., (2021) evaluaron el uso de Árbol de decisión (DT) y redes bayesianas (BN) basadas en datos llamadas Tree Augmented Naive Bayes (TAN), encontrando que como resultado, que los factores que más contribuyeron a la gravedad del derrame de petróleo fueron el “tipo de accidente” y el “tipo de embarcación= ”, lo cual será una guía que ayudará a las autoridades y a los responsables de= la formulación de políticas a predecir la gravedad de los derrames de petróleo= y contribuirá al desarrollo de estrategias y contramedidas importantes para l= os accidentes de embarcaciones que provocan derrames de petróleo.

Por su parte Kuznecovs et a= l., (2023) al realizar un análisis de la resistencia e integridad estructural d= el buque averiado, simulaciones de deriva de derrames de petróleo y, una evaluación = de las posibles consecuencias, suponen que la colisión se produjo en una ruta marítima con una alta densidad de tráfico marítimo, por lo que Bambulyak et al., (2020) afirman que la aplicación de un modelo de simulación para escenarios seleccionados de colisión de petroleros con una evaluación de los posibles tamańos de derrames de petróleo como resultado del tiempo de desca= rga, la profundidad de penetración, la energía y la fuerza aplicada, son necesar= ios para generar escenarios probables de derrames de petróleo causados por la colisión de petroleros, y las responsabilidades financieras por dańos ambientales causados por el mismo.

En este orden de ideas Wang= et al., (2020) proponen el uso de modelos estadísticos para generar un mapa de riesgo de derrames de petróleo, lo cual es una herramienta poderosa para identificar áreas de alto riesgo y desarrollar planes de contingencia, considerando que el alcance del impacto y el grado de dańo varían entre las diferentes fuentes debido a las condiciones topográficas, hidrológicas y meteorológicas locales especiales, donde generalmente hay una alta intensid= ad de contaminación de fuente puntual y una amplia gama de fuentes lineales.

Este tipo de investigación = ha sido desarrollada por Sinanaj (2020) para cuantificar los efectos de los accidentes marítimos en el medio marino en los mares de Albania, mediante el manejo de la literatura relacionada con los accidentes marítimos y el medio marino. A pesar de las consecuencias económicas y ambientales reportadas previamente Sepehri et al., (2022) seńalan que los enfoques del transporte marítimo 4.0 abordan los riesgos crítico= s de accidentes y las brechas que aún existen, donde el uso de datos en la nube, Internet de las cosas y análisis de big data, desempeńan papeles cruciales en los desarrollos actuales de la indus= tria 4.0, que ayudara a la identificación de riesgos y mitigación de las consecuencias económicas y ambientales producto de accidentes durante el transporte, carga y descarga de petróleo en ambientes marinos.

Los hallazgos reportados en= las Tablas 1, 2,3, y 4 apuntan a 3 vertientes, en primer lugar que a pesar de q= ue existen norman internacionales para regular las actividades de trasvase de combustible, algunas investigaciones reportan la ocurrencia de incidentes durante el proceso como incendios, explosiones fugas de combustibles, las cuales están asociados a perdidas económicas, dańos ambientales y riesgos p= ara la seguridad laboral, incluso en países con altos estándares de seguridad en operaciones marítimas como en la Unión europea y el sudeste asiático (Bambulyak, y Ehlers, 2020)

En segundo lugar la prevale= ncia de la ocurrencia de estos riesgos, no solo apuntan a consecuencias económic= as, sino que el dańo ambiental también es extremadamente grave (Bayazit y Kaptan,2023) el cual puede estar asociado a procesos de contaminación de ag= uas, cuyas implicaciones a su vez se relacionan con riesgos de salud pública para los habitantes de zonas costeras, lo que obliga a una mayor severidad en el cumplimiento de las normas durante el trasvase, asimismo existe el riesgo de accidentes laborales, por que los trabajadores están expuesto a acciones de= alto riesgo (Galieriková et al., 2021)

Finalmente, un aspecto posi= tivo que surge durante la investigación= es que los protocolos que complementa l= as normas jurídicas y técnicas ya establecidas para el trasvase de combustible= y que están basadas en modelos matemáticos y en el uso de aplicaciones de inteligencia artificial (Huang et al., 2023) han contribuido de manera posi= tiva para la identificación temprana de los riesgos durante el trasvase de combustible, lo que permite tomar acciones que eviten la ocurrencia de los mismos y las afectaciones antes mencionadas sobre trabajadores ecosistemas = y la población aledańa a zonas marinas donde se efectúa el proceso de trasvase.<= o:p>

Conclusiones

·         Se identificaron los riesgos que ocurren con may= or frecuencia durante la gestión de combustibles a nivel global, los cuales es= tán relacionados a riesgos de colisiones, incendios, derrames, explosiones, los cuales siguen ocurriendo con una alta prevalencia a pesar de contar con nor= mas internacionales estandarizadas para la gestión de combustibles, durante el proceso de carga y descarga.

·         Los protocolos empleados para la identificación = de riesgos durante la gestión de trasvase de combustible basados en modelos matemáticos muestran una alta eficiencia para la identificación oportuna de riesgos, durante el trasvase de combustible, lo que ayuda a tomar las medid= as preventivas para evitar su ocurrencia y minimizar los dańos e4conmicos y ambientales

·         La gestión de trasvase combustible basados en inteligencia artificial (IA) como: machine learning, deep learning y mejora= n la eficiencia de losa procedimientos para la identificación oportuna y cuantificación de los riesgos que = puiden ocurrir potencialmente durante el trasvase de combustible, lo que ayuda a l= levar a cabo las acciones de prevención para evitar que ocurran los mismos, asi c= mo su severidad.

·         La evaluación del impacto de los riesgos que ocu= rren durante la gestión de combustible y la importancia del cumplimiento de la normativa internacional es clave para mitigar los mismos y su impacto negat= ivo, el cual se ha asociado con afectaciones de índole económico, de seguridad laboral y ambiental, cuyos futuros estudios dedeb estar orienb tados ademas= de la identificacion de los riesgos a la cuantificación de los mismos, lo cual debe ser aplicado no solo para el trasvase de comnbustible , sino de otros liquidos y gases potenciualmentes peligrosos.

Conflicto de intereses

Los autores declaran que no existe conflicto de intereses en relación con el artículo presentado.

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El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento de la Revi= sta Alfa Publicaciones.

&nb= sp;

El artículo queda en propiedad de la revista y, por tanto, su publicaci= ón parcial y/o total en otro medio tiene que ser autorizado por el director de= la Revista Alfa Publicaciones.<= /o:p>

<= /span>

<= /span>

<= /span>

=

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                                      Globalización        <= /span>                Página <= !--[if supportFields]>PAGE   \* MERGEFORMAT30 | 51

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= ¹	Fernando Alberto Sáenz Olaya	https://orcid.org/0009-0006-9087-4220
	Universidad Católi= ca de Cuenca, Cuenca, Ecuador. fernando.saenz.70@est.ucacue.edu.ec<= span style=3D'font-size:10.0pt;font-family:"Times New Roman",serif;mso-fareast= -font-family: "Times New Roman";color:black'>
= ²	Diego Marcelo Cordero Guzman =	https://orcid.org/0000-0003-2138-2522
	Universidad Católica de Cuenca, Cuenca, Ecuador. = dcordero@ucacue.edu.ec
= ³	Daniel Jacobo Andrade Pesantez	https://orcid.org/0000-0003-0586-4038
	Universidad Católica de Cuenca, Cuenca, Ecuador. dandradep@ucacue.edu.ec<= /o:p>

=	Artículo de Investigación Científica y Tecnológica Enviado: 16/03/2024 Revisado: 14/04/2024 Aceptado: 08/05/2024 Publicado:05/06/2024 DOI: https://doi.org/10.33262/= ap.v6i2.3.491
<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",= serif; mso-fareast-font-family:Calibri;color:blue'>
Cítese:		Sáenz Olaya, F. A., Cordero Guzman , D. M., & Andrade Pesant= ez, D. J. (2024). Evaluación de riesgos en la gestión de trasvase de combustibles. Una revisión sistemática de literatura. AlfaPublicaciones, 6(2.3), 30–51. https:/= /doi.org/10.33262/ap.v6i2.3.491
		ALFA PUBLICACIONES, es una revista multidisciplinar, trimestral, que se public= ará en soporte electrónico tiene como misión contribuir a la &nbs= p; formación de profesionales competentes con visión humanística y crítica q= ue sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos e= n la sociedad. https://alfapublicaciones.= com La revista es editada por la Editorial Ciencia Digital (Editoria= l de prestigio registrada en la Cámara Ecuatoriana de Libro con No de Afiliaci= ón 663) www.celibro.org.ec
		Esta revista está protegida bajo una licencia Creative Commons Attribution Non Commercial No Derivatives 4.0 International. Copia de la licencia: http://creativecommons.org= /licenses/by-nc-nd/4.0/
Pala= bras claves:= Combustible, evaluación, inteligencia artificial riesgo, marítimo		Resumen Introducción. El trasvase de combustible es una actividad cotidiana en la mayoría de los países. A pesar de que a nivel internacional las operaciones están estand= arizadas por protocolos para la realización de la mismas, la ocurrencia de riesgos= aún sigue siendo reportadas. Objetivo. El objetivo de la presente investigación es conocer los métodos cuantitativos más eficientes para la gestión de riesgos e identificar los riesgos que ocurren con mayor frecue= ncia durante la gestión de combustibles a nivel global; para lo cual es necesa= rio conocer los protocolos empleados para la identificación de riesgos durante la ges= tión de trasvase combustible. Metodología. Se realizó una revisión sistemática mediante la metodología PRISMA. La búsqueda se realizó en revistas digitales a través de bases de datos como Scopus, Scielo, Latind= ex, Google scholar y Redalyc de artículos sobre la evaluación de riesgos en la gestión de trasvase de combustibles mediante la estrategia PICO. Resul= tados. De 85 artículos revisados con re= lación a la evaluación de riesgos en la gestión de combustibles se seleccionaron= 24, los cuales abordan los riesgos que ocurren durante el trasvase de combustible, los protocolos para su identificación y los impactos económi= cos y ambientales generados. Conclusión. Los modelos matemáticos y el = uso de la inteligencia artificial para la evaluación de riesgos en la gestión= de trasvase de combustibles son herramientas que permiten la identificación y cuantificación de los riesgos de tal manera de cumplir los protocolos para minimizar el impacto económico, sanitario y ambiental por su ocurrencia.<= o:p>
Keywords: Fuel, assessment, artificial intelligence risk, marine		Ab= stract Introduction. Fuel backloa= ding is a daily activity in most countries Despite the fact that international= ly operations are standardized by protocols for the realization of the same,= the occurrence of risks still continues to be reported. Objective. The objective of knowing the most efficient quantitative methods for risk management, to identify the risks occurring most frequently during fuel management at global level, for which it is necessary to know the protoco= ls employed for risk identification during management of fuel and assess the impact of risks occurring during fuel backpass management and the importa= nce of compliance with international regulation to mitigate the same. Meth= odology. A systematic review is conducted using the PRISMA methodology. Based = on the search was conducted in digital journals through databases such as Scopus, Scielo, Latindex, Google scholar and Redalyc of articles on risk = evaluation in fuel backwash management through the PICO strategy. Results. Ou= t of 85 reviewed articles relating to risk assessment in fuel management 24 we= re selected, from which they address the risks occurring during fuel transfe= r, the protocols for their identification and the economic and environmental impacts Conclusion. Mathematical models and the use of artificial intelligence for risk assessment in fuel backwash management are tools th= at allow the identification and quantification of risks in such a way as to = meet protocols to minimize the economic, health and environmental impact by its occurrence.