Process assessment of a full-scale wastewater treatment plant using reliability, resilience, and econo-socio-environmental analyses (R2ESE)

Ba-Alawi, Abdulrahman H.; Ifaei, Pouya; Li, Qian; Nam, Ki Jeon; Djeddou, Messaoud

doi:10.1016/j.psep.2019.11.018

Cited by 15 publications

(3 citation statements)

References 29 publications

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“…Economic and cost assessments are vital to the maintenance of sustainable development targets regardless of complexity. A new evaluation system to further explore the resilience and efficiency of a wastewater treatment plant (WWTP) was recommended by Ba-Alawi [77]. Both efficiency curves and reliability coefficient (COR) have been calculated based on discharge requirements.…”

Section: Operational Cost Analysismentioning

confidence: 99%

Sustainable Innovation in Membrane Technologies for Produced Water Treatment: Challenges and Limitations

et al. 2021

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Discharged water from the oil and gas fields is a common type of wastewater called produced water (PW). It consists of different combinations of salinities, oils, and mineral deposits. Growing industrial demand, accelerated urbanization, and rapid population growth are putting enormous strain on the world’s water supply. Based on sustainable freshwater supplies, North Africa, the Middle East, and South Asia confront the ultimate water shortages threat. Proper implementation of innovative membrane technologies in wastewater treatment is considered a solution towards tackling water insecurity and sustainability. Different types of innovative membrane technologies used for produced water treatment were considered in this work. A framework of innovative membrane technology was studied for industrial wastewater with direct contribution to the environmental and economical sustainability factors, taking into consideration grand challenges and limitations in energy costs and environmental constraints. Treated produced water can be utilized in irrigation providing many benefits only if the desalination sector is mature and fully developed.

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Section: Operational Cost Analysismentioning

confidence: 99%

Sustainable Innovation in Membrane Technologies for Produced Water Treatment: Challenges and Limitations

et al. 2021

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“…En el proceso de tratamiento de aguas residuales, los trabajadores de las plantas podrían estar expuestos a estos patógenos y a problemas graves de salud. Factores como la fuente, las propiedades fisicoquímicas, el volumen de las aguas residuales o el funcionamiento de la planta de tratamiento pueden influir en la presencia y la concentración de microorganismos (Amoah et al, 2022) Las acciones que se realizan en las plantas de tratamiento de aguas residuales (PTAR) han sido el foco de atención en los últimos años debido a la contaminación de los recursos hídricos y a la escasez inminente del recurso (Ba-Alawi et al, 2020). El crecimiento de la población, la urbanización y la falta de saneamiento hacen difícil la tarea de mantener el agua segura y limpia.…”

Section: Introductionunclassified

Riesgo biológico asociado al tratamiento de aguas residuales: caso de estudio usando la metodología BIOGAVAL

Lozano¹,

Armijos²,

Guaya³

2023

AXIOMA

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(1) Antecedentes: En las aguas residuales están presentes muchos microorganismos que son patógenos y peligrosos para la salud, entre algunos de los identificados están los virus, bacterias, hongos, protozoos y helmintos, en el proceso de tratamiento de aguas residuales, los trabajadores de estas plantas, podrían estar expuestos a estos patógenos y a problemas graves de salud. (2) El objetivo de este estudio es definir los agentes biológicos, determinar el nivel de riesgo biológico mediante la cuantificación de variables propuestas por el método y analizar la metodología BIOGAVAL, la factibilidad y ventajas de su aplicación. (3) Métodos: Se utiliza la metodología BIOGAVAL que ha sido creada para actividades en las cuales no se manipulan deliberadamente agentes biológicos como es el caso de las Plantas de Tratamiento de Aguas Residuales (PTAR), esta metodología utiliza variables como: determinación de los puestos a evaluar, identificación del agente biológico implicado, cuantificación de las variables determinantes del riesgo, clasificación del daño, vía de transmisión, tasa de incidencia del año anterior, vacunación, frecuencia de realización de tareas de riesgo, medidas higiénicas adoptadas, cálculo del nivel de riesgo biológico, interpretación de los niveles de riesgo biológico. (4) Conclusiones: Se muestra que los agentes microbiológicos peligrosos se originan de vertidos que se descargan de establecimientos y actividades cercanas, para la aplicación se define los siguientes microorganismos: Escherichia Coli, Salmonella spp, Shigella spp y Áscaris Lumbricoides. El nivel del riesgo se determina como Aceptable. La metodología BIOGAVAL se plantea como una alternativa práctica, útil y de bajo costo, se considera factible para la aplicación en actividades sin intención deliberada de manipulación de agentes biológicos frente a métodos cuantitativos de muestreo bacteriológico en aire o superficies, así como en la detección de bioaerosoles.

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“…Pant et al [ 13 ] obtained the cumulative density function (CDF) of a container terminal’s resilience using a simulation for given recovery orders and recovery probability distributions. Ba-Alawi et al [ 14 ] conducted a resilience assessment of the membrane bioreactor in a wastewater treatment plant based on the performance curves, with the assumption that the distribution of the failure data followed a lognormal distribution. Zinetullina et al [ 15 ] proposed a quantitative resilience assessment for chemical process systems with normally distributed variables such as timing and precision.…”

Section: Introductionmentioning

confidence: 99%

System resilience distribution identification and analysis based on performance processes after disruptions

Song

Li²

2022

PLoS ONE

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Resilience is a system’s ability to withstand a disruption and return to a normal state quickly. It is a random variable due to the randomness of both the disruption and resilience behavior of a system. The distribution characteristics of resilience are the basis for resilience design and analysis, such as test sample size determination and assessment model selection. In this paper, we propose a systematic resilience distribution identification and analysis (RDIA) method based on a system’s performance processes after disruptions. Typical performance degradation/recovery processes have linear, exponential, and trigonometric functions, and they have three key parameters: the maximum performance degradation, the degradation duration, and the recovery duration. Using the Monte Carlo method, these three key parameters are first sampled according to their corresponding probability density functions. Combining the sample results with the given performance function type, the system performance curves after disruptions can be obtained. Then the sample resilience is computed using a deterministic resilience measure and the resilience distribution can be determined through candidate distribution identification, parameter estimation, and a goodness-of-fit test. Finally, we apply our RDIA method to systems with typical performance processes, and both the orthogonal experiment method and the control variable method are used to investigate the resilience distribution laws. The results show that the resilience of these systems follows the Weibull distribution. An end-to-end communication system is also used to explain how to apply this method with simulation or test data in practice.

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Process assessment of a full-scale wastewater treatment plant using reliability, resilience, and econo-socio-environmental analyses (R2ESE)

Cited by 15 publications

References 29 publications

Sustainable Innovation in Membrane Technologies for Produced Water Treatment: Challenges and Limitations

Sustainable Innovation in Membrane Technologies for Produced Water Treatment: Challenges and Limitations

Riesgo biológico asociado al tratamiento de aguas residuales: caso de estudio usando la metodología BIOGAVAL

System resilience distribution identification and analysis based on performance processes after disruptions

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