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Case Studies in Thermal Engineering
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
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SSRN Electronic Journal
Article . 2022 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.60692/k9...
Other literature type . 2022
Data sources: Datacite
https://dx.doi.org/10.60692/16...
Other literature type . 2022
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Investigation of Combustion Performance of Tannery Sewage Sludge Using Thermokinetic Analysis and Prediction by Artificial Neural Network

التحقيق في أداء الاحتراق من حمأة المجاري المدبغة باستخدام التحليل الحراري والتنبؤ من قبل الشبكة العصبية الاصطناعية
Authors: Arslan Khan; Imtiaz Ali; Wasif Farooq; Salman Raza Naqvi; Muhammad Taqi Mehran; Ameen Shahid; Rabia Liaquat; +2 Authors

Investigation of Combustion Performance of Tannery Sewage Sludge Using Thermokinetic Analysis and Prediction by Artificial Neural Network

Abstract

L'élimination et la gestion des boues d'épuration des tanneries est un enjeu difficile pour les industries du cuir en raison de leurs effets néfastes sur l'environnement. Dans cette étude, la caractérisation et l'évaluation détaillées à l'aide de paramètres cinétiques et thermodynamiques des boues d'épuration de tannerie dans un environnement de combustion ont été utilisées. Des méthodes sans modèle d'isoconversion comme Ozawa-Flynn-Wall (OFW), Friedman et Kissinger-Akahira-Sunose (KAS) ont été utilisées pour étudier la cinétique et les paramètres thermodynamiques dans l'environnement de l'air. Les énergies d'activation (Ea) pour le Friedman, le KAS et l'OFW ont été rapportées. Les courbes DTG à la vitesse de chauffe de 5, 10, 20 et 40 °C/min montrent les conversions diversifiées en trois étapes majeures. Les valeurs Ea pour les gammes de modèles sont Friedman (148,96 kJ/mol-395,23 kJ/mol), KAS (169,65 kJ/mol-383,75 kJ/mol) et OFW (176,44 kJ/mol-377,85 kJ/mol). L'Ea moyen pour le Friedman est de 226,04 kJ/mol tandis que pour KAS et OFW, l'Ea moyen est de 230,71 kJ/mol et 230,11 kJ/mol. De plus, les valeurs de ΔH, ΔG et ΔS ont été analysées. En outre, la distribution de fréquence en appliquant le modèle DAEM est étudiée, et il y a six pseudo-composants impliqués dans la distribution de fréquence pour la combustion. Pour la prédiction de la dégradation thermique des boues d'épuration de la tannerie, un réseau neuronal artificiel (RNA) du modèle MLP-3-7-1 a été utilisé. Ce modèle montre qu'il existe un bon accord entre les valeurs expérimentales et les valeurs prédites. Dans l'ensemble, cette étude souligne l'importance de l'ANN pour la prédiction du comportement de combustion de la biomasse avec plus de précision.

La eliminación y la gestión de los lodos de depuración de las curtidurías es un problema difícil para las industrias del cuero debido a sus efectos adversos sobre el medio ambiente. En este estudio se empleó la caracterización y evaluación detallada utilizando parámetros cinéticos y termodinámicos de los lodos de depuradora de curtiduría en ambiente de combustión. Se emplearon métodos sin modelos isoconversionales como Ozawa-Flynn-Wall (OFW), Friedman y Kissinger-Akahira-Sunose (KAS) para investigar la cinética y los parámetros termodinámicos en el ambiente aéreo. Se informaron las energías de activación (Ea) para Friedman, KAS y OFW. Las curvas DTG a la velocidad de calentamiento de 5, 10, 20 y 40 °C/min muestran las conversiones diversificadas en tres etapas principales. Los valores de Ea para los rangos del modelo son Friedman (148.96 kJ/mol-395.23 kJ/mol), KAS (169.65 kJ/mol-383.75 kJ/mol) y OFW (176.44 kJ/mol-377.85 kJ/mol). La Ea media para Friedman es de 226,04 kJ/mol, mientras que para KAS y OFW la Ea media es de 230,71 kJ/mol y 230,11 kJ/mol. Además, se analizaron los valores de ΔH, ΔG y ΔS. Además, se investiga la distribución de frecuencias mediante la aplicación del modelo DAEM, y hay seis pseudocomponentes involucrados en la distribución de frecuencias para la combustión. Para la predicción de la degradación térmica de los lodos de depuradora de la curtiduría se utilizó una red neuronal artificial (ANN) del modelo MLP-3-7-1. Este modelo muestra que existe una buena concordancia entre los valores experimentales y los pronosticados. En general, este estudio destaca la importancia de la ANN para la predicción del comportamiento de combustión de la biomasa con mayor precisión.

The disposal and the management of sewage sludge from tanneries is a challenging issue for the leather industries because of their adverse effect on the environment. In this study the detailed characterization and assessment using kinetic and thermodynamic parameters of the tannery sewage sludge in combustion environment was employed. Isoconversional model-free methods like Ozawa-Flynn-Wall (OFW), Friedman and Kissinger-Akahira-Sunose (KAS) were employed to investigate the kinetics and the thermodynamic parameters in the air environment. Activation energies (Ea) for the Friedman, KAS and OFW were reported. The DTG curves at the heating rate of 5, 10, 20 and 40 °C/min show the diversified conversions in three major stages. The Ea values for the model ranges are Friedman (148.96 kJ/mol-395.23 kJ/mol), KAS (169.65 kJ/mol-383.75 kJ/mol) and OFW (176.44 kJ/mol-377.85 kJ/mol). The average Ea for the Friedman is 226.04 kJ/mol while for KAS and OFW the average Ea is 230.71 kJ/mol and 230.11 kJ/mol. Moreover, the values of ΔH, ΔG, and ΔS were analysed. Furthermore, the frequency distribution by applying the DAEM model is investigated, and there are six pseudo-components involved in the frequency distribution for combustion. For the thermal degradation prediction of the sewage sludge from the tannery, an artificial neural network (ANN) of the MLP-3-7-1 model was used. This model shows that there is good agreement between the experimental and the predicted values. Overall, this study highlights the importance of the ANN for the prediction of combustion behaviour of biomass with more accuracy.

يمثل التخلص من حمأة الصرف الصحي وإدارتها من المدابغ مشكلة صعبة بالنسبة للصناعات الجلدية بسبب تأثيرها السلبي على البيئة. في هذه الدراسة، تم استخدام التوصيف والتقييم التفصيليين باستخدام المعلمات الحركية والديناميكية الحرارية لحمأة المجاري المدبغة في بيئة الاحتراق. تم استخدام طرق خالية من النماذج المتساوية مثل Ozawa - Flynn - Wall (OFW) و Friedman و Kissinger - Akahira - Sunose (KAS) للتحقيق في الحركية والمعلمات الديناميكية الحرارية في بيئة الهواء. تم الإبلاغ عن طاقات التنشيط (EA) لـ Friedman و KAS و OFW. تُظهر منحنيات DTG بمعدل تسخين 5 و 10 و 20 و 40 درجة مئوية/دقيقة التحويلات المتنوعة في ثلاث مراحل رئيسية. قيم Ea لنطاقات النموذج هي Friedman (148.96 kJ/mol-395.23 kJ/mol) و KAS (169.65 kJ/mol-383.75 kJ/mol) و OFW (176.44 kJ/mol-377.85 kJ/mol). يبلغ متوسط Ea لـ Friedman 226.04 كيلو جول/مول بينما يبلغ متوسط Ea لـ KAS و OFW 230.71 كيلو جول/مول و 230.11 كيلو جول/مول. علاوة على ذلك، تم تحليل قيم ΔH و ΔG و ΔS. علاوة على ذلك، يتم التحقيق في توزيع التردد من خلال تطبيق نموذج DAEM، وهناك ستة مكونات زائفة تشارك في توزيع التردد للاحتراق. للتنبؤ بالتدهور الحراري لحمأة الصرف الصحي من المدبغة، تم استخدام شبكة عصبية اصطناعية (ANN) من نموذج MLP -3-7-1. يوضح هذا النموذج أن هناك اتفاقًا جيدًا بين القيم التجريبية والمتوقعة. بشكل عام، تسلط هذه الدراسة الضوء على أهمية ANN للتنبؤ بسلوك الاحتراق للكتلة الحيوية بدقة أكبر.

Keywords

Pulp and paper industry, Materials Science, Biomedical Engineering, Combustion, Environmental engineering, Thermoanalytical Data, Thermochemical Properties of Organic Compounds, FOS: Medical engineering, Quantum mechanics, Environmental science, Engineering, Chemical engineering, FOS: Chemical sciences, Materials Chemistry, Activation energy, Sewage sludge, Kinetic Analysis of Thermal Processes in Materials, FOS: Chemical engineering, DAEM, Sewage, Physics, Organic Chemistry, FOS: Environmental engineering, Biomass Pyrolysis and Conversion Technologies, Engineering (General). Civil engineering (General), Materials science, Chemistry, Kinetics, Physical chemistry, Tannery sewage sludge, Physical Sciences, Thermodynamics, TA1-2040, ANN

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
32
Top 10%
Top 10%
Top 10%