• Energy Research

Biomass pyrolysis is one of the cleaner ways to produce bioenergy focusing on bio-oil. The high oxygen content of oxygen in bio-oil limits its application in transportation applications. The deoxygenation of bio-oil using various catalyst systems is required to upgrade the bio-oil. Herein, we presented the scientometric analysis of microporous zeolites for deoxygenation of biomass-derived bio-oil. The state of the art review of biomass catalytic deoxygenation using zeolite-based materials is elucidated. A special focus on the role of catalyst physicochemical properties and degree of deoxygenation is Furthermore, the reaction pathways for different zeolites for bio-oil upgradation are presented. Finally, the technology readiness level is assessed and future recommendations are also presented.

This study investigates the efficacy of a prepared Ni/θ-Al2O3 catalyst during the pyrolytic conversion of Parachlorella kessleri HY-6 and compares the results with non-catalytic conversion. The catalyst was characterized by techniques such as Brunauer–Emmett–Teller (BET) for surface area, acidity, and X-ray powder diffraction (XRD). Isoconversional and combined kinetic methods were used to study the pyrolytic kinetics of the process. Ni/θ-Al2O3 was used at 10, 20, and 30% of the algal biomass. The addition of Ni/θ-Al2O3 facilitated the conversion by lowering the mean activation energy during pyrolysis. The catalytic effect was more pronounced at lower and higher conversions. The presence of the catalyst facilitated the pyrolysis as indicated by the lower value of activation energy and ∆H, and ∆G. Gases evolved during pyrolysis were qualitatively analyzed by FTIR to see the effect of catalyst on evolved gas composition during the pyrolysis process.

Since industrial development and globalization of the world, fossil fuels remain a major source of energy for almost all sectors of life. Fossil fuels, without doubt, play a vital role in the development of today’s world. However, there are increasing reservations about the consumption of fossil fuels due to their detrimental impact on our ecosystem. In recent decades, biodiesel has attracted much attention as a promising replacement for fossil fuel-based diesel, especially in the transportation sector. Biodiesel is a non-toxic, environmentally friendly, and carbon-free fuel that can be made from any kind of vegetable oil or fat. In recent developments, numerous techniques have been developed to efficiently prepare biodiesel from oil/fats. Therefore, in this paper, we cover a new advancement in techniques used for the conversion of oil/fat to biodiesel and the role of artificial intelligence (AI). AI approaches help predict the effectiveness of biodiesel production techniques and optimize the process, in addition to minimizing the cost of the process. The AI-enabled biodiesel prediction methods consist of several stages, i.e., biodiesel data collection, biodiesel data preprocessing, developing, and tuning machine learning (ML) algorithm on biodiesel data, and predicting unknown biodiesel properties. Therefore, the main purpose of applying AI to the biodiesel production process is to improve the process optimization and to develop AI models for fuel properties measurements and ultimately reduce the cost of the product. Because these problems have not been addressed previously, we hope that our analysis will help future researchers identify the appropriate technique and feedstock to produce high-quality biodiesel.

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.

The co-torrefaction of several biomasses may be a viable solution in the study area, as it produces biofuels and addresses waste-treatment concerns. This review evaluates biomass through ultimate, proximate, and FTIR analyses, and the mechanism of the co-torrefaction process is observed for product quality with a synergistic effect. Furthermore, the parameters of co-torrefaction, including temperature, reaction time, mass yield, energy yield, and the composition of the H/C and O/C ratio of the co-torrefied materials, are similar to those for coal composition. Different reactor types, such as fixed-bed, fluidized-bed, microwave, and batch reactors, are used for co-torrefaction, in which biomass blends with optimized blend ratios. The co-torrefaction process increases the bio-solid yield and heating value, the capacity to adsorb carbon dioxide, and the renewable fuel used for gasification. One of the objectives of this study is to adopt a process that must be viable, green, and sustainable without generating pollution. For this reason, microwave co-torrefaction (MCT) has been used in many recent studies to transform waste and biomass materials into an alternative fuel using a microwave reactor.

Abstract Agro-industrial residue is widely considered as a rich source of energy, with varying characteristics depending on the geographical region or origin from where it is collected. Rice husk, bagasse, corncob, wheat straw and wood chips do not find many applications in Pakistan. As they are available in large quantities and at lower cost, therefore it makes them a favorable candidate for bioenergy. In this study, five agro-industrial residues, of Pakistani origin, were thermally degraded in the absence of air and at a constant heating rate of 5 °C min−1. Kinetics of the pyrolysis process was performed using Coats-Redfern method at five reaction mechanisms. Corncob was found to degrade at lower temperature with fastest rate as compared to all the other wastes. The kinetic parameters obtained from Coats-Redfern method were used to evaluate the thermodynamic behavior of these wastes and afterwards a comparison was drawn. Based on the ascending order of the activation energy, the residues can be classified in terms of preference as corncob > rice husk > wood chips > wheat straw > bagasse.