• Energy Research

The protection of the environment and pollution control are issues of paramount importance. Researchers today are engrossed in mitigating the harmful impacts of petroleum waste on the environment. Lubricating oils, which are essential for the smooth operation of engines, are often disposed of improperly after completing their life. In the experimental work presented in this paper, deteriorated engine oil was regenerated using the acid treatment method and was reused in the engine. The comparison of the properties of reused oil, the engine’s performance, and the emissions from the engine are presented. The reuse of regenerated oil, the evaluation of performance, and emissions establish the usefulness of the regeneration of waste lubricating oil. For the used oil, total acid number (TAN), specific gravity, flash point, ash content, and kinematic viscosity changed by 60.7%, 6.7%, 4.4%, 96%, and 15.5%, respectively, compared with fresh oil. The regeneration partially restored all the lost lubricating oil properties. The performance parameters, brake power (BP), brake specific fuel consumption (BSFC), and exhaust gas temperature (EGT) improved with regenerated oil in use compared with used oil. The emissions CO and NOX contents for acid-treated oil were 9.7% and 17.3% less in comparison with used oil, respectively. Thus, regenerated oil showed improved performance and oil properties along with significantly reduced emissions when employed in an SI engine.

There is increasing interest in the harnessing of energy from waste owing to the increase in global waste generation and inadequate currently implemented waste disposal practices, such as composting, landfilling or dumping. The purpose of this study is to provide a modelling and simulation framework to analyze the technical potential of treating municipal solid waste (MSW) and refuse-derived fuel (RDF) for the polygeneration of biofuels along with district heating (DH) and power. A flexible waste gasification polygeneration facility is proposed in this study. Two types of waste—MSW and RDF—are used as feedstock for the polygeneration process. Three different gasifiers—the entrained flow gasifier (EFG), circulating fluidized bed gasifier (CFBG) and dual fluidized bed gasifier (DFBG)—are compared. The polygeneration process is designed to produce DH, power and biofuels (methane, methanol/dimethyl ether, gasoline or diesel and ammonia). Aspen Plus is used for the modelling and simulation of the polygeneration processes. Four cases with different combinations of DH, power and biofuels are assessed. The EFG shows higher energy efficiency when the polygeneration process provides DH alongside power and biofuels, whereas the DFBG and CFBG show higher efficiency when only power and biofuels are produced. RDF waste shows higher efficiency as feedstock than MSW in polygeneration process.

The majority of incident solar irradiance causes thermalization in photovoltaic (PV) cells, attenuating their efficiency. In order to use solar energy on a large scale and reduce carbon emissions, their efficiency must be enhanced. Effective thermal management can be utilized to generate additional electrical power while simultaneously improving photovoltaic efficiency. In this work, an experimental model of a hybrid photovoltaic-thermoelectric generation (PV-TEG) system is developed. Ten bismuth telluride-based thermoelectric modules are attached to the rear side of a 10 W polycrystalline silicon-based photovoltaic module in order to recover and transform waste thermal energy to usable electrical energy, ultimately cooling the PV cells. The experiment was then carried out for 10 days in Lahore, Pakistan, on both a simple PV module and a hybrid PV-TEG system. The findings revealed that a hybrid system has boosted PV module output power and conversion efficiency. The operating temperature of the PV module in the hybrid system is reduced by 5.5%, from 55°C to 52°C. Due to a drop in temperature and the addition of some recovered energy by thermoelectric modules, the total output power and conversion efficiency of the system increased. The hybrid system’s cumulative output power increased by 19% from 8.78 to 10.84 W, compared to the simple PV system. Also, the efficiency of the hybrid PV-TEG system increased from 11.6 to 14%, which is an increase of 17% overall. The results of this research could provide consideration for designing commercial hybrid PV-TEG systems.

Abstract For biomass/waste fueled power plants, stricter regulations require a further reduction of the negative impacts on the environment caused by the release of pollutants and withdrawal of fresh water externally. Flue gas quench (FGQ) is playing an important role in biomass or waste fueled combined heat and power (CHP) plants, as it can link the flue gas (FG) cleaning, energy recovery and wastewater treatment. Enhancing water evaporation can benefit the concentrating of pollutant in the quench water; however, when FG condenser (FGC) is not in use, it results in a large consumption of fresh water. In order to deeply understand the operation of FGQ, a mathematic model was developed and validated against the measurements. Based on simulation results key parameters affecting FGQ have been identified, such as the flow rate and temperature of recycling water and the moisture content of FG. A guideline about how to reduce the discharge of wastewater to the external and the withdrawal of external water can be proposed. The mathematic model was also implemented into an ASPEN Plus model about a CHP plant to assess the impacts of FGQ on CHP. Results show that when the FGC was running, increasing the flow rate and decreasing the temperature of recycling water can result in a lower total energy efficiency.

Due to the rapid increase in environmental degradation and depletion of natural resources, the focus of researchers is shifted from economic to socio-environmental problems. Blockchain is a disruptive technology that has the potential to restructure the entire supply chain for sustainable practices. Blockchain is a distributed ledger that provides a digital database for recording all the transactions of the supply chain. The main purpose of this research is to explore the literature relevant to blockchain for sustainable supply chain management. The focus of this review is on the sustainability of the blockchain-based supply chain concerning environmental conservation, social equality, and governance effectiveness. Using a systematic literature review, a total of 136 articles were evaluated and categorized according to the triple bottom-line aspects of sustainability. Challenges and barriers during blockchain adoption in different industrial sectors such as aviation, shipping, agriculture and food, manufacturing, automotive, pharmaceutical, and textile industries were critically examined. This study has not only explored the economic, environmental, and social impacts of blockchain but also highlighted the emerging trends in a circular supply chain with current developments of advanced technologies along with their critical success factors. Furthermore, research areas and gaps in the existing research are discussed, and future research directions are suggested. The findings of this study show that blockchain has the potential to revolutionize the entire supply chain from a sustainability perspective. Blockchain will not only improve the economic sustainability of the supply chain through effective traceability, enhanced visibility through information sharing, transparency in processes, and decentralization of the entire structure but also will help in achieving environmental and social sustainability through resource efficiency, accountability, smart contracts, trust development, and fraud prevention. The study will be helpful for managers and practitioners to understand the procedure of blockchain adoption and to increase the probability of its successful implementation to develop a sustainable supply chain network.

Combustion of waste for cogeneration of heat and power is the most convenient and practical choice to carry out through combined heat and power (CHP) plants. But, seasonal variation in heat demand ...

Les vibrations des roulements maintenant l'arbre à grande vitesse d'une turbine à vapeur sont contrôlées de manière critique pour une production d'énergie sûre et fiable dans les centrales électriques. Dans cet article, deux modèles de processus d'intelligence artificielle (IA), c'est-à-dire un réseau neuronal artificiel (RNA) et une machine à vecteur de support (SVM) basés sur la modélisation des vibrations relatives d'un palier d'arbre de turbine à vapeur d'un système de turbine à vapeur supercritique de 660 MW, sont présentés. Après de nombreux tests de visualisation basés sur le traitement des données et l'apprentissage automatique effectués sur les données opérationnelles brutes, les modèles ANN et SVM sont formés, validés et comparés par des tests de validation externes. ANN a surpassé SVM en termes de meilleure capacité de prédiction et est donc déployée pour simuler les scénarios d'exploitation construits. Le modèle de processus ANN est testé pour la plage de charge complète de la centrale électrique, c'est-à-dire de 353 MW à 662 MW et une réduction de 4,07 % de la vibration relative du roulement est prévue par le réseau. En outre, diverses stratégies d'exploitation de réduction des vibrations sont développées et testées sur le modèle de processus ANN validé et robuste. Une stratégie de fonctionnement sélectionnée qui a prédit une réduction prometteuse de la vibration relative du roulement est sélectionnée. Afin de confirmer l'efficacité de la prédiction du modèle de processus ANN, la stratégie d'exploitation sélectionnée est mise en œuvre sur le fonctionnement réel de la centrale. La réduction résultante des vibrations relatives du palier de la turbine, qui est inférieure à la limite d'alarme, est confirmée. Cela renforce le rôle du modèle de processus ANN en tant qu'outil d'excellence opérationnelle permettant de réduire les vibrations des équipements rotatifs à grande vitesse. Las vibraciones de los cojinetes que sujetan el eje de alta velocidad de una turbina de vapor se controlan de forma crítica para la generación de energía segura y fiable en las centrales eléctricas. En este documento, se presentan dos modelos de proceso de inteligencia artificial (IA), es decir, modelado de vibración relativa basado en red neuronal artificial (ANN) y máquina de vectores de soporte (SVM) de un cojinete de eje de turbina de vapor de un sistema de turbina de vapor supercrítico de 660 MW. Después de un extenso procesamiento de datos y pruebas de visualización basadas en el aprendizaje automático realizadas en los datos operativos sin procesar, los modelos ANN y SVM se capacitan, validan y comparan mediante pruebas de validación externas. ANN ha superado a SVM en términos de una mejor capacidad de predicción y, por lo tanto, se implementa para simular los escenarios operativos construidos. El modelo de proceso ANN se prueba para el rango de carga completo de la planta de energía, es decir, de 353 MW a 662 MW y la red predice una reducción del 4.07% en la vibración relativa del rodamiento. Además, se desarrollan y prueban varias estrategias operativas de reducción de vibraciones en el modelo de proceso ANN validado y sólido. Se selecciona una estrategia operativa seleccionada que ha predicho una reducción prometedora en la vibración relativa del rodamiento. Con el fin de confirmar la efectividad de la predicción del modelo de proceso ANN, la estrategia operativa seleccionada se implementa en la operación real de la planta de energía. Se confirma la reducción resultante de las vibraciones relativas del rodamiento de la turbina, que es inferior al límite de alarma. Esto consolida el papel del modelo de proceso ANN para ser utilizado como una herramienta de excelencia operativa que resulta en la reducción de la vibración de los equipos rotativos de alta velocidad. The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine's bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment. يتم التحكم في اهتزازات المحامل التي تحمل عمود التوربين البخاري عالي السرعة بشكل حاسم لتوليد الطاقة بشكل آمن وموثوق في محطات الطاقة. في هذه الورقة، يتم تقديم نموذجين لعملية الذكاء الاصطناعي (AI)، أي الشبكة العصبية الاصطناعية (ANN) ونمذجة الاهتزاز النسبي القائمة على آلة ناقلات الدعم (SVM) لمحمل عمود التوربين البخاري لنظام توربين بخاري فوق حرج بقدرة 660 ميجاوات. بعد معالجة البيانات المكثفة واختبارات التصور القائمة على التعلم الآلي التي يتم إجراؤها على البيانات التشغيلية الأولية، يتم تدريب نماذج ANN و SVM والتحقق من صحتها ومقارنتها من خلال اختبارات التحقق الخارجية. تفوقت ANN على SVM من حيث قدرة التنبؤ الأفضل، وبالتالي تم نشرها لمحاكاة سيناريوهات التشغيل المبنية. يتم اختبار نموذج عملية ANN لنطاق الحمل الكامل لمحطة الطاقة، أي من 353 ميجاوات إلى 662 ميجاوات وتتوقع الشبكة انخفاضًا بنسبة 4.07 ٪ في الاهتزاز النسبي للمحمل. علاوة على ذلك، يتم تطوير واختبار العديد من استراتيجيات تشغيل تقليل الاهتزاز على نموذج عملية ANN الذي تم التحقق من صحته وقوته. يتم اختيار استراتيجية تشغيل مختارة تنبأت بانخفاض واعد في الاهتزاز النسبي للمحمل. من أجل تأكيد فعالية التنبؤ بنموذج عملية ANN، يتم تنفيذ استراتيجية التشغيل المختارة على التشغيل الفعلي لمحطة الطاقة. تم تأكيد الانخفاض الناتج في الاهتزازات النسبية لمحمل التوربين، وهو أقل من حد الإنذار. وهذا يعزز دور نموذج عملية ANN لاستخدامه كأداة للتميز التشغيلي مما يؤدي إلى تقليل الاهتزاز للمعدات الدوارة عالية السرعة.