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Salman, Chaudhary AwaisORCID iD iconorcid.org/0000-0003-2661-1961
Publikasjoner (10 av 23) Visa alla publikasjoner
Yan, J. & Salman, C. A. (2023). Waste Biorefineries: Advanced Design Concepts for Integrated Waste to Energy Processes. Elsevier
Åpne denne publikasjonen i ny fane eller vindu >>Waste Biorefineries: Advanced Design Concepts for Integrated Waste to Energy Processes
2023 (engelsk)Bok (Annet vitenskapelig)
Abstract [en]

Waste Biorefineries: Advanced Design Concepts for Integrated Waste to Energy Processes presents a detailed guide to the design of energy-efficient and cost-effective waste-integrated biorefineries. Integrating thermochemical processing of waste with existing waste-to-energy technologies, the book includes the latest developments and technologies. It introduces current waste valorization techniques and examines reasons to modify existing waste-to-energy systems through the integration of new processes. In addition, the book explains the design of novel biorefineries and methods to assess these processes alongside detailed results, including the integration of waste-based CHP plants with waste gasification and the integration of pyrolysis technologies and biogas plants with waste thermochemical processing. Other sections discuss the issues and challenges of commercializing waste-to-energy technologies, including uncertainty in waste thermochemical process designs, the environmental impact of waste-integrated biorefineries, and the role of integrated waste-to-energy management in smart cities and urban energy systems. This book will be an invaluable reference for students, researchers and those in industry who are interested in the design and implementation of waste-to-energy systems, waste biomass-based combined heat and power plants, biogas plants and forest-based industries.

sted, utgiver, år, opplag, sider
Elsevier, 2023. s. 283
Serie
Waste Biorefineries: Advanced Design Concepts for Integr. Waste to Energy Processes
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-65194 (URN)10.1016/C2021-0-01133-0 (DOI)2-s2.0-85169396097 (Scopus ID)9780323917612 (ISBN)9780323983457 (ISBN)
Tilgjengelig fra: 2023-12-21 Laget: 2023-12-21 Sist oppdatert: 2023-12-21bibliografisk kontrollert
Ashraf, W. M., Rafique, Y., Uddin, G. M., Riaz, F., Asim, M., Farooq, M., . . . Salman, C. A. (2022). Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing. Alexandria Engineering Journal, 61(3), 1864-1880
Åpne denne publikasjonen i ny fane eller vindu >>Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
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2022 (engelsk)Inngår i: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 61, nr 3, s. 1864-1880Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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. (c) 2021 THE AUTHORS. Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-57734 (URN)10.1016/j.aej.2021.07.0391110-0168 (DOI)000765309500007 ()2-s2.0-85112569410 (Scopus ID)
Tilgjengelig fra: 2022-04-06 Laget: 2022-04-06 Sist oppdatert: 2024-05-29bibliografisk kontrollert
Munir, M. A., Habib, M. S., Hussain, A., Shahbaz, M. A., Qamar, A., Masood, T., . . . Salman, C. A. (2022). Blockchain Adoption for Sustainable Supply Chain Management: Economic, Environmental, and Social Perspectives. Frontiers in Energy Research, 10, Article ID 899632.
Åpne denne publikasjonen i ny fane eller vindu >>Blockchain Adoption for Sustainable Supply Chain Management: Economic, Environmental, and Social Perspectives
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2022 (engelsk)Inngår i: Frontiers in Energy Research, E-ISSN 2296-598X, Vol. 10, artikkel-id 899632Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Frontiers Media S.A., 2022
Emneord
blockchain, sustainable supply chain, green supply chain, triple bottom-line, circular supply chain, traceability
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-59403 (URN)10.3389/fenrg.2022.899632 (DOI)000811303800001 ()2-s2.0-85132554212 (Scopus ID)
Tilgjengelig fra: 2022-06-29 Laget: 2022-06-29 Sist oppdatert: 2022-08-29bibliografisk kontrollert
Saif-Ul-Allah, M. W., Khan, J., Ahmed, F., Salman, C. A., Gillani, Z., Hussain, A., . . . Hasan, M. (2022). Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant. Frontiers in Energy Research, 10, Article ID 945769.
Åpne denne publikasjonen i ny fane eller vindu >>Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
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2022 (engelsk)Inngår i: Frontiers in Energy Research, E-ISSN 2296-598X, Vol. 10, artikkel-id 945769Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author's knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models' development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.

sted, utgiver, år, opplag, sider
FRONTIERS MEDIA SA, 2022
Emneord
NOX prediction, machine learning, 1D-convolutional neural network, LSTM, GRU, coal-fired power plant
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-59890 (URN)10.3389/fenrg.2022.945769 (DOI)000847865600001 ()2-s2.0-85137032792 (Scopus ID)
Tilgjengelig fra: 2022-09-08 Laget: 2022-09-08 Sist oppdatert: 2022-09-14bibliografisk kontrollert
Saif-ul-Allah, M. W., Qyyum, M. A., Ul-Haq, N., Salman, C. A. & Ahmed, F. (2022). Gated Recurrent Unit Coupled with Projection to Model Plane Imputation for the PM2.5 Prediction for Guangzhou City, China. Frontiers in Environmental Science, 9, Article ID 816616.
Åpne denne publikasjonen i ny fane eller vindu >>Gated Recurrent Unit Coupled with Projection to Model Plane Imputation for the PM2.5 Prediction for Guangzhou City, China
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2022 (engelsk)Inngår i: Frontiers in Environmental Science, E-ISSN 2296-665X, Vol. 9, artikkel-id 816616Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Air pollution is generating serious health issues as well as threats to our natural ecosystem. Accurate prediction of PM2.5 can help taking preventive measures for reducing air pollution. The periodic pattern of PM2.5 can be modeled with recurrent neural networks to predict air quality. To the best of the author’s knowledge, very limited work has been conducted on the coupling of missing value imputation methods with gated recurrent unit (GRU) for the prediction of PM2.5 concentration of Guangzhou City, China. This paper proposes the combination of project to model plane (PMP) with GRU for the superior prediction performance of PM2.5 concentration of Guangzhou City, China. Initially, outperforming the missing value imputation method PMP is proposed for air quality data under consideration by making a comparison study on various methods such as KDR, TSR, IA, NIPALS, DA, and PMP. Secondly, it presents GRU in combination with PMP to show its superiority on other machine learning techniques such as LSSVM and two other RNN variants, LSTM and Bi-LSTM. For this study, data for Guangzhou City were collected from China’s governmental air quality website. Data contained daily values of PM2.5, PM10, O3, SOx, NOx, and CO. This study has employed RMSE, MAPE, and MEDAE as model prediction performance criteria. Comparison of prediction performance criteria on the test data showed GRU in combination with PMP has outperformed the LSSVM and other RNN variants LSTM and Bi-LSTM for Guangzhou City, China. In comparison with prediction performance of LSSVM, GRU improved the prediction performance on test data by 40.9% RMSE, 48.5% MAPE, and 50.4% MEDAE. 

sted, utgiver, år, opplag, sider
Frontiers Media S.A., 2022
Emneord
Bi-LSTM, GRU, Guangzhou city, LSTM, PM2.5 prediction, project to model plane
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-57535 (URN)10.3389/fenvs.2021.816616 (DOI)000760678200001 ()2-s2.0-85125136056 (Scopus ID)
Tilgjengelig fra: 2022-03-02 Laget: 2022-03-02 Sist oppdatert: 2022-03-18bibliografisk kontrollert
Khan, M. A., Khan, M. I., Kazim, A. H., Shabir, A., Riaz, F., Mustafa, N., . . . Salman, C. A. (2021). An Experimental and Comparative Performance Evaluation of a Hybrid Photovoltaic-Thermoelectric System. Frontiers in Energy Research, 9, Article ID 722514.
Åpne denne publikasjonen i ny fane eller vindu >>An Experimental and Comparative Performance Evaluation of a Hybrid Photovoltaic-Thermoelectric System
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2021 (engelsk)Inngår i: Frontiers in Energy Research, E-ISSN 2296-598X, Vol. 9, artikkel-id 722514Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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 degrees C to 52 degrees 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.

sted, utgiver, år, opplag, sider
FRONTIERS MEDIA SA, 2021
Emneord
photovoltaic, thermoelectric, hybrid, photovoltaic-thermoelectric, experimentation, performance evaluation
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-56311 (URN)10.3389/fenrg.2021.722514 (DOI)000708391600001 ()2-s2.0-85117098495 (Scopus ID)
Tilgjengelig fra: 2021-10-28 Laget: 2021-10-28 Sist oppdatert: 2021-10-28bibliografisk kontrollert
Masrur Hossain, M., Afnan Ahmed, N., Abid Shahriyar, M., Monjurul Ehsan, M., Riaz, F., Salehin, S. & Salman, C. A. (2021). Analysis and optimization of a modified Kalina cycle system for low-grade heat utilization. Energy Conversion and Management: X, 12, Article ID 100121.
Åpne denne publikasjonen i ny fane eller vindu >>Analysis and optimization of a modified Kalina cycle system for low-grade heat utilization
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2021 (engelsk)Inngår i: Energy Conversion and Management: X, ISSN 2590-1745, Vol. 12, artikkel-id 100121Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Kalina cycle system (KCS) offers an attractive prospect to produce power by utilizing low-grade heat sources where traditional power cycles cannot be implemented. Intending to explore the potential of exploiting low-grade heat sources for conversion to electrical energy, this study proposes two modified power generation cycles based on KCS-34. A multi-phase expander is positioned between the Kalina separator and the second heat regenerator in the proposed X-modification. In contrast, it is located between the mixer and second regenerator for Y-modification. To explore the potential benefits and limitations of the proposed modifications contrasted with the KCS-34, thermodynamic modeling and optimization have been conducted. The influence of critical decision parameters on overall cycle performance is analyzed. The result elucidates that by implementing an additional multi-phase expander, a significant amount of energy can be extracted from a lean ammonia water loop and X-modification can deliver superior thermodynamic performance compared with the Y-modification and the original KCS-34. With a reduced turbine inlet pressure of 58 bar and an ammonia concentration of 80%, the X-modified cycle's efficiency reaches a peak value of 17% and a net power yield of 1015 kW. An increase of 6.35% can be achieved compared with the conventional KCS-34 operating at the same conditions. Maximum exergy destruction of the working substance was observed in the condenser. 

sted, utgiver, år, opplag, sider
Elsevier Ltd, 2021
Emneord
Kalina cycle system, Low-grade thermal source, Multi-phase expander, Thermodynamic analysis, Ammonia, Ammonium hydroxide, Regenerators, Cycle systems, Kalina cycle, Low grade, Lowgrade heat source (LGHS), Optimisations, Power, Thermal source, Thermoanalysis
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-57100 (URN)10.1016/j.ecmx.2021.100121 (DOI)000733406600002 ()2-s2.0-85122679932 (Scopus ID)
Tilgjengelig fra: 2022-02-24 Laget: 2022-02-24 Sist oppdatert: 2022-03-18bibliografisk kontrollert
Raihan Uddin, M., Mahmud, S., Salehin, S., Abdul Aziz Bhuiyan, M., Riaz, F., Modi, A. & Salman, C. A. (2021). Energy analysis of a solar driven vaccine refrigerator using environment-friendly refrigerants for off-grid locations. Energy Conversion and Management: X, 11, Article ID 100095.
Åpne denne publikasjonen i ny fane eller vindu >>Energy analysis of a solar driven vaccine refrigerator using environment-friendly refrigerants for off-grid locations
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2021 (engelsk)Inngår i: Energy Conversion and Management: X, ISSN 2590-1745, Vol. 11, artikkel-id 100095Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In many remote localities, one of the underlying reasons for not receiving life-saving vaccines is the lack of electricity to store the vaccines in the required refrigerated conditions. Solar Photovoltaic (PV) refrigerators have been considered as a viable and green solution to store the vaccines in remote localities having no access to electricity. In this paper, a detailed methodology has been presented for the performance evaluation of a solar PV powered vaccine refrigerator for remote locations. Thermal modelling with hourly cooling load calculations and refrigeration cycle simulations were carried out. The performance parameters for three environment-friendly refrigerants: R152a, R1234yf, and R1234ze(E) has been compared against the commonly used R134a for two remote, off-grid locations in Bangladesh and South Sudan. The energy systems comprising of solar PV panels and batteries to run the refrigerator were modelled in HOMER software for techno-economic optimizations. For both the locations, R152a was found to be the best performing refrigerant exhibiting higher COP (2%−5.29%) as compared to the other refrigerants throughout the year, while R1234ze(E) exhibited COPs on par with R134a, and R1234yf had the least performance. Techno-economic analysis showed an energy system providing electricity to the refrigerator with R152a also had lower levelized cost of electricity (0.48%−2.54%) than the systems having other refrigerants in these locations.

sted, utgiver, år, opplag, sider
Elsevier Ltd, 2021
Emneord
Modelling, Solar PV, Solar refrigeration, Techno-economic, Thermal systems, Vaccine storage
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-55521 (URN)10.1016/j.ecmx.2021.100095 (DOI)000700580300005 ()2-s2.0-85110666625 (Scopus ID)
Tilgjengelig fra: 2021-08-05 Laget: 2021-08-05 Sist oppdatert: 2021-10-07bibliografisk kontrollert
Queiroz, M. V., Blanco Ojeda, F. W., Amjad, M., Riaz, F., Salman, C. A., Parise, J. A. & Bandarra Filho, E. P. (2021). Experimental comparison between R134a/R744 and R438A/R744 (drop-in) cascade refrigeration systems based on energy consumption and greenhouse gases emissions. Energy Science & Engineering, 9(12), 2281-2297
Åpne denne publikasjonen i ny fane eller vindu >>Experimental comparison between R134a/R744 and R438A/R744 (drop-in) cascade refrigeration systems based on energy consumption and greenhouse gases emissions
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2021 (engelsk)Inngår i: Energy Science & Engineering, ISSN 2050-0505, Vol. 9, nr 12, s. 2281-2297Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This experimental study evaluates the energy performance and climatic changes of a cascade cooling system operating with the R134a/R744 pairs (cooling capacity of 4.5-6 kW) and R438A/R744. In both cases, the low-temperature refrigerant, R744, operated under subcritical conditions. The experimental apparatus basically consists of two vapor-compression cycles coupled by a plate cascade condenser. Two operational variables, from R744 cycle, were controlled: the degree-of-superheat and the compressor frequency. The experiment was initially assembled to pair R134a/R744. Subsequently, the R134a refrigerant charge in the high-temperature cycle was replaced by R438A, on a drop-in basis. The two systems, R134a/R744 and R438A/R744, were compared for similar cooling capacities and cold chamber air temperatures. Results showed that the energy consumption of the high-temperature compressor, operating with R438A, was higher than R134a for all tests. As a result, the COP values for R438A/R744 were 30% lower than those for R134a/R744. The greenhouse gases emissions of the two systems were evaluated using the total equivalent warming impact factor, TEWI, whose value for the R438A/R744 pair was approximately 29.5% higher, compared with R134a/R744. Since R438A was originally designed to substitute R22, a few comparative tests were carried out with the latter, always with R744 as the low-temperature cycle working fluid.

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-56206 (URN)10.1002/ese3.976 (DOI)000703741100001 ()2-s2.0-85116144665 (Scopus ID)
Tilgjengelig fra: 2021-10-14 Laget: 2021-10-14 Sist oppdatert: 2021-12-16bibliografisk kontrollert
Usman, M., Jamil, M. K., Riaz, F., Hussain, H., Hussain, G., Shah, M. H., . . . Lee, M. (2021). Refining and reuse of waste lube oil in si engines: A novel approach for a sustainable environment. Energies, 14(10), Article ID 2937.
Åpne denne publikasjonen i ny fane eller vindu >>Refining and reuse of waste lube oil in si engines: A novel approach for a sustainable environment
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2021 (engelsk)Inngår i: Energies, E-ISSN 1996-1073, Vol. 14, nr 10, artikkel-id 2937Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The protection of the environment and pollution control are issues of paramount impor-tance. 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. 

sted, utgiver, år, opplag, sider
MDPI AG, 2021
Emneord
Engine performance, Environment, Exhaust emissions, Lubricating oil regeneration, Pollution, Spark-ignition engine
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-54638 (URN)10.3390/en14102937 (DOI)000662466100001 ()2-s2.0-85106873616 (Scopus ID)
Tilgjengelig fra: 2021-06-10 Laget: 2021-06-10 Sist oppdatert: 2023-08-28bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0003-2661-1961

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