| 1. |
- Munir, M. Adeel, et al.
(författare)
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Blockchain Adoption for Sustainable Supply Chain Management : Economic, Environmental, and Social Perspectives
- 2022
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Ingår i: Frontiers in Energy Research. - : Frontiers Media S.A.. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Ashraf, Waqar Muhammad, et al.
(författare)
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Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
- 2022
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Ingår i: Alexandria Engineering Journal. - 1110-0168 .- 2090-2670. ; 61:3, s. 1864-1880
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Tidskriftsartikel (refereegranskat)abstract
- 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/).
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| 3. |
- Khan, Muhammad Ahsan Iqbal, et al.
(författare)
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An Experimental and Comparative Performance Evaluation of a Hybrid Photovoltaic-Thermoelectric System
- 2021
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Ingår i: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Li, Hailong, 1976-, et al.
(författare)
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Performance of flue gas quench and its influence on biomass fueled CHP
- 2019
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Ingår i: Energy. - : Elsevier. - 0360-5442 .- 1873-6785. ; 180, s. 934-945
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Tidskriftsartikel (refereegranskat)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.
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| 5. |
- Masrur Hossain, M., et al.
(författare)
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Analysis and optimization of a modified Kalina cycle system for low-grade heat utilization
- 2021
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Ingår i: Energy Conversion and Management. - : Elsevier Ltd. - 2590-1745. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Naqvi, Muhammad, et al.
(författare)
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Polygeneration system integrated with small non-wood pulp mills for substitute natural gas production
- 2018
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Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 224, s. 636-646
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Tidskriftsartikel (refereegranskat)abstract
- This study aims to examine the potential substitute natural gas (SNG) production by integrating black liquor gasification (BLG) island with a small wheat straw-based non-wood pulp mills (NPM), which do not employ the black liquor recovery cycle. For such integration, it is important to first build knowledge on expected improvements in an overall integrated non-wood pulp mill energy system using the key performance indicators. O2-blown circulating fluidized bed (CFB) gasification with direct causticization is integrated with a reference small NPM to evaluate the overall performance. A detailed economic analysis is performed together with a sensitivity analysis based on variations in the rate of return due to varying biomass price, total capital investment, and natural gas prices. The quantitive results showed considerable SNG production but significantly reduced electricity production. There is a substantial CO2 abatement potential combining CO2 capture and CO2 mitigation from SNG use replacing compressed natural gas (CNG) or gasoline. The economic performance through sensitivity analysis reflects significant dependency on both substitute natural gas production and natural gas market price. Furthermore, the solutions to address the challenges and barriers for the successful commercial implementation of BLG based polygeneration system at small NPMs are discussed. The system performance and discussion on the real application of integrated system presented in this article form a vital literature source for future use by large number of small non-wood pulp industries.
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| 7. |
- Queiroz, Marcus Vinicius Almeida, et al.
(författare)
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Experimental comparison between R134a/R744 and R438A/R744 (drop-in) cascade refrigeration systems based on energy consumption and greenhouse gases emissions
- 2021
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Ingår i: Energy Science & Engineering. - : Wiley. - 2050-0505. ; 9:12, s. 2281-2297
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 8. |
- Raihan Uddin, M., et al.
(författare)
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Energy analysis of a solar driven vaccine refrigerator using environment-friendly refrigerants for off-grid locations
- 2021
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Ingår i: Energy Conversion and Management. - : Elsevier Ltd. - 2590-1745. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Saif-Ul-Allah, Muhammad Waqas, et al.
(författare)
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Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
- 2022
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Ingår i: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 10. |
- Saif-ul-Allah, M. W., et al.
(författare)
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Gated Recurrent Unit Coupled with Projection to Model Plane Imputation for the PM2.5 Prediction for Guangzhou City, China
- 2022
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Ingår i: Frontiers in Environmental Science. - : Frontiers Media S.A.. - 2296-665X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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