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  • 1.
    Khan, Muhammad Ahsan Iqbal
    et al.
    Univ Lahore, Dept Technol, Lahore, Pakistan..
    Khan, Muhammad Irfan
    Univ Lahore, Lahore Sch Aviat, Lahore, Pakistan..
    Kazim, Ali Hussain
    Univ Engn & Technol Lahore, Dept Mech Engn, Lahore, Pakistan..
    Shabir, Aqsa
    Lahore Coll Women Univ, Dept Elect Engn, Lahore, Pakistan..
    Riaz, Fahid
    Natl Univ Singapore, Dept Mech Engn, Singapore, Singapore..
    Mustafa, Nauman
    Univ Engn & Technol Lahore, Dept Mech Engn, Lahore, Pakistan..
    Javed, Hassan
    Univ Lahore, Dept Technol, Lahore, Pakistan..
    Raza, Ali
    Univ Lahore, Dept Technol, Lahore, Pakistan..
    Hussain, Mohsin
    Univ Lahore, Dept Technol, Lahore, Pakistan..
    Salman, Chaudhary Awais
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    An Experimental and Comparative Performance Evaluation of a Hybrid Photovoltaic-Thermoelectric System2021In: Frontiers in Energy Research, E-ISSN 2296-598X, Vol. 9, article id 722514Article in journal (Refereed)
    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.

  • 2.
    Munir, M. Adeel
    et al.
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Habib, M. Salman
    Univ Engn & Technol Lahore, Dept Ind & Mfg Engn, Lahore, Pakistan..
    Hussain, Amjad
    Univ Engn & Technol Lahore, Dept Mech Engn, Lahore, Pakistan..
    Shahbaz, Muhammad Ali
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Qamar, Adnan
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Masood, Tariq
    Univ Strathclyde, Dept Design Mfg & Engn Management, Glasgow, Scotland..
    Sultan, M.
    Bahauddin Zakariya Univ, Dept Agr Engn, Multan, Pakistan..
    Mujtaba, M. A.
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Imran, Shahid
    Univ Engn & Technol Lahore, Dept Mech Engn New Campus, Lahore, Pakistan..
    Hasan, Mudassir
    King Khalid Univ, Coll Engn, Chem Engn Dept, Abha, Saudi Arabia..
    Akhtar, Muhammad Saeed
    Yeungnam Univ, Coll Engn, Sch Chem Engn, Gyongsan, South Korea..
    Ayub, Hafiz Muhammad Uzair
    Yeungnam Univ, Coll Engn, Sch Chem Engn, Gyongsan, South Korea..
    Salman, Chaudhary Awais
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Blockchain Adoption for Sustainable Supply Chain Management: Economic, Environmental, and Social Perspectives2022In: Frontiers in Energy Research, E-ISSN 2296-598X, Vol. 10, article id 899632Article in journal (Refereed)
    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.

  • 3.
    Saif-Ul-Allah, Muhammad Waqas
    et al.
    COMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan..
    Khan, Javed
    COMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan..
    Ahmed, Faisal
    COMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan..
    Salman, Chaudhary Awais
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Gillani, Zeeshan
    COMSATS Univ Islamabad, Dept Comp Sci, Lahore, Pakistan..
    Hussain, Arif
    COMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan..
    Yasin, Muhammad
    COMSATS Univ Islamabad, Dept Chem Engn, Lahore, Pakistan..
    Ul-Haq, Noaman
    COMSATS Univ Islamabad, Dept Chem Engn, Lahore, Pakistan..
    Khan, Asad Ullah
    COMSATS Univ Islamabad, Dept Chem Engn, Lahore, Pakistan.;Natl Univ Sci & Technol, Dept Chem Engn, SCME, Islamabad, Pakistan..
    Bazmi, Aqeel Ahmed
    COMSATS Univ Islamabad, Proc & Energy Syst Engn Ctr, Dept Chem Engn, PRESTIGE, Lahore, Pakistan..
    Ahmad, Zubair
    Yeungnam Univ, Sch Chem Engn, Gyongsan, South Korea..
    Hasan, Mudassir
    King Khalid Univ, Coll Engn, Dept Chem Engn, Abha, Saudi Arabia..
    Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant2022In: Frontiers in Energy Research, E-ISSN 2296-598X, Vol. 10, article id 945769Article in journal (Refereed)
    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.

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