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Authors = Roozbeh Vaziri

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18 pages, 3673 KiB  
Article
Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis
by Roozbeh Vaziri, Akeem Adeyemi Oladipo, Mohsen Sharifpur, Rani Taher, Mohammad Hossein Ahmadi and Alibek Issakhov
Sustainability 2021, 13(21), 11654; https://doi.org/10.3390/su132111654 - 21 Oct 2021
Cited by 14 | Viewed by 2420
Abstract
Analyzing the combination of involving parameters impacting the efficiency of solar air heaters is an attractive research areas. In this study, cost-effective double-pass perforated glazed solar air heaters (SAHs) packed with wire mesh layers (DPGSAHM), and iron wools (DPGSAHI) were fabricated, tested and [...] Read more.
Analyzing the combination of involving parameters impacting the efficiency of solar air heaters is an attractive research areas. In this study, cost-effective double-pass perforated glazed solar air heaters (SAHs) packed with wire mesh layers (DPGSAHM), and iron wools (DPGSAHI) were fabricated, tested and experimentally enhanced under different operating conditions. Forty-eight iron pieces of wool and fifteen steel wire mesh layers were located between the external plexiglass and internal glass, which is utilized as an absorber plate. The experimental outcomes show that the thermal efficiency enhances as the air mass flow rate increases for the range of 0.014–0.033 kg/s. The highest thermal efficiency gained by utilizing the hybrid optimized DPGSAHM and DPGSAHI was 94 and 97%, respectively. The exergy efficiency and temperature difference (∆T) indicated an inverse relationship with mass flow rate. When the DPGSAHM and DPGSAHI were optimized by the hybrid procedure and employing the Taguchi-artificial neural network, enhancements in the thermal efficiency by 1.25% and in exergy efficiency by 2.4% were delivered. The results show the average cost per kW (USD 0.028) of useful heat gained by the DPGSAHM and DPGSAHI to be relatively higher than some double-pass SAHs reported in the literature. Full article
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18 pages, 1558 KiB  
Article
Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus
by Soolmaz L. Azarmi, Akeem Adeyemi Oladipo, Roozbeh Vaziri and Habib Alipour
Sustainability 2018, 10(9), 2965; https://doi.org/10.3390/su10092965 - 21 Aug 2018
Cited by 35 | Viewed by 4410
Abstract
This study was undertaken to forecast the waste generation rates of the accommodation sector in North Cyprus. Three predictor models, multiple linear regression (MLR), artificial neural networks (ANNs) and central composite design (CCD), were applied to predict the waste generation rate during the [...] Read more.
This study was undertaken to forecast the waste generation rates of the accommodation sector in North Cyprus. Three predictor models, multiple linear regression (MLR), artificial neural networks (ANNs) and central composite design (CCD), were applied to predict the waste generation rate during the lean and peak seasons. ANN showed highest prediction performance, specifically, lowest values of the standard error of prediction (SEP = 2.153), mean absolute error (MAE = 1.378) and highest R2 value (0.998) confirmed the accuracy of the model. The analysed waste was categorised into recyclable, general waste and food residue. The authors estimated the total waste generated during the lean season at 2010.5 kg/day, in which large hotels accounted for the largest fraction (66.7%), followed by medium-sized hotels (19.4%) and guesthouses (2.6%). During the peak season, about 49.6% increases in waste generation rates were obtained. Interestingly, 45% of the waste was generated by British tourists, while the least waste was generated by African tourists (7.5%). The ANN predicted that small and large hotels would produce 5.45 and 22.24 tons of waste by the year 2020, respectively. The findings herein are promising and useful in establishing a sustainable waste management system. Full article
(This article belongs to the Section Environmental Sustainability and Applications)
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