Efficiency enhancement in double‐pass perforated glazed solar air heaters with Porous beds : Taguchi‐artificial neural network optimization and cost–benefit analysis

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 plex‐ iglass 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 DPG‐ SAHI 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.https://www.mdpi.com/journal/sustainabilitydm2022Mechanical and Aeronautical Engineerin

A critical assessment of technical advances in pharmaceutical removal from wastewater – A critical review

Use of pharmaceutical products has seen a tremendous increase in the recent decades. It has been observed that more than thirty million tons of pharmaceuticals are consumed worldwide. The used pharmaceutical products are not completely metabolized in human and animal body. Therefore, they are excreted to the environment and remain there as persistent organic chemicals. These compounds emerge as toxic contaminants in water and affect the human metabolism directly or indirectly. This literature review is an endeavour to understand the origin, applications and current advancement in the removal of pharmaceuticals from the environment. It discusses about the pharmaceuticals used in medical applications such diagnosis and disease treatment. In addition, it discusses about the recent approaches applied in pharmaceutical removal including microbial fuel cells, biofiltration, and bio nanotechnology approaches. Moreover, the challenges associated with pharmaceutical removal are presented considering biological and environmental factors. The review suggest the potential recommendations on pharmaceutical removal.The corresponding author Prof. Vinay Kumar is thankful to all the co-authors for their collaborative efforts in writing this paper. This work was supported by Department of Community Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India.Peer reviewe

MIL-53 (Fe)-based photo-sensitive composite for degradation of organochlorinated herbicide and enhanced reduction of Cr(VI)

Highly robust composite photocatalyst (WO3/MIL-53(Fe)) was synthesized and characterised. WO3/MIL-53(Fe) exhibits remarkable photocatalytic efficiency for reduction of Cr(VI) and oxidation of 2,4-dichlorophenoxyacetic acid (2,4-D) organochlorinated herbicide. After 240 min sunlight irradiation, only ∼30% reduction ratio of Cr(VI) was recorded in the presence of WO3. However, about 70% and 94% Cr(VI) reduction were achieved in the presence of MIL-53(Fe) and WO3/MIL-53(Fe), respectively. The removal efficiency proceeds more rapidly in the binary mixture of 2,4-D and Cr(VI) than in the single component system, indicating synergetic effect between the photooxidation and reduction reactions. O2− played a key role in the reduction of Cr (VI) and h+ is confirmed to be the dominant active species in the degradation of 2,4-D

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

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

Highly robust AgIO3/MIL-53 (Fe) nanohybrid composites for degradation of organophosphorus pesticides in single and binary systems: Application of artificial neural networks modellingm

A robust sunlight-driven AgIO₃/MIL-53 (Fe) nanohybrid composite (NC) was successfully synthesised and characterised. The efficacy of the NC was estimated by decomposing two organophosphates pesticides (methyl malathion (MP) and chlorpyrifos (CP)) under sunlight irradiation. The degradation of MP and CP was strongly influenced by pH, catalyst dose and initial pesticide concentration. Under 60 min solar light illumination, ∼78–90% CP and MP were degraded individually in tap and distilled water, respectively. In binary mixture (MP + CP), ∼70% mineralisation was achieved within 180 min. The efficiency of NC is attributed to the prolonged separation of photogenerated carriers, a large concentration of surface hydroxyl groups and high specific surface area. Under artificial neural network (ANN) predicted conditions, 0.5 g NC, pH 5 and 2.5 mL of 50 mg/L Na₂S₂O₈ are required for complete mineralisation of the pesticide

Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis

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

High-Performance Nanocatalyst for Adsorptive and Photo-Assisted Fenton-Like Degradation of Phenol: Modeling Using Artificial Neural Networks

<p>High-performance activated carbon-zinc oxide (Ac–ZnO) nanocatalyst was fabricated via the microwave-assisted technique. Ac–ZnO was characterized and the results indicated that Ac–ZnO is stable, had a band gap of 3.26 eV and a surface area of 603.5 m²g⁻¹, and exhibited excellent adsorptive and degrading potentials. About 93% phenol was adsorbed within 550 min of reaction by Ac–ZnO. Impressively, a complete degradation was achieved in 90 min via a photo-Fenton/Ac–ZnO system under optimum conditions. An artificial neural network (ANN) model was developed and applied to study the relative significance of input variables affecting the degradation of phenol in a photo-Fenton process. The ANN results indicate that increases in both H₂O₂ and Ac–ZnO dosage enhanced the rate of phenol degradation. The highest rate constant at the optimum conditions was 0.093 min⁻¹ and it was found to be consistent with the ANN-predicted rate constant (0.095 min⁻¹).</p