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Conversion of novel non-edible Bischofia javanica seed oil into methyl ester via recyclable zirconia-based phyto-nanocatalyst: A circular bioeconomy approach for eco-sustenance
(Elsevier, 2023-05-01) Ameen, Maria; Zafar, Muhammad; Ramadan, Mohamed Fawzy; Ahmad, Mushtaq; Makhkamov, Trobjon; Bokhari, Syed Awais Ali Shah; Mubashir, Muhammad; Chuah, Lai Fatt; Show, Pau Loke
The current study assesses Bischofia javanica Blume's potential as novel non-edible seed oil for environmentally benign biodiesel production using phyto-nanocatalyst, i.e., green nanoparticles (NPs) of zirconium oxide (ZrO2) synthesized with aqueous leaf extract of the same plant via the biological method. Using response surface methods, the maximum yield (95.8 wt.%) was obtained at a 1:6 oil-to-methanol molar ratio, 2.5 wt.% catalyst loading, 70 degrees C reaction temperature and 2 h of reaction time. In addition, advanced analytical techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) were used to characterize green nanoparticles. Six peaks in the GC-MS spectrum were identified, showing the presence of six different methyl esters such as methyl palmitate, methyl linoleate, methyl oleate, methyl stearate, methyl linolenate and methyl 11-eicosenoate. In addition, 1HNMR and 13CNMR confirmed the high conversion yield of the esters group with distinct peaks at 3.649 ppm and 174.19 ppm. Biodiesel prepared from Bischofia javanica has fuel qualities that meet international standards. Fuel properties were found analogous to international standards viz. ASTM and EN. These include flash point (80 degrees C), density at 15 degrees C (0.8623 kg/L), kinematic viscosity (5.32 mm2/s), cloud (-11 degrees C), pour point (-8 degrees C) and sulphur content of 0.00047 wt.%. The results indicate that the green nanocatalyst and synthesized biodiesel from the Bischofia javanica appear to be highly reliable and cost-effective candidates for producing sustainable and eco-friendly biodiesel to overcome energy crises and climatic deteriorations, which would assist in the shift from a linear to a circular economy.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Algorithm for the comprehensive thermal retrofit of housing stock aided by renewable energy supply: A sustainable case for Krakow
(Elsevier, 2023-01-15) Barnaś, Krzysztof; Jeleński, Tomasz; Nowak-Ocłoń, Marzena; Racoń-Leja, Kinga; Radziszewska-Zielina, Elzbieta; Szewczyk, Bartłomiej; Śladowski, Grzegorz; Toś, Cezary; Varbanov, Petar Sabev
This paper proposes an approach to the comprehensive adaptation of prefabricated panel-block buildings, many of which were built before 1989, to climate change and the requirements of people with special needs while alleviating Modernist planning deficiencies. The proposal targets panel-block technologies of Eastern Bloc countries, and its application is demonstrated based on the Polish W-70/Wk-70 system but can be applied to any other prefabricated housing. The large-scale use of such systems in Central and Eastern Europe after the Second World War, coupled with their service life being far longer than initially expected, means that they form sizeable parts of these regions’ housing stocks, which are often energy-inefficient and are hard to replace with new development. We propose a novel, structured approach to identifying buildings from this group using Geographic Information Systems (GIS), urban and social analysis, and Multi-Criteria Decision-Making support methods (MCDM) for comprehensive thermal retrofitting, combined with remodelling to address crucial deficiencies in accessibility and public space renewal. Our approach can aid in extending the utility of panel-block buildings in preparation for their eventual replacement. The model presented includes an energy audit of buildings, proposing measures to reduce their energy consumption. It is proposed to retrofit the mechanical ventilation and change the heating system to a significant share of renewable energy supply by applying the current method. This would allow the users to save up to 80% of their current energy consumption and related Greenhouse Gas emissions.
Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan
(MDPI, 2023-06-01) Sadenova, Marzhan; Beisekenov, Nail; Varbanov, Petar Sabev; Pan, Ting
The article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017-2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the optimal learning algorithms to obtain accurate predictions.
Review of Developments in Plate Heat Exchanger Heat Transfer Enhancement for Single-Phase Applications in Process Industries
(MDPI, 2023-07-30) Arsenyeva, Olga; Tovazhnyansky, Leonid; Kapustenko, Petro; Klemeš, Jiří; Varbanov, Petar Sabev
A plate heat exchanger (PHE) is a modern, effective type of heat transfer equipment capable of increasing heat recuperation and energy efficiency. For PHEs, enhanced methods of heat transfer intensification can be further applied using the analysis and knowledge already available in the literature. A review of the main developments in the construction and exploration of PHEs and in the methods of heat transfer intensification is presented in this paper with an analysis of the main construction modifications, such as plate-and-frame, brazed and welded PHEs. The differences between these construction modifications and their influences on the thermal and hydraulic performance of PHEs are discussed. Most modern PHEs have plates with inclined corrugations on their surface that create a strong, rigid construction with multiple contact points between the plates. The methods of PHE exploration are mostly experimental studies and/or CFD modelling. The main corrugation parameters influencing PHE performance are the corrugation inclination angle in relation to the main flow direction and the corrugation aspect ratio. Optimisation of these parameters is one way to enhance PHE performance. Other methods of heat transfer enhancement, including improving the form of the plate corrugations, use of nanofluids and active methods, are considered. Future research directions are proposed, such as improving fundamental understanding, developing new corrugation shapes and optimisation methods and area and cost estimations.
Melamine-isatin tris Schiff base as an efficient corrosion inhibitor for mild steel in 0.5 molar hydrochloric acid solution: weight loss, electrochemical and surface studies
(Royal Society of Chemistry, 2023-06-22) Arshad, Ifzan; Qureshi, Khizar; Saleemi, Awais Siddique; Abdullah, Ali; Bahajjaj, Aboud Ahmed Awadh; Ali, Shafaqat; Bokhari, Syed Awais Ali Shah
In the current study, 3,3 & PRIME;,3 & PRIME;& PRIME;-((1,3,5-triazine-2,4,6-triyl)tris(azaneylylidene))tris(indolin-2-one) (MISB), which is the condensation product of melamine (triazine) and isatin, was investigated as a mild steel corrosion inhibitor in 0.5 M HCl. The ability of the synthesized tris-Schiff base to suppress corrosion was evaluated utilizing weight loss measurements, electrochemical techniques and theoretical computation. The maximum inhibition efficiency of 92.07%, 91.51% and 91.60% was achieved using 34.20 x 10(-3) mM of MISB in weight loss measurements, polarization, and EIS tests, respectively. It was revealed that an increase in temperature decreased the inhibition performance of MISB, whereas an increase in the concentration of MISB increased it. The analysis demonstrated that the synthesized tris-Schiff base inhibitor followed the Langmuir adsorption isotherm and was an effective mixed-type inhibitor, but it exhibited dominant cathodic behavior. According to the electrochemical impedance measurements, the R-ct values increased with an increase in the inhibitor concentration. The weight loss and electrochemical assessments were also supported by quantum calculations and surface characterization analysis, and the SEM images showed a smooth surface morphology.

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