Future Sustainability最新文献

{"title":"Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review","authors":"MD. Tanvir Amin, Wahid Bin Noor","doi":"10.55670/fpll.fusus.2.2.2","DOIUrl":"https://doi.org/10.55670/fpll.fusus.2.2.2","url":null,"abstract":"High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.","PeriodicalId":517009,"journal":{"name":"Future Sustainability","volume":" 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of satellite-derived solar irradiance datasets: a case study in Saudi Arabia","authors":"A.F. Almarshoud","doi":"10.55670/fpll.fusus.2.2.1","DOIUrl":"https://doi.org/10.55670/fpll.fusus.2.2.1","url":null,"abstract":"A robust dataset of Surface Solar Irradiance is essential for secure competitive financing for solar energy projects. Rating agencies and lenders alike require verification of the solar-resource dataset for utilizing each solar energy project, as this can be translated directly into expected electrical energy and revenues. The accuracy of the dataset and the variability of solar radiation, as recorded by historical solar data, play a significant role in estimating the future performance of the project and its budget. The historical observed solar irradiance datasets by local stations are the best and most reliable for a specific site, but they are not always available for long and continuous periods in any location, especially in arid areas. So, the importance of historical solar radiation datasets derived from satellite-based models arises here. This paper validates the historical modeled datasets of the three most famous satellite-based commercial prediction models (SolarGIS, SUNY, and Solcast) against the observed dataset by six ground stations in Saudi Arabia under different climatic zones. The validation method has been implemented using the standard error metrics: Maximum Absolute Error (MAE) and relative Maximum Bias Error (rMBE). The validation process showed that, in the case of GHI, the discrepancy between observed and predicted values is narrow, while in the case of DNI, the discrepancy is wide. Also, the predicted GHI values are more accurate than predicted DNI values, and -in general- the values predicted by the SUNY model are less accurate than those predicted by SolarGIS and Solcast models for both GHI and DNI. The resultant of this validation process could be accepted not for the six locations under study only but, also for deserts and arid areas across Saudi Arabia and might be extended to similar arid areas around the world.","PeriodicalId":517009,"journal":{"name":"Future Sustainability","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak","authors":"Geoffrey Tan, Hadi N. Afrouzi, Jubaer Ahmed, Ateeb Hassan, Firdaus M-Sukki","doi":"10.55670/fpll.fusus.2.2.3","DOIUrl":"https://doi.org/10.55670/fpll.fusus.2.2.3","url":null,"abstract":"Solar energy is one of the clean renewable energy sources that can offset the rising consumption of fossil fuels. However, the meteorological parameters, such as solar irradiance, ambient and solar module temperatures, relative humidity, etc., constantly change, and so does the solar power generation. Such variations cause instability in the power grid operation due to injecting an unpredicted amount of power. Hence, solar energy prediction models capable of learning from past weather data and predicting future energy generation are highly desired for grid operation and planning. The objective of this study is to determine the suitable meteorological parameters for the solar energy prediction model based on the Pearson correlation coefficient and to implement them in different machine learning models. It is found in this study that five meteorological parameters, namely Air temperature, cloud opacity, global tilted irradiance, relative humidity, and zenith angle, correlate highly with solar energy generation. Later, based on the correlations, four machine-learning models were implemented to predict the solar power for Kuching, Sarawak. The accuracy of the models is measured through standard matrices such as root mean square error, mean square error, mean absolute error, and R-squared value.","PeriodicalId":517009,"journal":{"name":"Future Sustainability","volume":"32 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Green hydrogen prospects in Peninsular Malaysia: a techno-economic analysis via Monte Carlo simulations","authors":"Mohammad Nurizat Rahman, Mazlan Abdul Wahid","doi":"10.55670/fpll.fusus.2.2.4","DOIUrl":"https://doi.org/10.55670/fpll.fusus.2.2.4","url":null,"abstract":"According to Malaysia's National Energy Transition Roadmap, hydrogen is a critical component of the country's energy transition. However, there is a scarcity of hydrogen studies for Peninsular Malaysian states, which limits discussions on green hydrogen production. This study employs a Monte Carlo model to assess the economic and technical factors influencing the success of green hydrogen in Peninsular Malaysia. The study focuses on three target years: 2023, 2030, and 2050, representing various stages of technological development and market adoption. The levelized cost of hydrogen (LCOH) of a 1-MW Proton Exchange Membrane (PEM) electrolyzer system ranges from $5.39 to $10.97 per kg in 2023, highlighting early-stage challenges and uncertainties. A 6-MW PEM electrolyzer system could achieve an LCOH of $3.50 to $4.72 per kg by 2030, indicating better prospects. Because of technological advancements and cost reductions, a 20-MW PEM electrolyzer system could achieve an LCOH of $3.12 to $3.64 per kg in 2050. The findings indicate that the northern regions of Peninsular Malaysia have consistently low LCOH values due to favorable geographical conditions. Due to minor variations in solar capacity factors, uncertainty distributions in LCOH remain stable across different regions. Some states may face increased uncertainty, emphasizing the need for additional policy support mechanisms to mitigate risks associated with green hydrogen investments. The sensitivity analysis shows that key cost drivers are shifting, with early-stage electrolyzer investments dominating in 2023 and electricity prices becoming more important in 2030 and 2050. Future research could focus on optimizing green hydrogen systems for areas with underdeveloped green hydrogen industries. This study contributes to informed discussions about green hydrogen production by emphasizing the importance of tailored strategies that consider local conditions and highlighting the role of Peninsular Malaysia in the energy transition.","PeriodicalId":517009,"journal":{"name":"Future Sustainability","volume":"81 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental study on combustion characteristics of billboard materials","authors":"X. Gui, Jiaojiao Wu, Zelin He, Xiange Song, Tian Liu, Jun Zhou","doi":"10.55670/fpll.fusus.2.1.3","DOIUrl":"https://doi.org/10.55670/fpll.fusus.2.1.3","url":null,"abstract":"Billboards are permanent facilities in large commercial buildings, indoor and outdoor public places, and in fire accidents, billboards will become the main cause of the expansion and spread of fires. To reduce the fire accidents caused by the burning of billboards, this paper conducted experimental tests on 12 commonly used billboard material types of Polyethylene glycol terephthalate (PET) and Polyvinyl chloride (PVC). Among the 12 materials, only PVC4 belongs to the range of flame-retardant materials. PVC6 has the lowest calorific value, less heat release, and a stronger fire effect than other materials. The flammability experiment shows that the ignition time of the material is positively correlated with the combustion height under the same ignition method, and the ignition time is also positively correlated with the combustion width. Under the same ignition time conditions, the flame aspect ratio using edge ignition is greater than or equal to the flame aspect ratio using surface ignition, and the fire hazard is greater. It is necessary to avoid the presence of combustibles around 250 mm to cause fire spread. The monomer combustion experiment shows that the flame spread area of PVC material is much larger than that of PET material. Among all materials, the most dangerous is PVC6, which releases the largest CO concentration and the fastest rate after combustion, produces the most flue gas within 100 after combustion and has poor flame-retardant performance. The combustion of all advertising materials releases less CO and CO2 concentration, which can cause physiological adverse reactions in the human body but will not cause death.","PeriodicalId":517009,"journal":{"name":"Future Sustainability","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning in high-entropy alloys: phase formation predictions with artificial neural networks","authors":"Md Fahel Bin Noor, Nusrat Yasmin, T. Besara","doi":"10.55670/fpll.fusus.2.1.5","DOIUrl":"https://doi.org/10.55670/fpll.fusus.2.1.5","url":null,"abstract":"Due to their complex compositions, high entropy alloys (HEAs) offer a diverse range of material properties, making them highly adaptable for various applications, including those crucial for future sustainability. Phase engineering in HEAs presents a unique opportunity to tailor materials for environmentally friendly technologies and energy-efficient solutions. However, the challenge of predicting phase selection, a key aspect in harnessing the full potential of HEAs for sustainable applications, is compounded by the limited availability of HEA data. This study presents a distinctive approach by using a precisely produced and selected dataset to train an artificial neural network (ANN) model. This dataset, unlike prior studies, is uniquely constructed to contain an equal amount of training data for each phase in HEAs, which includes single-phase solid solutions (SS), amorphous (AM), and intermetallic compounds (IM). This methodology is relatively unexplored in the field and addresses the imbalanced data issue common in HEA research. To accurately assess the model's performance, rigorous cross-validation was employed to systematically adapt the model's hyperparameters for phase formation prediction. The assessment includes metrics such as phase-wise accuracy (AM 86.67% SS 81.25% & IM 82.35%), confusion matrix, and Micro-F1 score (0.83), all of which collectively demonstrate the effectiveness of this approach. The study highlights the importance of feature parameters in phase prediction for HEAs, shedding light on the factors influencing phase selection. Its balanced dataset and training method notably advance machine learning in HEA phase prediction, providing valuable insights for material design amidst challenges and data scarcity in the field.","PeriodicalId":517009,"journal":{"name":"Future Sustainability","volume":"159 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microstructured pebble stone like Ni-NiO composite as anode of high-performance lithium-ion batteries","authors":"Safina-E-Tahura Siddiqui, Md. Arafat Rahman, Md. Saiful Islam, Jin-Hyuk Kim, Nirjhor Barua","doi":"10.55670/fpll.fusus.2.1.1","DOIUrl":"https://doi.org/10.55670/fpll.fusus.2.1.1","url":null,"abstract":"Ni-NiO electrodes were synthesized via thermal oxidation of pure nickel powder and evaluated as anode of lithium-ion batteries (LIBs). The composite synthesized at 600˚C, 800˚C, and 1000˚C exhibited nanochips, crushed gravel stone, and pebble stone-like morphology, respectively. The nanochips- and crushed gravel stone featured-like electrodes exhibited erratic behavior, and specific capacity faded rapidly from 754.49 mAh g-1 and 101.12 mAh g-1 to 464.04 mAh g-1 and 9.55 mAh g-1, respectively over 10th cycle at a current rate of 1C as the electrode experiences internal short circuit. The pebble stone-like Ni-NiO electrode exhibited improved and stable cyclic performance with 1st discharge capacity of 365.17 mAh g-1 and reduced to 67.42 mAh g-1 even after 40th cycle at 1C current rate. The improved electrochemical performance of composite Ni-NiO with a pebble stone-like feature can be attributed to the mechanical stability of the electrode, which can buffer volume expansion, and the presence of more nanoparticles on the electrode surface allows more interaction with Li+.","PeriodicalId":517009,"journal":{"name":"Future Sustainability","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139963424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The extraction process of nanocellulose from organic waste and incorporating it into biopolymers for mechanical property enhancement","authors":"Lamia Afrin Ratry, Md. Bazlul Mobin Siddique","doi":"10.55670/fpll.fusus.2.1.2","DOIUrl":"https://doi.org/10.55670/fpll.fusus.2.1.2","url":null,"abstract":"Nanocellulose possesses excellent properties such as an elastic modulus of 220 GPa, Young’s modulus of 10- 150 GPa, low density of around 1.6 g/cm3, and high thermal stability. To enhance mechanical flexibility, nanocellulose can strengthen the bio-polymers. This research project aims to review the extraction methods and the characterization of the nanocellulose extracted from organic waste materials such as banana peel, pineapple leaf fiber, crown, corncob, palm oil, etc., focusing on the possibility of adding the nanocellulose to enhance the properties such as tensile strength, young’s modulus, water vapor permeability of the biopolymers. Chemical extraction methods like alkaline treatment, bleaching treatment, sulfuric and formic acid hydrolysis, TEMPO-mediated oxidation, and mechanical extraction methods such as ball milling, ultrasonication, high-pressure homogenization, and grinding have been studied. The results obtained from all the characterization techniques have been tabulated. From the results tabulation, the length of cellulose nanocrystal and cellulose nanofiber is 100-350 nm and 350 nm and above, respectively. The hydrolysis time and the types of acid used will affect the yield and aspect ratio; the acid concentration will also affect the degradation temperature. Mechanical treatment results in a higher yield of the nanocellulose, but mechanical treatment is not economically solvent due to the heavy use of power. Considering that nanocellulose extracted via chemo-mechanical treatment has outstanding characteristics that can potentially improve the mechanical properties when incorporated into the biopolymers.","PeriodicalId":517009,"journal":{"name":"Future Sustainability","volume":"397 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A techno-economic investigation for utilizing solar energy in irrigation of palm trees in Saudi Arabia","authors":"A. Almarshoud","doi":"10.55670/fpll.fusus.2.1.4","DOIUrl":"https://doi.org/10.55670/fpll.fusus.2.1.4","url":null,"abstract":"This paper presents a techno-economic investigation for utilizing photovoltaic solar energy in water pumping applications for the irrigation of Palm trees in the Qassim region in Saudi Arabia. The Analysis has been done by applying four technical indicators and three economic indicators on a real farm of palm trees. The investigation took into account the varied water demand for palm trees over the years, meteorological data of the region, the characteristics of the borehole, and the local market prices of PV system components. The investigation has been done using two options of PV systems: grid-connected system and standalone system. The results showed the superiority of the grid-connected system in spite of the unfair price of energy exchange with the utility grid. The results achieved are the Levelled cost of energy, which is in the range from 0.013 to 0.019 $/kWh. The standardized cost of produced water is in the range from 0.011 to 0.013 $/m3, and the simple payback time is in the range from 9.65 to 12.22 years. The results are considered to encourage farmers in the region to convert to solar energy utilization.","PeriodicalId":517009,"journal":{"name":"Future Sustainability","volume":"494 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}