Journal of Polymers and the Environment - Poly ε-caprolactone (PCL) synthesized by ring-opening polymerization method, and then it blended with polylactic acid (PLA). The blend was loaded with... 相似文献
Journal of Material Cycles and Waste Management - Discarded dry fig and raisin, which exporting companies have to claim and destroy, were subjected to batch dark fermentation at 37 °C... 相似文献
Journal of Material Cycles and Waste Management - In this day and age, an important indicator of sustainable waste management is zero-waste index. Zero-waste approach is adopted by many... 相似文献
This study examines the use of fly ash, a thermal power plant waste, and the effect of diatomite, a fossil algae type, on waste-based geopolymers in the production of sustainable geopolymer binders. The effects of 1%, 2%, 3%, 4% and, 5% diatomite substitution on waste-based mortars were investigated. Mortars containing 10% and 12% Na+ by weight based on the binder material were cured at 75 °C for 48 h. The flexural and compressive strength, abrasion resistance, determination of ultrasonic pulse velocity, and resistance to high temperatures of geopolymer mortar samples were investigated. In addition, FESEM images, EDX and XRD analyses of geopolymer mortar samples were made, and their microstructures were examined. 2% diatomite substitution increased flexural and compressive strength. In parallel with this situation, it was concluded that the abrasion resistance and ultrasonic pulse velocity of the geopolymer mortar with 2% diatomite substituted increased. In addition, it has been shown in FESEM images that the microstructure has a denser morphology. All geopolymer mortars lost strength after the high temperatures of 300 °C, 600 °C and 900 °C. As a result, it was concluded that diatomite containing highly reactive silica can be used in geopolymer systems.
Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960–2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t?+?1 output variable by using t, t???1, t???2, and t???3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE?=?0.9345, OI?=?0.9438, R2?=?0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE?=?0.9528, OI?=?0.9559, R2?=?0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE?=?0.9398, OI?=?0.9483, R2?=?0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.
Journal of Material Cycles and Waste Management - Nowadays, textile waste arising from increasing clothing production, consumption, and disposal activities has led to environmental, social, and... 相似文献
Environmental Science and Pollution Research - The composition and abundance of solid waste and the effect of COVID-19 measures were studied in an inland water ecosystem in Turkey. Solid waste... 相似文献