Machine-learning approaches in geo-environmental engineering: Exploring smart solid waste management.

Over the past few decades, increased attention has been paid to domestic waste (DW) generation. DW comprises a large percentage of municipal solid waste (MSW), and its handling and processing involves serious technical issues while also consuming a major portion of municipal budgets. The accurate estimation, prediction, and characterization of DW is an ongoing challenge for many cities, municipalities, and local governments as they strive to implement sustainable strategies for MSW. The main objective of the present study is to estimate and correctly predict DW quantities using machine-learning (ML) algorithms. Several different ML algorithms are used in the research, including linear regression, regression trees, Gaussian process regression, support vector machine, and autoregressive integrated moving average methods for time series analysis. Two case studies are presented in this paper. In the first, domestic waste data covering the period from 2010 to 2021 were collected from the Saudi and Bahrain authorities, and in the second, the domestic waste-generating behavior of a family of eleven members was followed for one month. The results show that the biodegradable and non-biodegradable wastes generated by the family were in the range of 1.7-7.9 kg and 0.0-2.0 kg, respectively, and promising outcomes were obtained using an appropriate selection of input predictors in conjunction with time series analysis. The trained models are validated and tested using several types of evaluation metrics, including calculated residuals, mean square error, root mean square error, and coefficient determination (R2-Score). The latter values are in the range of 0.67-0.85 for the training and testing datasets for many of the predicted waste quantities. The results obtained from the study show that these algorithms can be used to reduce the environmental, economic, and societal impacts of waste by designing a smart waste management engineering system.

Thermal effect on curved photovoltaic panels: Model validation and application in the Tabuk region.

This paper aimed to investigate the temperature effect on photovoltaic (PV) cell parameters. The PV cell parameters such as series and parallel resistances, diode ideality factor, and diode saturation current, are not considered in the reported stepwise modeling. The present work aims to improve available models used in the modeling and simulation of PV modules to support the researcher and power project developer. All the required temperature-dependent parameters are determined to model the simulated PV module with high accuracy using Simulink/MATLAB software. To validate the method, a 36-cell-50W solar panel with different radii of curvature is set up to assess solar power outputs under varying irradiance and temperature conditions. For the present application, the Tabuk region (Saudi Arabia) is chosen based on its location and climatic conditions. The method provided conformity to the measured power outputs for varying Global Horizontal Irradiance (GHI) and temperature conditions. The maximum power output of the PV module increases from 14.4 W to 25.8 W when the received solar power density varies from 307 W/m2 to 526 W/m2 depending on the level of curvature starting from a semi-cylindrical shape to a vaulted shape to a flat shape. The curved PV module shows slightly higher power variation with temperature as compared to the flat one. Above 25°C, the power output is about 20% less at a maximum temperature of 65°C. When the temperature drops below 25°C, the power outputs increase about 6% and 11.5% for corresponding temperatures of 15°C and 5°C, respectively.

Fabrication and Characterization of Flexible Solid Polymers Electrolytes for Supercapacitor Application.

In this work, solid flexible polymer blend electrolytes (PBE) composed of polyvinyl alcohol (PVA) and polyvinyl pyrrolidone (PVP) with different amounts of sodium thiocyanate (NaSCN) salt mixed in double-distilled water (solvent) are prepared via solution casting method. The obtained films are characterized using several techniques. The study of the surface morphology of the polymer blend salt complex films via the POM technique reveals the presence of amorphous regions due to the NaSCN effect. FTIR spectra studies confirm the complex formation between PVA, PVP, and NaSCN. The addition of 20 wt% NaSCN salt in the composition PVA: PVP (50:50 wt%) polymer blend matrix leads to an increase in the number of charge carriers and thus improves the ionic conductivity. The ionic conductivity of each polymer blend electrolyte was studied using the electrochemical impedance spectroscopy (EIS) method. The highest room temperature ionic conductivity of 8.1 × 10-5 S/cm S cm-1 is obtained for the composition of PVA: PVP (50:50 wt%) with 20 wt% NaSCN. LSV test shows the optimized ion-conducting polymer blend electrolyte is electrochemically stable up to 1.5 V. TNM analysis reveals that 99% of ions contribute for the conductivity against 1% of electrons only in the highly conductive polymer electrolyte PVA: PVP (50:50 wt%) + 20 wt% NaSCN. A supercapacitor device was fabricated using the optimized ion-conducting polymer blend film and graphene oxide (GO) coated electrodes. The GCD curve clearly reveals the behavior of an ideal capacitor with less Faradic process and low ESR value. The columbic efficiency of the GO-based system is found to be 100%, the GO-based electrode exhibits a specific capacitance of 12.15 F/g and the system delivers the charge for a long duration. The specific capacitance of the solid-state supercapacitor cell was found to be 13.28 F/g via the CV approach close to 14.25 F/g obtained with EIS data at low frequency.

The Impact of Polymer Electrolyte Properties on Lithium-Ion Batteries.

In recent decades, the enhancement of the properties of electrolytes and electrodes resulted in the development of efficient electrochemical energy storage devices. We herein reported the impact of the different polymer electrolytes in terms of physicochemical, thermal, electrical, and mechanical properties of lithium-ion batteries (LIBs). Since LIBs use many groups of electrolytes, such as liquid electrolytes, quasi-solid electrolytes, and solid electrolytes, the efficiency of the full device relies on the type of electrolyte used. A good electrolyte is the one that, when used in Li-ion batteries, exhibits high Li+ diffusion between electrodes, the lowest resistance during cycling at the interfaces, a high capacity of retention, a very good cycle-life, high thermal stability, high specific capacitance, and high energy density. The impact of various polymer electrolytes and their components has been reported in this work, which helps to understand their effect on battery performance. Although, single-electrolyte material cannot be sufficient to fulfill the requirements of a good LIB. This review is aimed to lead toward an appropriate choice of polymer electrolyte for LIBs.

Evaluation of the efficiency of an experimental biocover to reduce BTEX emissions from landfill biogas.

Landfill emissions include volatile organic compounds (VOCs) and, particularly, benzene, toluene, ethyl-benzene and xylene isomers (collectively called BTEX). The latter are the most common VOCs found in landfill biogas. BTEX affect air quality and may be harmful to human health. In conjunction with a study aiming to evaluate the efficiency of passive methane oxidizing biocovers, a complementary project was developed with the specific goal of evaluating the reduction in VOC emissions due to the installation of a biocover. One of the biocovers constructed at the Saint-Nicéphore (Quebec, Canada) landfill site was instrumented for this purpose. The total BTEX concentration in the raw biogas ranged from 28.7 to 65.4ppmv, and the measured concentration of BTEX in biogas emitted through the biocover ranged from below the limit of detection (BLD) to 2.1ppmv. The other volatile organic compounds (OVOCs) concentration varied from 18.8 to 40.4ppmv and from 0.8 to 1.2ppmv in the raw biogas and in the emitted biogas, respectively. The results obtained showed that the biocover effectiveness ranged from 67% to 100% and from 96% to 97% for BTEX and OVOC, respectively.