Textile wastewater accommodates many toxic organic contaminants which could potentially threaten the ecosystem if left untreated. Methylene blue is a toxic, nondegradable, cationic dye which is reportedly found in significant amounts in the textile effluent stream as it is widely used to dye silk and cotton fabrics. This study reports an investigation of methylene blue removal using a composite membrane fabricated using chitosan and graphene oxide. The fabricated composite membrane was characterized using Scanning Electron Microscopy, FTIR Spectroscopy, Raman Spectroscopy, UV vis spectroscopy, and X ray Diffraction. The isotherm modelling conducted confirmed a maximum adsorptive capacity of 179 mg/g which was well fitted with the Langmuir isotherm model indicating a homogenous monolayer adsorption.
Demand for organic food is rapidly increasing. This is despite several constraints in the process of producing organic foods. Producers should aim to maximize their profit by catering to the rising demand of this niche market. Manufacturing cost per unit is comparatively higher in the organic chain compared to the conventional chain. Hence, producers must make critical decisions when supplying their products to different markets. A Python-based Linear Programming optimization model tested using Google optimization Tools has been developed to identify the optimum delivery volume that should be supplied to each market which has been identified in this empirical study. The designed model aims to maximize profits by minimizing unsold products and postharvest waste. The developed model can guide producers who operate in the organic perishable supply chain to gain market benefits in short food supply chains. There is a lack of research on sustainability aspects in agricultural coordination and applications in supply network performance. Therefore, this study fills this gap by addressing the issue of postharvest waste in the distribution process of organic vegetables and fruits. The model can be extended into other product variants to validate the model’s applicability under different market scenarios.
Due to the increase in electricity demand and the rapid depletion of fossil fuels, energy management has become a critical issue over the last two decades. Thus, researchers, utility suppliers, governments, and policymakers are working in tandem to develop novel solutions. In recent years, solutions based on Intrusive Load Monitoring (ILM) and Non-intrusive Load Monitoring (NILM) have garnered the interest of many researchers. However, NILM systems are less difficult to implement and more cost-effective than ILM systems. Even though available NILM-based solutions can identify single-state devices with acceptable accuracy, identifying the various operating states of multi-state devices remains a problem. This research work proposes a novel supervised learning algorithm to correctly identify the operating states of multi-state residential devices. Results obtained through extensive simulations indicate that the proposed algorithm can achieve device and state identification accuracy of 93 percent and 91 percent, respectively.
Mobile users may experience frequent service outages or poor quality of service in rural areas due to the scarcity of base stations in the proximity. Therefore, a cost effective and flexible solution is introduced in this paper with unmanned aerial vehicles (UAVs) and terrestrial base stations (TBSs), coexisting under a stochastic network with the combination of millimeter waves and non-orthogonal access (NOMA) techniques. This is achieved by deploying both the UAVs (millimeter wave frequencies) and TBSs (sub-6 GHz frequencies) in the same system, and selecting the transmitter based on a user association scheme. The maximum signal-to-interference-plus-noise ratio (SINR) and the closest transmitter user association schemes are compared using extensive simulations to identify the most suitable scheme in terms of the outage probability and the achievable downlink rate. The results show that the closest transmitter user association policy results in lower outage probability and higher downlink rates for UAV-assisted millimeter wave NOMA Networks.
The adequacy evaluation of modern renewable-rich power systems tends to be a computationally challenging task due to variations of renewable power generation. Recently, more computationally efficient evolutionary algorithms and swarm intelligence-based methods are utilized for evaluating the adequacy of power systems. In this paper, the authors have proposed a wind and solar integrated composite system adequacy evaluation framework using an Evolutionary Swarm Algorithm (ESA). The system failure states which have a higher probability of occurrence are explored using the ESA to estimate the adequacy indices of the system. The wind and solar power generation are modeled using a clustering-based method considering their annual effective power output. Moreover, the correlation between the system load and renewable power generation is modeled in the adequacy evaluation framework. Using the proposed framework, several case studies are conducted on the IEEE Reliability Test System to analyze the impact of integrating renewables on the adequacy of composite systems. The results show that wind generation tends to improve system reliability than solar due to its higher availability. In addition, the equivalent capacities of wind and solar generators are found to be 125MW and 215MW against a 50MW hydro generator.
