The work in this paper extends a memristive chaotic system with transcendental nonlinearities to the fractional-order domain. The extended system’s chaotic properties were validated through bifurcation analysis and spectral entropy. The presented system was employed in the substitution stage of an image encryption algorithm, including a generalized Arnold map for the permutation. The encryption scheme demonstrated its efficiency through statistical tests, key sensitivity analysis and resistance to brute force and differential attacks. The fractional-order memristive system includes a reconfigurable coordinate rotation digital computer (CORDIC) and Grünwald–Letnikov (GL) architectures, which are essential for trigonometric and hyperbolic functions and fractional-order operator implementations, respectively. The proposed system was implemented on the Artix-7 FPGA board, achieving a throughput of 0.396 Gbit/s. © 2023 by the authors.
Parameter Identification of Li-ion Batteries: A Comparative Study
Lithium-ion batteries are crucial building stones in many applications. Therefore, modeling their behavior has become necessary in numerous fields, including heavyweight ones such as electric vehicles and plug-in hybrid electric vehicles, as well as lightweight ones like sensors and actuators. Generic models are in great demand for modeling the current change over time in real-time applications. This paper proposes seven dynamic models to simulate the behavior of lithium-ion batteries discharging. This was achieved using NASA room temperature random walk discharging datasets. The efficacy of these models in fitting different time-domain responses was tested through parameter identification with the Marine Predator Algorithm (MPA). In addition, each model’s term’s impact was analyzed to understand its effect on the fitted curve. The proposed models show an average absolute normalized error as low as (Formula presented.). © 2023 by the authors.
Potentials of algae-based activated carbon for the treatment of M.orange in wastewater
Activated carbon is a promising material with high efficiency in dye removal from polluted wastewater. However, commercial activated carbon is expensive and generates black color in the medium. Therefore, searching for low-cost, eco-friendly activated carbon sources such as agricultural wastes and algal biomasses is essential. Hence, this study is directed to prepare the physical and the H3PO4 chemical activated carbon from the algae ”Sargassum dent folium” and the raw algae itself and apply it for Methyl Orange (M. orange) removal from contaminated wastewater and compare its performance with the commercial activated carbon. First, adsorbent materials are prepared and involved in the optimization process for M. orange removal using some preliminary experiments, followed by Response Surface Method-ology (RSM) and Artificial Neural Network (ANN). Finally, Isotherm and kinetics are studied to explain the adsorption mechanism. In contrast to other materials, results show that physical algae-activated carbon achieves the maximum removal efficiency of 96.687%. These results are obtained from ANN combined with Moth Search Algorithm (MSA), representing the most effective model for achieving the highest M. orange removal efficiency from Physical algae activated carbon. In the algae case, the best experimental and predicted removal efficiencies are 85.9407 RE%, 88.5 indicated RSM RE%, and 85.9431 predicted ANN RE%. The best observed and predicted removal efficiencies for the H3PO4 chemical activated carbon are 89.6157 RE%, 82.38 predicted RSM RE%, and 89.5442 predicted ANN RE%. The best experimental and predicted removal efficiencies for the physical-activated carbon are 94.7935 RE%, 95.49 indicated RSM RE%, and 95.4298 predicted ANN RE%. The best observed and predicted removal efficiencies for the commercial-activated carbon are 92.2659 RE%, 96.65 predicted RSM RE%, and 92.2658 predicted ANN RE%. In the algae case, the best experimental and predicted removal efficiencies are 85.9407 %RE, 88.5 predicted RSM RE %, and 85.9431 expected ANN RE%. For the H3PO4 chemical activated carbon, the best experimental and predicted removal efficiencies are 89.6157%RE, 82.38 indicated RSM RE%, and 89.5442 predicted ANN RE%. For the physical-activated carbon, the best observed and predicted removal efficiencies are 94.7935 %RE, 95.49 predicted RSM RE%, and 95.4298 indicated ANN RE%. For the commercial-activated carbon, the best experimental and predicted removal efficiencies are 92.2659 %RE, 96.65 predicted RSM RE%, and 92.2658 predicted ANN RE%. This study intends to treat industrial wastewater contaminated with the anionic M. orange dye using raw algae and their generated activated carbon (physical and chemical forms), which are economical. It then compares the results to the effectiveness of commercial activated carbon. In the state of the raw algae, Temkin and Langmuir isotherm models best suit the data, while Temkin agrees well with the data from physical-activated carbon. Temkin and Freundlich’s models are fitted with the H3PO4 chemical activated carbon. The model that fits the raw algae physically activated carbon and H3PO4 chemical-activated carbon the best is pseudo-second-order kinetics. Future research could examine the produced activated carbon-based algae’s capacity to extract more contaminants from contaminated wastewater. This study intends to treat industrial wastewater contaminated with the anionic M. orange dye using raw algae and their generated activated carbon (physical and chemical forms), which are economical. It next compares the results to the effectiveness of commercial activated carbon. © 2023 The Authors
Smart Irrigation Systems: Overview
Countries are collaborating to make agriculture more efficient by combining new technologies to improve its procedure. Improving irrigation efficiency in agriculture is thus critical for the survival of sustainable agricultural production. Smart irrigation methods can enhance irrigation efficiency, specially with the introduction of wireless communication systems, monitoring devices, and enhanced control techniques for efficient irrigation scheduling. The study compared on a wide range of study subjects to investigate scientific approaches for smart irrigation. As a result, this project included a wide range of topics related to irrigation methods, decision-making, and technology used. Information was gathered from a variety of scientific papers. So, our research relied on several published documents, the majority of which were published during the last four years, and authors from all over the world. In the meantime, various irrigation initiatives were given special attention. Following that, the evaluation focuses on the key components of smart irrigation, such as real-time irrigation scheduling, IoT, the importance of an internet connection, smart sensing, and energy harvesting. Author
Bio-inspired adsorption sheets from waste material for anionic methyl orange dye removal
Abstract: Nano zero-valent iron (nZVI), bimetallic nano zero-valent iron-copper (Fe0–Cu), and Raw algae (sargassum dentifolium) activated carbon-supported bimetallic nano zero-valent iron-copper (AC-Fe0–Cu) are synthesized and characterized using FT-IR, XRD, and SEM. The maximum removal capacity is demonstrated by bimetallic activated carbon AC-Fe0–Cu, which is estimated at 946.5 mg/g capacity at the condition pH = 7, 30 min contact time under shaking at 120 rpm at ambient temperature, 200 ppm of M.O, and 1 g/l dose of raw algae-Fe0–Cu adsorbent. The elimination capability of the H3PO4 chemical AC-Fe0–Cu adsorbent is 991.96 mg/g under the conditions of pH = 3, 120 min contact time under shaking at 120 rpm at room temperature, 200 ppm of M.O, and 2 g/l doses of H3PO4 chemical AC-Fe0–Cu adsorbent. The Bagasse activated carbon adsorbent sheet achieves a removal capacity of 71.6 mg/g MO dye solution. Kinetic and isothermal models are used to fit the results of time and concentration experiments. The intra-particle model yields the best fit for bimetallic Fe0–Cu, AC-Fe0–Cu, H3PO4 chemical AC-Fe0–Cu and bagasse activated carbon(CH), with corrected R-Squared values of 0.9656, 0.9926, 0.964, and 0.951respectively. The isothermal results emphasize the significance of physisorption and chemisorption in concentration outcomes. Response surface methodology (RSM) and artificial neural networks (ANN) are employed to optimize the removal efficiency. RSM models the efficiency and facilitates numerical optimization, while the ANN model is optimized using the moth search algorithm (MSA) for optimal results. Highlights: 1.The Fe0–Cu composite, when combined with activated carbon from Bagasse Pulp (CH), exhibited the most effective decolorization effectiveness for anionic colours present in wastewater.2.The utilization of composites presents a promising opportunity for efficient dye removal due to its cost-effectiveness and environmentally sustainable nature. 3.The utilization of response surface approach and artificial neural network modelling improves the efficacy of removal processes and treatment techniques. © 2023, The Author(s).