A new biomass-based hybrid energy system integrated with a flue gas condensation process and energy storage option: An effort to mitigate environmental hazards

This study presents a novel approach to kidney stone diagnosis using convolutional neural networks (CNN), applied to computed tomography (CT) images. The research addresses the challenge of data imbalance and protocol variation in medical imaging, which often leads to poor generalization of deep learning models. The model first uses three preprocessing techniques to enhance the quality of raw images and increase their quantity for effective CNN training. The main idea is to optimize the main arrangement of the convolutional neural network based on the proposed flexible version of dwarf mongoose optimization (FDMO) algorithm to provide a good detector model in kidney stone diagnosis. The model is then trained and tested on images from “CT Kidney Dataset”, and its comparison results with some other published works demonstrates its robustness and ability to generalize. The results indicate a significant improvement in diagnostic accuracy, potentially minimizing physician-induced errors and enhancing patient care. 

Hybrid convolutional neural network and Flexible Dwarf Mongoose Optimization Algorithm for strong kidney stone diagnosis

Coastal cities can generate electricity via wind and solar farms. However, the variable nature of these renewable sources makes power output fluctuate. Compressed air energy storage (CAES) is the best solution to address this issue. On the other hand, the challenge of providing potable water persists, even in coastal cities, despite the proximity to the sea. Therefore, reverse osmosis (RO) technology has been used to address this issue due to its reduced energy consumption and environmental impact. The waste heat from the CAES is recommended to increase the system's overall efficiency. In this regard, an innovative green hybrid multi-generation system based on a CAES is proposed. The organic Rankine cycle ORC and RO desalination system have been used in this system to recover waste heat and supply potable and domestic hot water. A comprehensive analysis of the proposed system includes energy, exergy, economic, exergoeconomic, and advanced exergy analyses. The findings indicate this system can produce 37.48 MWh of electricity, 19.42 kg/s of potable water, and 2.67 kg/s of domestic hot water. Furthermore, with a total profit of $ 24.54 M and a payback period of 3.3 years, this system's energy and exergy round trip efficiencies are 73.54 % and 55.01 %, respectively. According to the advanced exergy analysis of the system, 31.5 % of the total exergy destruction is avoidable. 

A comprehensive study of a green hybrid multi-generation compressed air energy storage system for sustainable cities: Energy, exergy, economic, exergoeconomic, and advanced exergy analysis

ABSTRACT PEMFC (the proton exchange membrane fuel cell) is an extremely effective energy converter system that generates electricity through electrochemical reactions. Optimizing the design of PEMFC stacks is important for their proper utilization and construction. In the current essay, we present a novel strategy for estimating optimal model parameters of proton exchange membrane fuel cell stacks utilizing the COSA (Combined Owl Search Algorithm), an improved metaheuristic algorithm. We compared the performance of COSA against other published methods, comprising COA, TLBO, BH, the original OSA, and BBO by employing them in three different cost functions. The results show that COSA achieved the minimum error values compared to the other algorithms for all case studies, with error values of 0.01, 1.20, and 0.93 for BCS 500W PEMFC, NedStackPS6, and SR-12 500 W PEMFC stack models, respectively. Our proposed approach could be utilized to optimize other kinds of fuel cells and integrated with other optimization strategies to improve its upcoming efficiency. Future work can focus on simplifying the algorithm structure to reduce its complexity during the optimization process. Overall, the COSA algorithm provides an effective tool for achieving more efficient and optimal fuel cell stacks.

Optimal parameters estimation of the proton exchange membrane fuel cell stacks using a combined owl search algorithm

Fuel cells are a newly developed source for generating electric energy. These cells produce electricity through a chemical reaction between oxygen and hydrogen, which releases electrons. In recent years, extensive research has been conducted in this field, leading to the emergence of high-power batteries. This study introduces a novel technique to enhance the power quality of grid-connected proton-exchange membrane (PEM) fuel cells. The proposed approach uses an inverter following a buck converter that reduces voltage. A modified pelican optimization (MPO) algorithm optimizes the controller firing. A comparison is made between the controller’s performance, based on the recommended MPO algorithm and various other recent approaches, demonstrating the superior efficiency of the MPO algorithm. The study’s findings indicate that the current–voltage relationship in proton-exchange membrane fuel cells (PEMFCs) follows a logarithmic pattern, but becomes linear in the presence of ohmic overvoltage. Furthermore, the PEMFC operates at an impressive efficiency of 60.43% when running at 8 A, and it can deliver a significant power output under specific operating conditions. The MPO algorithm surpasses other strategies in terms of efficiency and reduction in voltage deviation, highlighting its effectiveness in managing the voltage stability, and improving the overall performance. Even during a 0.2 sagging event, the MPO-based controller successfully maintains the fuel cell voltage near its rated value, showcasing the robustness of the optimized regulators. The suggested MPO algorithm also achieves a superior accuracy in maintaining the voltage stability across various operating conditions.

Optimal Design of the Proton-Exchange Membrane Fuel Cell Connected to the Network Utilizing an Improved Version of the Metaheuristic Algorithm

Multi-criteria evaluation and optimization of a novel thermodynamic cycle based on a wind farm, Kalina cycle and storage system: An effort to improve efficiency and sustainability

Based upon the extended projective Riccati equations method, we investigate the dispersive long wave equation (DLWE) using the symbolic computation system. Several fami- lies of analytical solutions are obtained, including some new and more general ones.

/pdf/exact-solutions-to-the-dispersive-long-wave-equation-1dcyvkcsol.pdf

Exact Solutions to the Dispersive Long Wave Equation

Composition and Signification of Stable Carbon Isotope of Island Plants: A Case Study from Weizhou Island

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Multi-criteria evaluation and optimization of a novel thermodynamic cycle based on a wind farm, Kalina cycle and storage system: An effort to improve efficiency and sustainability

Exact Solutions to the Dispersive Long Wave Equation

Composition and Signification of Stable Carbon Isotope of Island Plants: A Case Study from Weizhou Island