Image encryption is an important issue in protecting the content of images and in the area of information security. This article proposes a novel method for image encryption and decryption using the structure of the artificial neural network (ANN)-based chua chaotic system (CCS). This structure was efficiently designed on a field-programmable gate array (FPGA) chip utilizing the xilinx system generator (XSG) tool with the IEEE-754-1985 32-bit floating-point number format. For ANN-based CCS design, a multilayer feed forward neural network (FFNN) structure with three inputs and three outputs was created. This structure consists of one hidden layer with four neurons, each of which has a Tangent Sigmoid activation function. The training of ANN-based CCS yielded a 3.602e-13 mean square error (MSE) value. After successfully training the ANN-based CCS, the design was carried out on FPGA, utilizing the ANN structure's bias and weight values as a reference. The xilinx vivado (2017.4) design suite was used to synthesis and test the ANN-based CCS on the FPGA. The histogram, correlation coefficient, and entropy are used to perform security analysis on various images. Finally, FPGA hardware co-simulation using a Xilinx Artix7 xc7a100t-1csg324 chip was utilized to verify that the encryption and decryption of the images were successful.

Recently, pipelines have replaced more carbon-intensive transportation methods making them more environmentally friendly for transporting energy and water supplies. However, pipelines can pollute the air, water, soil, and climate when they leak, causing economic, and environmental damage. Pipeline online monitoring provides data analysis and suitable controlling strategies to contain the risk. This paper proposes a three-dimensional numerical model simulation taking advantage of the fluids moving through pipelines at specific speeds. The transport speeds depend on many conditions, such as pipe diameter, the pressure through which the fluid is being transported, and other factors, such as terrain's topography and viscosity of the fluid. Under these conditions, the inspection approach uses a self-charging movable ball. The sensors inside the ball capture data as it travels through the pipe. The simulation focuses on spherical flow and pipe noise with and without leakage based on the COMSOL software platform. The paper shows the effect of several parameters, including leak location, sensor placement, ball diameter, sound pressure level propagation along a pipe and around the sphere, velocity, and temperature distribution that give the background for future smart ball design in a promising practical pipeline test project.

The performance of pH controllers in industrial cooling towers is critical for maintaining optimal operational conditions and ensuring system efficiency. Industries such as the fertilizer, petrochemical, oil refinery, gas production, and power plant sectors rely on cooling towers, where poor pH regulation can lead to corrosion, scaling, and microbial growth. Traditional proportional–integral–derivative (PID) controllers are used for pH neutralization but often struggle with the cooling tower environments’ dynamic and nonlinear nature, resulting in suboptimal performance and increased operational costs. A hybrid particle swarm optimization (PSO) algorithm combined with a multiple adaptive neuro-fuzzy inference system (MANFIS) was developed to address these challenges. The MANFIS leverages fuzzy logic and neural networks to handle nonlinear pH fluctuations, while PSO improves the convergence speed and solution accuracy. This hybrid approach optimized the PID controller parameters for real-time adaptive pH control. The methodology involved collecting open-loop pH data, deriving the system transfer function, designing the PID controller, and implementing the PSO–MANFIS algorithm to fine-tune PID gains. Three tuning methods—MATLAB Tuner, MANFIS, and PSO–MANFIS—were compared. The findings proved that the PSO–MANFIS approach markedly enhanced the closed-loop efficiency by reducing overshoot and enhancing the dynamic response. These findings demonstrate that the PSO–MANFIS approach effectively maintains pH levels within desired limits, reduces energy consumption, and minimizes chemical usage and the risk of mechanical equipment damage. This study provided valuable insights into optimizing pH control strategies in industrial cooling tower systems, offering a practical solution for improving efficiency and reliability.

Biodiesel reactor is the heart of biodiesel system. These reactors involve a highly complex set of chemical reactions and heat transfers. The high nonlinearity requires an efficient control algorithm to handle the variation of operational process parameters and the effect of process disturbances efficiently. In this paper, Fuzzy logic and Adaptive controllers are compared for advance microwave biodiesel reactor. The process control is complex and nonlinear, the Adaptive control have longer time and unreliability in dealing with the system parameters including temperature, microwave power, liquid flow rate as well as the prediction of chemical reaction. The proposed fuzzy logic control will provide precise temperature control and faster warm-up phase with quicker response to disturbances with minimal overshoot and undershoot where Adaptive control techniques can not meet these extra challenges. A closed loop fuzzy and adaptive controllers are used to automatically and continuously adjust the applied power of microwave reactor under different perturbations. Labview based software tool will be presented and used for measurement and control of the full system, with real time monitoring.