Wasan Abdulrazzaq Wali
@uobasrah.edu.iq
university of basrah/ college of engineering / computer engineering department
university of basra
RESEARCH INTERESTS
control engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Atheel K. Abdul Zahra and Wasan A. Wali
University of Basrah - College of Engineering
This article emphasizes on a strategy to design a Super Twisting Sliding Mode Control (STSMC) method. The proposed controller depends on the device of Field Programmable Gate Array (FPGA) for controlling the trajectory of robot manipulator. The gains of the suggested controller are optimized using Chaotic Particle Swarm Optimization (PSO) in MATLAB toolbox software and Simulink environment. Since the control systems speed has an influence on their stability requirements and performance, (FPGA) device is taken in consideration. The proposed control method based on FPGA is implemented using Xilinx block sets in the Simulink. Integrated Software Environment (ISE 14.7) and System Generator are employed to create the file of Bitstream which can be downloaded in the device of FPGA. The results show that the designed controller based of on the FPGA by using System Generator is completely verified the effectiveness of controlling the path tracking of the manipulator and high speed. Simulation results explain that the percentage improvement in the Means Square Error (MSEs) of using the STSMC based FPGA and tuned via Chaotic PSO when compared with the same proposed controller tuned with classical PSO are 17.32 % and 13.98 % for two different cases of trajectories respectively.
Aliaa H. Abbas and Wasan A. Wali
IEEE
Discussing the impact of carbon dioxide on the environment and whether it is beneficial or harmful depends on its source. Human activity sources have led to excessive release of carbon dioxide, contributing to global warming, climate change and health issues. The use of non-thermal plasma can convert carbon dioxide into a useful fuel through the production of synthetic natural gas (syngas). Synthetic gas is a mixture of hydrogen and carbon monoxide. This paper presents a simulation of a cylindrical microwave plasma based on COMSOL Multiphysics software to produce carbon monoxide (CO) from the dissociation of carbon dioxide through pure and gaseous mixture streams. Most the studies simulated a two-dimensional model with different shapes. In this research, a three-dimensional simulation of a cylindrical reactor will be presented as the powerful way to visualize objects and environments realistically. The simulation covers a range of scenarios, including high carbon dioxide concentrations, the introduction of argon gas, and coupling with other ionization and surface reactions.
Marwa H. Abed, Wasan A. Wali, and Musaab Alaziz
Institute of Advanced Engineering and Science
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.
Marwa Abed, Wasan Wali, and Musaab Alaziz
University of Basrah - College of Engineering
Due to the changing flow conditions during the pipeline’s operation, several locations of erosion, damage, and failure occur. Leak prevention and early leak detection techniques are the best pipeline risk mitigation measures. To reduce detection time, pipeline models that can simulate these breaches are essential. In this study, numerical modeling using COMSOL Multiphysics is suggested for different fluid types, velocities, pressure distributions, and temperature distributions. The system consists of 12 meters of 8-inch pipe. A movable ball with a diameter of 5 inches is placed within. The findings show that dead zones happen more often in oil than in gas. Pipe insulation is facilitated by the gas phase’s thermal inefficiency (thermal conductivity). The fluid mixing is improved by 2.5 m/s when the temperature is the lowest. More than water and gas, oil viscosity and dead zones lower maximum pressure. Pressure decreases with maximum velocity and vice versa. The acquired oil data set is utilized to calibrate the Support Vector Machine and Decision Tree techniques using MATLAB R2021a, ensuring the precision of the measurement. The classification result reveals that the Support Vector Machine (SVM) and Decision Tree (DT) models have the best average accuracy, which is 98.8%, and 99.87 %, respectively.
Wisal Adnan Al-Musawi, Mohammed Abd Ali Al-Ibadi, and Wasan A. Wali
Institute of Advanced Engineering and Science
<span lang="EN-US">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.</span>
Wisal Adnan Al-Musawi, Wasan A. Wali, and Mohammed Abd Ali Al-Ibadi
Institute of Advanced Engineering and Science
<p>This study aims to design a new architecture of the artificial neural networks (ANNs) using the Xilinx system generator (XSG) and its hardware co-simulation equivalent model using field programmable gate array (FPGA) to predict the behavior of Chua’s chaotic system and use it in hiding information. The work proposed consists of two main sections. In the first section, MATLAB R2016a was used to build a 3×4×3 feed forward neural network (FFNN). The training results demonstrate that FFNN training in the Bayesian regulation algorithm is sufficiently accurate to directly implement. The second section demonstrates the hardware implementation of the network with the XSG on the Xilinx artix7 xc7a100t-1csg324 chip. Finally, the message was first encrypted using a dynamic Chua system and then decrypted using ANN’s chaotic dynamics. ANN models were developed to implement hardware in the FPGA system using the IEEE 754 Single precision floating-point format. The ANN design method illustrated can be extended to other chaotic systems in general.</p>
Marwa H. Abed, Wasan A. Wali, and Musaab Alaziz
IEEE
During the pipeline lifecycle, the impact of design, welding fractures, corrosion, and other issues cause pipeline leaks, leading to considerable economic losses and environmental pollution. Internal leak detection localization systems are sensitive to oil, gas, water, and multiproduct pipelines. Under steady circumstances, conventional leak detection methods are often employed to calculate the likelihood of a leak based on fluid flow variations at each inlet and outlet. Leaks occur when operational changes create transitory circumstances that cause local pressure and flow variations. An effective monitoring system must be in place and evaluated regularly for accurate leak detection, even under transitory settings. This paper discusses preliminary numerical modeling targeted at creating inspection techniques for pipe leakage detection utilizing the acoustic physics of a smart ball sensor and COMSOL Multiphysics 5.6. The simulation examines the effects of flow regime, leak location, ball placement, ball diameter, and leakage noise on the propagation of sound pressure levels inside the pipe and around the sphere. The simulated data is utilized to fine-tune the control strategy inside the mobility test ball. According to the research, water is more susceptible to sound energy than other fluids. The influence of oil on sound energy is larger. The ball diameter affects the fluid’s hydrodynamics, although the diameter is readily used for oscillation reflection.
Wisal A. Al-Musawi, W.A. Wali, and Mohammed A. Al-Ibadi
IEEE
Chaos is one of the major subjects in non-linear science and has been widely studied extensively since the discovery of the Lorenz system. The Chua circuit is a nonlinear circuit characterized by its simplicity and chaotic behavior. It involves a non-linear term initially described by a piece-wise-linear function and displays very rich and common bifurcation and chaos phenomena such as double scroll and multi-scroll. In this article, we use the Xilinx System Generator (XSG) software to simulate the Chua circuit for generating double-scroll and multi-scroll. Design models are produced with 32-bit fixed-point data formats with a fraction of 16 bits, with a clock step size of 0.01 (dt) and implemented on an FPGA to evaluate design performance, including maximum operating clock frequency, resource utilization, and power consumption. This architecture is implemented on the FPGA device Artix7 xc7a100t-1csg324. The results of the hardware co-simulation of the FPGA show the expected outputs of the chaotic generator by Chua.
W. A. Wali
Institute of Advanced Engineering and Science
The predictions for the original chaos patterns can be used to correct the distorted chaos pattern which has changed due to any changes whether from undesired disturbance or additional information which can hide under chaos pattern. This information can be recovered when the original chaos pattern is predicted. But unpredictability is most features of chaos, and time series prediction can be used based on the collection of past observations of a variable and analysis it to obtain the underlying relationships and then extrapolate future time series. The additional information often prunes away by several techniques. This paper shows how the chaotic time series prediction is difficult and distort even if Neuro-Fuzzy such as Adaptive Neural Fuzzy Inference System (ANFIS) is used under any disturbance. The paper combined particle swarm (PSO) and (ANFIS) to exam the prediction model and predict the original chaos patterns which comes from the double scroll circuit. Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.
W. A. Wali
IEEE
Carbon dioxide content in the atmosphere increases year by year and the seriousness of carbon dioxide emission has increased consequently. Fires, explosions and burnings at refineries, oil depots, and chemical stores. Moreover, due to the use of poor fuels in transportation, generators and heating systems, the dimensions of these negative consequences continue. This paper presents the carbon dioxide conversion control based on microwave plasma technology to reduce concentrations of carbon dioxide in the atmosphere and convert it to useful materials and maintain the dynamic equilibrium of carbon dioxide formation and conversion. Paper presents the argon gas flow rate controller to decrease the argon gas flow gradually on plasma microwave while ensuring the CO2 decomposes without or with a minimum amount of argon gas which this the most difficult step in the work.
W.A. Wali, K.H. Hassan, J.D. Cullen, A. Shaw, and A.I. Al-Shamma’a
Elsevier BV
Abstract In this paper, a comparison between intelligent controllers (Fuzzy Logic, Neuro-Fuzzy and Adaptive), when they applied for a novel advanced microwave biodiesel reactor to produce the biodiesel not from pure oil but also from waste cooking oil and fats. This novel microwave reactor is designed to be capable of operating at commercial production rates (kg/h) instead of laboratory scale (g/day), and reduces chemical reaction time, from hours under conventional heating, to minutes. In this reactor, as modern chemical process involves high temperatures, high pressures and catalysts so the process flow is very complicated. It is well known that traditional controllers cannot achieve a good performance when the process to be controlled is characterized by high nonlinearities and parameter uncertainties so it requires the efficient control to handle these multivariable problems as well as to adapt to time varying dynamics. The designed controllers were based on LabVIEW to automatically and continuously adjust the applied power of microwave reactor under different perturbations, and full system real time monitoring.
W.A. Wali, A.I. Al-Shamma’a, Kadhim H. Hassan, and J.D. Cullen
Elsevier BV
Abstract Biofuels, such as biodiesel, are good for the environment because they add fewer emissions to the atmosphere than petroleum-based fuel. Conventional biodiesel processes are mainly based on use of high power thermal heating to produce biodiesel from pure or waste feedstock such as virgin vegetable oils or waste cooking oils. The development of a novel continuous microwave biodiesel reactor for the conversion of waste oil and fats into biodiesel is reported. This process has the capability to enhance the production of biodiesel in a very short time as compared with conventional methods that require lengthy hours and days. Real time monitoring and control process in microwave biodiesel reactor is necessary to adjust the applied power of microwave reactor under different perturbations for the process temperature control, and full system real time monitoring. The paper focuses on an artificial intelligence technique to design online genetic-ANFIS temperature control based on LabVIEW. The designed controller was compared with error-based Adaptive controller to explore the robustness of the proposed controller in nonlinear real time application.
W. A. Wali, J. D. Cullen, K. H. Hassan, A. Mason, and A. I. Al-Shamma'a
IEEE
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.
W. A. Wali, J. D. Cullen, K. H. Hassan, A. Mason, and A. I. Al-Shamma'a
IEEE
The proportional integral derivative (PID) controllers are widely applied in industrial process owing to their simplicity and effectiveness for both linear and nonlinear systems, and the tuning methods still a hot research area to give the optimum result for control behavior. Fuzzy logic and proportional integral derivative (PID) controllers are designed and compared for use in control on-line an advanced biodiesel microwave reactor system which converts waste cooking oil into biodiesel by transesterification chemical process. A closed loop fuzzy and PID 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, that will make charts real time display as well as state real time monitoring.
W A Wali, K H Hassan, J D Cullen, A I Al-Shamma'a, A Shaw, and S R Wylie
IOP Publishing
Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.