Papers by Keyword: MATLAB

Abstract: The availability of electrical energy is essential for human progress and economic development. Renewable energy solutions, including waste-to-energy (WtE) systems, present sustainable alternatives but require advanced control strategies for optimal performance. This research aims to enhance the control of drum level, temperature, and pressure in WtE steam boilers at Ethiopia's Reppie power plant. The existing Programmable Logic Controller (PLC) system is limited in its ability to predict future states and handle nonlinear system behaviors. To overcome these challenges, a Radial Basis Function Autoregressive with Exogenous input (RBF-ARX) model was developed and integrated with a Model Predictive Controller (MPC). The results demonstrate that the MPC approach significantly surpasses the performance of the Linear Quadratic Regulator (LQR) in terms of control efficiency. For temperature control, the MPC achieves a settling time of 0.3955 seconds and a rise time of 0.0195 seconds, compared to LQR's 5.99 seconds. Similarly, for pressure control, the MPC achieves a settling time of 0.6678 seconds, outperforming the LQR's 12.507 seconds. Drum level regulation further showcases the superiority of MPC, with a settling time of 0.5223 seconds versus the LQR's 8.302 seconds. This proposed RBF-ARX-based MPC framework not only optimizes control efficiency at Reppie but also demonstrates scalability and applicability to other WtE plants, enhancing operational performance under varying conditions. MATLAB/Simulink was used for the modeling and simulation, confirming the robustness of this approach for global adoption in WtE systems.

Abstract: Global warming is a concern nowadays due to excessive release of harmful gasses to the environment, leading to greenhouse effect phenomena worldwide. Based on the data provided by global pollution agencies, the release of greenhouse gasses to the atmosphere is the main cause of pollution and the increase in atmospheric temperature due to warming. Greenhouse gasses (GHGs) contents released to the environment is worrying, with carbon dioxide (CO₂) is reported at the highest concentration compared to other gasses. There are many studies conducted to develop and evaluate the performance of harmful gas sensors incorporating inorganic and organic semiconductive materials. Organic semiconductors (OSCs) are environmentally friendly materials, relatively cheaper technology, and comprised of a wide range of materials with good carrier mobility. Therefore, in this work, Organic Thin Film Transistor (OTFT) is developed for gas sensor application. As global warming is becoming more serious, this solution is instead a sustainable solution to the environment, as organic molecules which are held together via Van der Waals bond are easily processed via low-temperature deposition and solution processing as compared to more complicated processes involved in conventional inorganic counterpart. In addition, the developed sensor is generally robust due to the ability to withstand high humidity conditions and can be fabricated on flexible substrates. In this work, suitable materials are identified in basic OTFT construction, which are the electrodes, dielectric and substrate. The scope is mainly focusing on the development of bottom gate OTFT construction, incorporating p-type active material which are Trisisopropylsilylethynyl Pentacene (TIPS Pentacene), Aluminium (Al) as drain and source electrodes, PEDOT: PSS as gate electrode and Polyvinyl alcohol (PVA) as gate dielectric. The materials in bottom gate bottom contact (BGBC) configuration, fabricated via screen printing technique is experimentally tested towards CO₂ detection. CO₂ is initially detected at 1618 ppm with contact resistance of 15 kΩ, and at 10 ml/minute flow rate, the developed configuration is demonstrated able to achieve sensitivity of 2.069 Ω/ppm. In conclusion, the studied BGBC OTFT has demonstrated suitability and applicability in CO₂ gas sensing for sustainable environmental condition monitoring, that could lead to safer environment for the living things on earth. With the proposed dimensions, in the future it is possible to proceed with this work to be fabricated by using more advanced techniques such as photolithography and many others.

Abstract: An adaptive control system is an advanced method for controlling the speed of a moving motorized vehicle. Using this intelligent control system, the driver can easily control the speed of the car according to his wishes or the prevailing situation. The adaptive control system consists of a sensor attached to a moving vehicle which then registers the speed of the car and provides input to the processing unit. The controller is designed according to the force exerted by the car to drive a certain distance in a certain time. This time, the control uses a PID controller. This method is followed for various tunings of Kp, Ki, and Kd values for the P, PI, PID, and IPD structures for a cruise control system using MATLAB. The PID used in this experiment is intended to control the speed to make it more stable and optimal.

Abstract: The optimized surface morphology of electrospun magnesium oxide (MgO) nanofibers can be achieved based on the parameters set during the fabrication of nanofibers. However, not all materials used during the electrospinning process can be synthesized together as it depends on the application’s needs. This research aims to study the factors that influence the surface area of the MgO nanofibers due to material preparations and electrospinning parameters. The research is based on data obtained from the previous research and was analyzed to evaluate the effect on MgO nanofibers that synthesized with different materials. Based on the data analysis using Brunauer-Emmert-Teller (BET), the surface area for carbon sorbent is higher than organic sorbent. The surface area for carbon sorbent of nitrogen could be achieved up to 324.5 m²/g compared to only 104.8 m²/g using organic sorbent for magnesium oxalate dihydrate (MO). The studies show that the use of nitrogen as a carbon sorbent in the fabrication of electrospun MgO nanofibers may produce a good quality of nanofibers.

Abstract: Cover factor is defined as the ratio of the area covered by yarns to the total area of the fabric. This fabric’s characteristic is a basic construction parameter of woven cloth related to its end-use behaviour. Different authors are focused on studying the effect of the cover factor fabric on different properties of the fabric, like air permeability, ultraviolet protection, noise absorption and light transmission. However, the aim of this work is study the capacity of the weft to achieve a certain degree of coverage in the woven fabric, taking advantage of the warp's ability to allow light to pass through its own structure and the opacity of the weft.

Abstract: MATLAB was used to compute the effective thermal conduction of different samples of hard isotropic low porosity composites. The computational algorithms intended to use the Effective Medium Theory (EMT) Model to estimate the effective thermal conductivity (k_eff) of homogeneous composites. It estimates k_eff of a homogeneous mixture of components with known volume fractions and components’ conductivities. Starting with preliminary indicators to check the homogeneity conditions, we follow two approaches one for the measured samples and the other for a hypothetical sample with a certain specific desired property. One approach is to use preliminary indicators of surface homogeneity of the measured samples either via electron and/or via optical transmission microfilm scanning. The other approach is used for both measured and hypothetical samples by assuming a layered structure of parallel and series of slabs to compute upper and lower bounds of conduction. We used MATLAB to implement a fine precision computing algorithm to investigate the composite samples. The results predicted by the EMT model were examined for validation. The deviation of k_eff from the experiment for the homogenous samples is between 3 to 28% depending on the uniformity of distribution of phases within the composite matrix.

Abstract: The paper is focused on the fire model parameter variability and its effect on the determination of fire resistance of concrete structural members.For the modelling of fire, the parametric temperature-time curve given in EN 1991-1-2 is used.First part of the paper is aimed on the fire model parameter variability in general.First, fire model parameter ranges are described and their combinations are created using two common sampling methods -- Monte Carlo and Latin Hypercubes.Then, the combinations are analysed, unreasonable combinations are identified, and viable combinations are illustrated.Moreover, the characteristics of the temperature-time curves obtained using the parameter combinations are discussed. Namely, we focus on the temperature evolution, duration of fire, andthe maximum temperature reached.In the second part of the paper, an illustrative example is presented.The example is focused on the analysis of the fire resistance of a concrete slab panel. The panel is placed in a fire compartment with given fire model parameter ranges. In the example, the variability of the fire model parameters is captured using the Latin Hypercubes sampling method.The thermal analysis of the slab panel as well as the subsequent mechanical analysis are both conducted by using numerical methods described in our previous work. The calculations are performed in MATLAB environment.Finally, the obtained results are presented and discussed.It is shown that the Latin Hypercube sampling can be used as an effective tool for the investigation of the effect of fire model parameter variability on the fire resistance.

Abstract: Dry density modeling is a valuable issue. Artificial neural networks (ANNs) have been used in many problems in geotechnical engineering and have demonstrated great success. In this paper, the ANN model is proposed to predict the dry density of the soil. The developed model is managed by the Matlab Neural Network Interface (R2016a). To create the ANN model, liquid limit, plastic limit, plasticity index, moisture content, specific gravity, finer accuracy than sieve 200, total suspended solids, organic and SO₃ were selected and used as input parameters. There are (9, 6,5 and 3) nodes, (10) nodes and (1) node used for input, hidden layers and output layers, respectively. The value of dry density obtained from three sources was sympathetic. The first source is the experimental results of 99 soil samples conducted in Al-Najaf Institution laboratory for this study. The second source was to propose the expected dry density using multiple linear regression analysis (MLRA) on the samples used in the first source; The results show, that the prediction of the use of ANNs was closely consistent with the experimental data. Correlation coefficient (R²) and mean square error (MSE) were 0.97368 and 3.19474 10^-3, respectively. The observed results of the proposed system were very comparable with those obtained from empirical analysis and the prediction obtained from multiple linear regression analysis, where the advanced ANN approach is applicable.

Abstract: The simulation and application of industrial robots has developed very quickly in recent decades. Along with the development of computer science, a lot of softwares to perform dynamic simulation have been created. The results of simulation can be used for layout evaluation, kinematic, dynamic study, off-line programming to avoid obstacle and for design mechanical structure of robots. A co-simulation of 2R industrial robots have been performed by Recurdyn and Matlab. The input parameters are executed under Matlab, and then exported to Recurdyn environment. Kinematic parameters will be executed by RecurDyn then exported to Matlab. The main tasks of this paper are performing 2R robotic manipulator kinematic simulation in two postures with the same trajectory and the same time. Thus, the result of simulation can be compared with theories. Finally, a real 2R robot model was used to verify the trajectory with CAE simulation.

Abstract: Dispersion is one of the fate processes of oil spill. This research has been carried out on the numerical simulation of the dispersion of crude oil using the model obtained from the work of Hamam (1987). The model was solved with the explicit, implicit and Crank-Nicolson methods of solution of partial differentiation equations with the aid of MATLAB, and the concentration of the crude oil dispersed in water was obtained. The results obtained revealed that the three methods could be used to study the process because the profiles given by all of them were very similar. Also discovered from the investigations carried out was that the concentration of crude oil was decreasing with time for a particular spatial point while, for a particular time, it was increasing along the length of the water body. It was also discovered that experiments would be very necessary in order for the validation of the results obtained from the simulations.