A dynamic simulator for MSF plants

Some theoretical aspects in simulation of multiple stage desalination systems

Faults analysis for MSF plants

Expensive specification testing of analog and mixed-signal circuits is currently being replaced by fault-oriented test techniques, similar to the ones used for digital circuits. However, the large number of possible failure modes in analog and mixed-signal circuits presents a major bottleneck in terms of fault simulation complexity and size of fault dictionary. For a given set of possible faults, it has been shown that only a small fraction of the total number of faults contribute to diagnostic accuracy. In this paper, we present a fault sampling methodology that identifies those faults by simulating only a minimal fraction of the entire set of possible faults. The proposed sampling algorithm is based on statistical fault simulation and leads to major reductions in fault simulation complexity for hierarchical analog and mixed-signal circuits.

Estimation of possible maximum earthquake magnitudes of Quaternary faults in the southern Korean Peninsula

In Distributed Control System (DCS), multitasking management has been important issues continuously researched and developed. In this paper, DCS was applied in global temperature control system by coordinating three Local Control Units (LCUs). To design LCU’s controller parameters, both analytical and experimental method were employed. In analytical method, the plants were firstly identified to get their transfer functions which were then used to derive control parameters based on desired response qualities. The experimental method (Ziegler-Nichols) was also applied due to practicable reason in real industrial plant (less mathematical analysis). To manage set-points distributed to all LCUs, master controller was subsequently designed based on zone of both error and set-point of global temperature controller. Confirmation experiments showed that when using control parameters from analytical method, the global temperature response could successfully follow the distributed set-points with 0% overshoot, 193.92 second rise time, and 266.88 second settling time. While using control parameters from experimental method, it could also follow the distributed set-points with presence of overshoot (16.9%), but has less rise time and settling time (111.36 and 138.72 second). In this research, the overshoot could be successfully decreased from 16.9 to 9.39 % by changing master control rule. This proposed method can be potentially applied in real industrial plant due to its simplicity in master control algorithm and presence of PID controller which has been generally included in today industrial equipments.

The regulation and stabilization of a system's output to achieve desired performance and ensure system reliability require the use of different controllers, but selecting appropriate control parameters presents a challenge in ensuring robustness and stability. Proportional–integral–derivative (PID) controllers are popularly used due to their simplicity and effectiveness in addressing these challenges. In this paper, a simple, effective, and efficient, novel graphical technique is proposed to design PID and its variants (PI/PD) controllers, which addresses the challenges in selecting appropriate control parameters. The method involves creating three equispaced vectors for controller parameters , and obtaining a 3D (2D in PI/PD) Cartesian grid of these vectors. All nodes in the grid provide several possible controllers, and integral time squared error (ITSE) is calculated for each controller from the closed‐loop step response of the system. The obtained ITSE is plotted in a 4D (PID) or 3D (PI/PD) graph, and controller parameters corresponding to the minimum value of ITSE are identified. Furthermore, the proposed graphical technique aids in choosing the lower and upper bounds (LB and UB) if the controller is designed using optimization techniques. The better selection of LB and UB reduces the search space resulting in lesser execution time and fewer iterations. To validate the proposed graphical technique, we designed various controllers for widely‐used brush‐less DC and switch reluctance motors in electric vehicles. Additionally, by choosing the LB and UB with the proposed technique, controllers are also designed using three optimization techniques: particle swarm optimization, black widow optimization algorithm, and honey badger algorithm. The obtained controllers using the graphical technique outperformed the optimization techniques in terms of time and frequency domain specifications, and the proposed selection of lower and upper bounds resulted in improved performance in terms of iterations and execution time.

Performance-based Fault Mitigation in Sampled-Data Process Systems with Sensor Faults and Measurement Delays

This paper presents an active fault-tolerant control (FTC) design approach by virtue of an adaptive linear parameter varying (LPV) methodology. In this study, the magnitude of fault is obtained from the results of a fault detection and diagnosis (FDD) scheme. Based on the estimated fault magnitude, a fault-tolerant linear parameter dependent statefeedback controller in a convex polytopic LPV representation format is designed so as to counteract the adverse effects of actuator faults. The parameters that vary with actuator fault magnitudes are specified as design parameters for LPV controller, while the ultimate control law can be obtained using the well-established linear matrix inequality (LMI) conditions. Numerical simulations on an unmanned quadrotor helicopter nonlinear model are carried out to demonstrate the effectiveness of the proposed method.

The present investigation is based on the development of a conceptual design of a desalination system incorporating the technology of the heat pump, for which the conceptual foundations related to water and its characteristics were addressed; a description of the different desalination systems and technical characteristics of a heat pump. The information collected allowed to start the development of the methodological framework, in which the lineal design model was applied to define the parameters and requirements that were considered to determine the conceptual design of the desalination system using a heat pump, through which, the main result was that the heat pump technology is applicable to a seawater desalination system, considering the seawater conditions on the coast of Chile and the standardized flows that the Navy could require.

We demonstrate that extremely rapid and weak periodic and non-periodic signals can easily be detected by using the autocorrelation of intensity as a function of time. We use standard radio-astronomical observations that have artificial periodic and non-periodic signals generated by the electronics of terrestrial origin. The autocorrelation detects weak signals that have small amplitudes because it averages over long integration times. Another advantage is that it allows a direct visualization of the shape of the signals, while it is difficult to see the shape with a Fourier transform. Although Fourier transforms can also detect periodic signals, a novelty of this work is that we demonstrate another major advantage of the autocorrelation, that it can detect non-periodic signals while the Fourier transform cannot. Another major novelty of our work is that we use electric fields taken in a standard format with standard instrumentation at a radio observatory and therefore no specialized instrumentation is needed. Because the electric fields are sampled every 15.625 nanoseconds, they therefore allow detection of very rapid time variations. Notwithstanding the long integration times, the autocorrelation detects very rapid intensity variations as a function of time. The autocorrelation could also detect messages from Extraterrestrial Intelligence as non-periodic signals.

Why do continental normal fault earthquakes have smaller maximum magnitudes?

In this work, a machine's health monitoring system (MHMS) that can detect anomalies for a centrifugal compressor, operating in a real plant, is proposed. A compressor is a crucial machine but over time its performance can decline, and faults can develop. In the compressor under investigation, several sensors have been installed. Subsequently, its behavior is monitored and recorded. During this period, no noticeable fault is detected. In the proposed technique, simulated faults are employed to build up the data that can be used in the investigation, then the performance of the compressor is analyzed and compared with ML and MLP. Herein, the study indicates that the data accumulated is a good candidate for this challenging case. Overall, the proposed technique demonstrates good potential for detection of anomalies regarding the real centrifugal compressor.

Dynamic Models for predicting the concentration profiles of the reactants and product in a Continuous Stirred Tank Reactor for the transesterification of used cooking oil (triglyceride) to biodiesel has been developed using the principle of conservation of mass. The developed system of differential equations were integrated numerically using fourth order Runge-Kutta algorithm embedded in ode 45 solver of 7.5 Mathlab program. The models were validated by solving the model equations with kinetic data and other relevant data from literatures. The results and trends were similar and in agreement with those from these literatures. Simulations of the reactor to (±) step changes in the inlet flowrates of the reactants (used cooking oil and methanol) showed great effect on biodiesel production, (instability—oscillations and reduction in output concentration of biodiesel). A feedback control strategy was developed with a Proportional-Integral (PI) Controller and a close loop model was developed for control studies. The closed loop response of the reactor output (biodiesel concentration) showed continuous oscillatory response with offset. Hence the controller parameters (proportional gain Kc and integral time ) were tuned using the “On-Line Trial and Error Method” implemented using MathLab Simulink to obtain optimum values that ensured quick stability of the closed-loop system, reduced or no oscillatory response and no offset. The optimum controller parameters were: proportional gain Kc ＝8.306 and integral time = 17.157 minutes.

This study proposes a new optimization approach, which is called as artificial ecosystem optimization algorithm with fitness-distance balance guiding mechanism by using opposite based learning methods (FDBAEO_OBLs) for the speed regulation of direct current (DC) motor. The performance of the proposed FDBAEO_OBL algorithm is tested in two different experimental studies. In the first experimental study, the proposed approach is tested in the CEC2020 benchmark test functions and the FDBAEO algorithm, which included the best OBL approach, is determined using non-parametric Wilcoxon and Friedman statistical analysis methods. Second, the parameters of proportional integral derivative (PID), tilt integral derivative (TID), proportional integral derivative with filter (PIDF), tilt integral derivative with filter (TIDF), fractional-order proportional integral derivative (FOPID), fractional-order proportional integral derivative with filter (FOPIDF), proportional integral derivative with fractional-order filter (PIDFF) and fractional-order proportional integral derivative with fractional-order filter (FOPIDFF) controller structures to be used in DC motor closed loop speed control are determined with FDBAEO_OBL, and the performances of the controllers are investigated. Integral absolute error (IAE), integral time absolute error (ITAE), integral time squared error (ITSE) and integral squared error (ISE) performance indices are used as the objective function of the operation process in which the control parameters are determined. According to the comparative step response results of the controller structures, the four best controller structures for DC motor speed regulation are determined. The performances of these controllers are examined under different simulation conditions and according to the results obtained, it is seen that the best controller structure is FOPIDFF. The FDBAEO_OBL algorithm, which is used in both benchmark test functions and DC motor speed regulation, shows an effective, durable and superior performance in finding the optimal solution values during the optimization.

Hybrid AC–DC networks are transforming high‐voltage transmission and medium‐voltage distribution grids by embracing the advantages of both AC and DC systems, which facilitates the inclusion of renewable energy sources and distributed generation. As modular multilevel converters (MMCs) are vastly employed in such hybrid networks, determining their maximal fault current in worst‐case scenario is a critical design factor for planning and implementation of a reliable protection scheme. This study develops a novel mathematical framework that applies a Lagrangian energy method to calculate the maximal fault magnitude. This method allows to account for converter's internal energy and compute its impact on the amplitude of the fault current. It is shown when the converter is interfacing weak AC sources with high internal impedance such as wind farms or solar farms, dumping the internal energy of the converter into the fault is the salient contributing factor of the fault magnitude. Furthermore, to distinguish and classify the output overcurrent as either ignorable transients or destructive faults, a perceptron with sigmoid threshold is employed. The model is verified using a simulated medium‐voltage hybrid AC–DC distribution network.