The PH regulation of cooling tower plant in southern fertilizers company (SCF) in Iraq is important for industry pipes protection and process continuity. According to the Mitsubishi standard, the PH of cooling water must be around (7.1 to 7.8). The deviation in PH parameter affects the pipes, such as corrosion and scale. Acidic water causes pipes to corrode, and alkaline water causes pipes to scale. The sulfuric acid solution is used for PH neutralization. The problem is that the sulfuric acid is pumped manually in the cooling tower plant every two or three hours for PH regulation. The manual operation of the sulfuric acid pump makes deviations in the PH parameter. It is very difficult to control the PH manually. To solve this problem, a PID controller for PH regulation was used. The reason for using the PID controller is that the PH response is irregular through the neutralization process. The methodology is to calculate the transfer function of the PH loop using the system identification toolbox of MATLAB, to design and implement a PID controller, to optimize the PID controller response using particle swarm optimization PSO algorithm, and to make a comparison among several tuning methods such as Ziegler Nichols (ZN) tuning method, MATLAB tuner method, and PSO algorithm tuning method. The results showed that the PSO-based PID controller tuning gives a better overshoot, less rise time, and an endurable settling time than the other tuning methods. Hence, the PH response became according to the target range. The experimental results showed that the PH regulation improved using the PSO-based PID controller tuning.
Mathematical modeling is very effective method to investigate interaction between insulin and glucose. In this paper, a new mathematical model for insulin-glucose regulation system is introduced based on well-known Lokta-Volterra model. Chaos is a common property in complex biological systems in the previous studies. The results here are in accordance with previous ones and indicating that insulin-glucose regulating system has many dynamics in different situations. The overall result of this paper may be helpful for better understanding of diabetes mellitus regulation system including diseases such as hyperinsulinemia and Type1 DM.
In this paper, enhancing dynamic performance in power systems through load frequency control (LFC) is explored across diverse operating scenarios. A new Neural Network Model Predictive Controller (NN-MPC) specifically tailored for two-zone load frequency power systems is presented. ” Make your paper more scientific. The NN-MPC marries the predictive accuracy of neural networks with the robust capabilities of model predictive control, employing the nonlinear Levenberg-Marquardt method for optimization. Utilizing local area error deviation as feedback, the proposed controller’s efficacy is tested against a spectrum of operational conditions and systemic variations. Comparative simulations with a Fuzzy Logic Controller (FLC) reveal the proposed NN-MPC’s superior performance, underscoring its potential as a formidable solution in power system regulation.
This paper addresses the problem of pitch angle regulation of floating wind turbines with the presence of dynamic uncertainty and unknown disturbances usually encountered in offshore wind turbines, where two control laws are derived for two different cases to continuously achieve zero pitch angle for the floating turbine. In the first case, the time- varying unknown coefficients that characterize the turbine's dynamics are assumed reasonably bounded by known functions, where robust controller is designed in terms of these known functions to achieve zero pitch angle for the turbine with exponential rate of convergence. While in the second case, the turbine's dynamics are considered to be characterized by unknown coefficients of unknown bounds. In this case, a sliding- mode adaptive controller is constructed in terms of estimated values for the unknown coefficients, where these values are continuously updated by adaptive laws associated with the proposed controller to ensure asymptotic convergence to zero for the turbine's pitch angle. Simulations are performed to demonstrate the validity of the proposed controllers to achieve the required regulation objective.
This paper presents and discusses a buck DC/DC converter control based on fuzzy logic approach, in which the fuzzy controller has been driven by voltage error signal and a current error signal for which the load current has been taken as a reference one. The validity of the proposed approach has been examined through starting the buck DC/DC converter at different loading and input voltages (to monitor the starting performances), exposing the converter into large load resistance and input voltage step variations (to explore its dynamic performance), in addition to step and smooth variation in the reference voltage (to see its ability in readjusting its operating point to comply with the new setting). The simulation results presented an excellent load & line regulations abilities in addition to a good reference tracking ability. It also showed the possibility of using the buck converter as smooth variable voltage source (under smooth reference voltage variations).
The paper dells with a modified experimental prototype for pulse-width modulation (PWM) sliding mode control (SMC) applied to a DC-to-DC-boost converter operated in continuous conduction mode (CCM). Experimental results show that the proposed control schme provides good voltage regulation and is suitable for common DC-to-DC conversion purposes. The prototype and its implementation are given in detail. The static and dynamic performances of the The static and dynamic performances of the experimental system are recorded. Experimental results show that the proposed control scheme provides good voltage regulation and is suitable for common DC-to-DC conversion purposes.
Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches, and is usually done for the purpose of loss reduction. Loss reduction can result in substantial benefits for a utility. Other benefits from loss reduction include increased system capacity, and possible deferment or elimination of capital expenditures for system improvements and expansion. There is also improved voltage regulation as a result of reducing feeder voltage drop. Research work included by this paper focuses on using branch exchange method to minimize losses and solve the problems over different radial configuration. Solution’s algorithm for loss minimization has been developed based on two stages of solution methodology. The first stage determines maximum loss-reduction loop by comparing the size of circles for every loop. In a distribution system, a loop is associated by a tie-line and hence there are several loops in the system. To obtain the maximum loss- reduction loop, size of modified zero loss-change circles are compared, and the loop within the largest circle is identified for maximum loss-reduction. The second stage determines the switching operation to be executed in that loop to reach a minimum loss network configuration by comparing the size of the loop circle for each branch-exchange. The smallest circle is to be identified for the best solution; the size of the loop circle is reduced when the losses are minimized. The performance of the proposed branch exchange method is tested on 16-bus distribution systems.
In this paper, a fuzzy based controller for boost type AC/DC converter has been presented. Its operation and performance have been investigated through its simulation in the environment of Mat Lab. The system has been tested under various loading conditions. The obtained results showed that this fuzzy based controller can effectively control the power factor and the harmonic contents of the current drawn from the power factor system distribution network.
Development of distribution systems result in higher system losses and poor voltage regulation. Consequently, an efficient and effective distribution system has become more urgent and important. Hence proper selection of conductors in the distribution system is important as it determines the current density and the resistance of the line. This paper examines the use of different evolutionary algorithms, genetic algorithm (GA), to optimal branch conductor selection in planning radial distribution systems with the objective to minimize the overall cost of annual energy losses and depreciation on the cost of conductors and reliability in order to improve productivity. Furthermore, The Backward-Forward sweep iterative method was adopted to solve the radial load flow analysis. Simulations are carried out on 69-bus radial distribution network using GA approach in order to show the accuracy as well as the efficiency of the proposed solution technique.