Micro-peat as a potential low-cost adsorbent material for COD and NH3-N removal

Micro-peat (M-P) was demonstrated in the present study as a potential low cost natural adsorbent for the removal of COD and ammoniacal nitrogen (NH3-N) from landfill leachate. A series of batch experiments were carried out under fixed conditions and the influence of mixture ratio was investigated. The characteristics of leachate were then determined. Results indicated that leachate is non-biodegradable with high concentration of COD (2739.06 mg/L), NH3-N (1765.34 mg/L) and BOD5/COD ratio (0.09). The optimum ratio for activated carbon (AC) and M-P in the removal of COD and NH3-N obtained were at 2.5:1.5 (87%) and 1.0:3.0 (65%) respectively. The low-cost natural adsorbent used in the present investigation is an attractive alternative to the conventional adsorbent (AC). Thus, M-P can be appropriated for use in leachate treatment that could be cost-effective due its local availability and adsorption property

Data-driven online temperature compensation for robust field-oriented torque-controlled induction machines

Squirrel-cage induction machines (IMs) with indirect field-oriented control are widely used in industry and are frequently chosen for their accurate and dynamic torque control. During operation, however, temperature rises leading to changes in machine parameters. The rotor resistance, in particular, alters, affecting the accuracy of the torque control. The authors investigated the effect of a rotor resistance parameter mismatch in the control algorithm on the angular rotor flux misalignment and the subsequent deviation of stator currents and motor torque from their setpoints. Hence, an online, data-driven torque compensation to eliminate the temperature effect is proposed to enable robust torque-controlled IMs. A model-based analysis and experimental mapping of the temperature effect on motor torque is presented. A temperature-torque lookup-table is subsequently implemented within the control algorithm demonstrating the ability to reduce the detrimental effect of temperature on torque control. Experimental results on a 5.5 kW squirrel-cage induction motor show that the proposed data-driven online temperature compensation method is able to reduce torque mismatch when compared to having no temperature compensation. Up to 17% torque mismatch is reduced at nominal torque and even up to 23% at torque setpoints that are lower than 20% of the nominal torque. A limited torque error of <1% remains in a broad operating range

Development and analysis of a self-tuned neuro-fuzzy controller for induction motor drives

Induction motors (IM) have been widely utilized in industry for variable speed drives due to some of their advantages, such as rugged construction, low cost and reliable service with easy maintenance, as compared to conventional dc motors. For variable speed drive applications, the controller plays an important role so that the motor can follow the reference trajectories without any significant deviation. Furthermore, a controller which can provide fast speed response and handle uncertainties and disturbances, is absolutely necessary for high performance drive systems. Traditionally, fixed gain proportional-integral (PI) and some adaptive controllers have been utilized in industry for a long time. However, there are some disadvantages of these controllers to handle uncertainties which are inherent to a nonlinear IM. As a result, recently researchers paid their attention to apply intelligent algorithms to control the IM for high performance variable speed drive applications. Intelligent algorithms such as fuzzy logic (FL), neural network (NN), neuro-fuzzy (NF), etc, have inherent advantages as compared to the conventional controllers. In this thesis, a novel neuro-fuzzy controller (NFC) has been developed for speed control o f EM. For the complete drive, the indirect field orientation control is utilized in order to decouple the torque and flux controls. Thus, the induction motor can be controlled like a dc motor and hence the high performance can be achieved without lacking the advantage o f ac over dc motors. The proposed neuro-fuzzy controller incorporates Sugeno model based fuzzy logic laws with a five-layer artificial neural network (ANN) scheme. The controller is designed for low computational burden, which will be suitable for real-time implementation. Furthermore, for the proposed NFC an improved self-tuning method is developed based on the IM theory and its high performance requirements. The main task o f the tuning method is to adjust the parameters o f the fuzzy logic controller (FLC) in order to minimize the square of the error between actual and reference output. In this thesis, a model reference adaptive flux (MRAF) observer is also developed to estimate the d-axis rotor flux linkage in both constant flux and flux weakening regions based on motor voltage, current and reference trajectories for flux linkage. Thus, it provides safe operation to control the motor at high speeds, especially, above the rated speed. The d-axis reference flux linkage of the indirect field oriented control is provided by flux weakening method. Furthermore, a proportional-integral (PI) based flux controller is used to provide the compensation for the reference flux model by comparing the flux reference and the observed flux from Gopinath model flux observer. A complete simulation model for indirect field oriented control of IM incorporating the proposed MRAF observer based NFC is developed in Matlab/Simulink. In order to prove the superiority of the proposed controller, the performance of the proposed controller is compared with a conventional PI as well as fuzzy logic controller (FLC) based IM drives. The performance of the proposed IM drive is investigated extensively at different operating conditions in simulation. The performance of the proposed MRAF observer based NFC controller is found robust and a potential candidate for high performance industrial drive applications

Design and Simulation of an Efficient Neural Network Based Speed Controller For Vector Controlled Induction Motor Drive

This project work start with the development of simulation model of rotor magnetic field oriented vector control system based on MATLAB software. This paper proposes the development of a Neural Network controller in place of PI controller commonly used in the vector control structure for efficient speed control and smaller settling time. After successful implementation of proposed Neural Network controller, the results obtained, which shows the superior performance of NN controller over conventional PI controller. In addition to this, It is also shown by the resultant response that, the proposed modified vector control structure based on Neural Network controller smoothen out the ripples in the motor torque and stator current as fine as will provide best speed regulation with smaller settling time requirement. DOI: 10.17762/ijritcc2321-8169.150517

Resonant mode controllers for launch vehicle applications

Electro-mechanical actuator (EMA) systems are currently being investigated for the National Launch System (NLS) as a replacement for hydraulic actuators due to the large amount of manpower and support hardware required to maintain the hydraulic systems. EMA systems in weight sensitive applications, such as launch vehicles, have been limited to around 5 hp due to system size, controller efficiency, thermal management, and battery size. Presented here are design and test data for an EMA system that competes favorably in weight and is superior in maintainability to the hydraulic system. An EMA system uses dc power provided by a high energy density bipolar lithium thionyl chloride battery, with power conversion performed by low loss resonant topologies, and a high efficiency induction motor controlled with a high performance field oriented controller to drive a linear actuator

Modelling of Neural Network based Speed Controller for Vector Controlled Induction Motor Drive

This project work start with the development of simulation model of rotor magnetic field oriented vector control system based on MATLAB software. This paper proposes the development of a Neural Network controller in place of PI controller commonly used in the vector control structure for efficient speed control and smaller settling time. It is expected that the proposed modified vector control structure based on Neural Network controller smoothen out the ripples in the motor torque and stator current as fine as will provide best speed regulation with smaller settling time requirement. DOI: 10.17762/ijritcc2321-8169.150512

To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

Robust indirect field oriented control of induction generator

The paper presents a novel robust field oriented vector control for induction generators. The proposed controller exploits the concept of indirect field orientation and guarantees asymptotic DC-link voltage regulations when DC-load is constant or slowly varying. An output-feedback linearizing Lyapunov’s based technique is employed for the voltage controller design. Flux subsystem design provides robustness with respect to rotor resistance variations. Decomposition of the voltage and current-flux subsystems, based on the two-time scale separation, allows to use a simple controllers tuning procedure. Results of comparative experimental study with standard indirect field oriented control are presented. It is shown that in contrast to existing solutions designed controller provides system performances stabilization when speed and flux are varying. Experimentally shown that robust field oriented controller ensures robust flux regulation and robust stabilization of the torque current dynamics leading to improved energy efficiency of the electromechanical conversion process. Proposed controller is suitable for energy generation systems with variable speed operation