Artificial intelligence based direct torque control of induction motor drive system

In this project, a three-phase Induction motor (IM) under the direct torque control (DTC) technique is studied. IM is known for its simple engines and its self-starter feature but it always suffered a setback in the area of torque and speed control as it is a highly coupled nonlinear plant and proves to be most complex and expensive speed drive. The application of direct torque control (DTC) is beneficial for fast torque reaction in IM but provide high torque and ripples due to harmonic effects. Thus, the speed control of induction motor is important to achieve maximum torque and efficiency. The aim of this study is to improve tracking performance of the induction motor drive using artificial intelligence control system. A method for controlling induction motor drive is presented with Proportional-Integral (PI) controller and Artificial Neural Networks (ANNs) for performance comparison. MATLAB/SIMULINK software is used to develop a three-phase 2 pole-cage type induction motor model. Also the performances of the two controllers have been verified in terms of its speed and torque responses. The ANN is trained so that the speed of the drive tracks the reference speed. This study proved that the performance and dynamics of the induction motor are enhanced using ANN controller as compared with PI controller

Chemical composition of essential oil of exudates of Dryobalanops aromatica

Purpose: To identify the chemical composition of essential oil from the exudates of Dryobalanops aromatica from Malaysia.Methods: Exudate was collected from D. aromatica and subjected to fractional  distillation to obtain essential oil. Gas chromatography-mass spectrometry  (GC-MS) was used to characterize the composition of the isolated essential oil.Results: The yield of essential oil was 7.58 %, with the highest yield (3.24 %) within the first 2 h of fractional distillation. Thirty compounds which accounted for 97.56 % of essential oil composition were identified. These include sesquiterpenes (46.87 %), monoterpenes (31.05 %), oxygenated monoterpenes (16.76 %) and oxygenated  sesquiterpenes (2.13 %). Borneol accounted for 0.74 % of the essential oil.Conclusion: Essential oil from the exudates of D. aromatica contains terpenoid  compounds and borneol.Keywords: Dryobalanops aromatica, exudate, fractional distillation, essential oil, GS-MS, borneo

Determination of borneol and other chemical compounds of essential oil of Dryobalanops aromatica exudate from Malaysia

Purpose: To determine borneol and other chemical compounds of essential oil derived from the exudate of Dryobalanops aromatica in Malaysia.Methods: Exudate was collected from D. aromatica and subjected to fractional distillation to obtain essential oil. Gas chromatography-mass spectrometry (GC-MS) was performed to characterize the composition of the isolated essential oil.Results: Essential oil (7.58 %) was obtained with the highest yield (3.24 %) in the first 2 h of fractional distillation. Thirty compounds which accounted for 97.56 % of total essential oil composition were identified by GC-MS, and they include sesquiterpenes (46.87 %), monoterpenes (31.05 %), oxygenated monoterpenes (16.76 %) and oxygenated sesquiterpenes (2.13 %). Borneol (0.74 %) was also detected in the essential oil.Conclusion: Borneol and other terpenoid compounds are present in the essential oil of the exudate of D. aromatica.Keywords: Exudate, Dryobalanops Aromatica, Fractional distillation, Essential oil, Gaschromatography-mass spectrometry, Borneo

Loss minimization DTC electric motor drive system based on adaptive ANN strategy

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategies

Cytotoxic and antibacterial activities of endophytic fungi isolated from plants at the National Park, Pahang, Malaysia

<p>Abstract</p> <p>Background</p> <p>Endophytes, microorganisms which reside in plant tissues, have potential in producing novel metabolites for exploitation in medicine. Cytotoxic and antibacterial activities of a total of 300 endophytic fungi were investigated.</p> <p>Methods</p> <p>Endophytic fungi were isolated from various parts of 43 plants from the National Park Pahang, Malaysia. Extracts from solid state culture were tested for cytotoxicity against a number of cancer cell lines using the MTT assay. Antibacterial activity was determined using the disc diffusion method.</p> <p>Results</p> <p>A total of 300 endophytes were isolated from various parts of plants from the National Park, Pahang. 3.3% of extracts showed potent (IC₅₀< 0.01 μg/ml) cytotoxic activity against the murine leukemic P388 cell line and 1.7% against a human chronic myeloid leukemic cell line K562. <it>Sporothrix </it>sp. (KK29FL1) isolated from <it>Costus speciosus </it>showed strong cytotoxicity against colorectal carcinoma (HCT116) and human breast adenocarcinoma (MCF7) cell lines with IC₅₀values of 0.05 μg/ml and 0.02 μg/ml, respectively. Antibacterial activity was demonstrated for 8% of the extracts.</p> <p>Conclusion</p> <p>Results indicate the potential for production of bioactive agents from endophytes of the tropical rainforest flora.</p

Loss minimization DTC electric motor drive system based on adaptive ANN strategy

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategie

Loss minimization DTC electric motor drive system based on adaptive ANN strategy

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategie

Numerical study of interparticle radiation force acting on rigid spheres in a standing wave

10.1121/1.4916968Journal of the Acoustical Society of America13752614-262