Robiah Ahmad

Model structure selection for a discrete-time non-linear system using a genetic algorithm

Abstract In recent years, extensive works on genetic algorithms have been reported covering various applications. Genetic algorithms (GAs) have received significant interest from researchers and have been applied to various optimization problems. They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. First the effect of different combinations of GA operators on the performance of the model developed is studied. A proposed algorithm called modified GA, or MGA, is presented and a comparison between a simple GA and a modified GA is carried out. The performance of the proposed algorithm is also compared to the model developed using the orthogonal least squares (OLS) algorithm. The adequacy of the developed models is tested using one-step-ahead prediction and correlation-based model validation tests. The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.

Red and blue LED with pulse lighting control treatment for Brassica chinensis in Indoor farming

This study proposes a method of optimizing plant growth in control environment using led lighting system. Indoor farming system can cultivate varieties of plant mainly vegetables. Research on indoor farming using Led as a light source for plant photosynthesis has becoming high in demand due to uncertainty of weather and pest mortality. Red / Blue (RB) LEDs combination of ratio 16:4 as light source has been used to simulate actual sun light radiation and has similar absorption spectrum of the plant photosynthesis. Optimizing plant growth with climate control system to maintain and monitor humidity, temperature and light intensity is a must to ensure the survival of the plant and is proposed for the system. The system of data logger capability to acquire data and perform analytical work after each experiment performs. The study compares the effect of plant growth with different amount of light exposed. The finding from the experiment has shown very significant result for plant with pulse lighting control system of 1-hour duration with 15 minutes light off period as compared to typical photoperiod at 12 hours continuous lighting system and 12 hours light off period. From data analysis, we conclude that the plant with pulse light treatment which was recorded 291% higher photosynthesis rates is having better growth rates compared to conventional 12 hours lighting system.

SIMULTANEOUS OPTIMIZATION OF FOUR PARAMETERS IN THE STACK UNIT OF A THERMOACOUSTIC REFRIGERATOR

Thermoacoustic refrigerators are environmentally friendly cooling systems that use no refrigerants. Optimization of the performance of any cooling system is crucial for an efficient energy management. Most of the optimization techniques in thermoacoustic systems utilized to date involved experimental and numerical parametric studies which are generally limited to the variations of the parameters to be optimized at discrete values. This study reports on the optimization of a thermoacoustic refrigerator using multi-objective genetic algorithm (MOGA). The study introduces the ability of MOGA to optimize four different variables which are length of stack, center position of stack, blockage ratio and drive ratio simultaneously. The four variables are optimized to achieve the two objectives; a maximum cooling and minimum acoustic power required at the stack and provide the optimum coefficient of performance, COP. The results show that the optimum COP = 1.35 with a cooling power of Qc = 6.57 W, acoustic power of Wn = 4.86 W and with the resonator diameter of D = 3.8 cm.

Performance Optimization of a Microchannel Heat Sink Using the Improved Strength Pareto Evolutionary Algorithm (SPEA2)

In this paper, a feasible optimization scheme for rectangular microchannel heat sinks, which incorporates the thermal resistance model and the Improved Strength Pareto Evolutionary Algorithm (SPEA2), is reported. An alternative coolant, namely, ammonia gas, is used to improve the overall thermal and hydrodynamic performances of the considered system. Results from the optimization showed significant reduction in the total thermal resistance compared to the conventional air-cooled systems up to 35% for the same allowable pumping power. The SPEA2 exhibited excellent performance when it was compared to another multiobjective algorithm, NSGA2. The results reported in this study open the door for the incorporation of some other algorithms, which have not been used in the optimization of microchannel heat sinks. Finally, the outcome of this paper predicts a promising future for the usage of ammonia gas in the area of electronics cooling.

A literature review on green supply chain modelling for optimising CO 2 emission

Global warming impacts are becoming more visible in our daily life. Supply chain activities and many logistics activities are the leading sources of carbon dioxide (CO2) emission and environmental pollutions. These issues have raised concerns to reduce CO2 emissions amount through design and planning of supply chain networks. Operations research has been recognised by many studies as an effective tool to deal with CO2 emission in design and planning of green supply chains. To date, a number of literature reviews have highlighted the contribution of operations research to green supply chain management with broader areas of focus. In this paper, we present a review which highlights the operations research contribution to recent green supply chain and logistics literature which specifically focuses on planning and control of supply chain activities with respect to CO2 emission. Finally, we propose some possible areas for further developments of current studies and directions for future research.

Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems

Modeling input–output data representing a dynamic system is a challenging task when multiple objectives are involved. The developed model needs to be parsimonious yet still adequate. To achieve these goals, two objective functions, i.e. optimum structure and minimum predictive error, need to be satisfied. Most works in system identification only consider one objective function, i.e. minimum predictive error, and the model structure is obtained by trial and error. This paper attempts to establish the needs of a multi-objective optimization algorithm by comparing it with a single-objective optimization algorithm. In this study, two different types of optimization algorithms are used to model a discrete-time system. These are an elitist non-dominated sorting genetic algorithm for multi-objective optimization and a modified genetic algorithm for single-objective optimization. Simulated and real systems data are studied for comparison in terms of model predictive accuracy and model complexity. The results show the advantage of the multi-objective optimization algorithm compared with the single-objective optimization algorithm in developing an adequate and parsimonious model for a discrete-time system.

Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification

Abstract The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. The flexibility of a genetic algorithm allows various strategies to be applied to it. One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. The strategy has been shown to be better than the simple genetic algorithm and conventional statistical method, but it contains inadequate justification of how the separation is made. The usage of objective function values for separation of groups does not carry much flexibility and is not suitable since different time-dependent data have different levels of equilibrium and thus different ranges of objective function values. This paper investigates the optimum grouping of chromosomes by fixed group ratios, enabling more efficient identification of dynamic systems using a NARX (Non-linear AutoRegressive with eXogenous input) model. Several simulated systems and real-world timedependent data are used in the investigation. Comparisons based on widely used optimization performance indicators along with outcomes from other research are used. The issue of model parsimony is also addressed, and the model is validated using correlation tests. The study reveals that, when recombination and mutation are used for different groups, equal composition of both groups produces a better result in terms of accuracy, parsimony, speed, and consistency.

WSN application in LED plant factory using continuous lighting (CL) method

This study was performed to investigate the best practise on using LED light to optimum growth to reduce turn around time using different kind of photoperiod study and using Wireless Sensor Network (WSN) technology as remote monitoring system has been conducted. Growth performance of Brassica Chinensis under two different wavelengths (blue and red) 16:4 as light source has been used to determine plant growth performance and phytochemicals aspect of plant characteristics. Two experiments were conducted which is the pulse treatment (1 hour light and 1 hour dark) and continuous light (CL) photoperiod treatment in both trials. Observation such as leaves count, height, dry weight and chlorophyll of both plant were analysed. It was noted that the CL photoperiod has significant effect on overall growth performance and remarkably lead to improve the efficiency of the plant factory. In order to reason on data and monitor the environmental parameters of the plant factory, an intelligent system using embedded system has been developed to automate the LED control and manipulation. The result shows that the system is stable and has referential significantly in the area of plant factory or indoor farming system.

Optimization of nanofluid-cooled microchannel heat sink

The optimization of a nanofluid-cooled rectangular microchannel heat sink is n reported. Two nanofluids with volume fraction of 1 %, 3 %, 5 %, 7 % and 9 % n are employed to enhance the overall performance of the system. An n optimization scheme is applied consisting of a systematic thermal resistance n model as an analysis method and the elitist non-dominated sorting genetic n algorithm (NSGA-II). The optimized results showed that the increase in the n particles volume fraction results in a decrease in the total thermal n resistance and an increase in the pumping power. For volume fractions of 1 n %, 3 %, 5 %, 7 % and 9 %, the thermal resistances were 0.072, 0.07151, n 0.07075, 0.07024 and 0.070 [oK W-1] for the SiC-H2O while, they were 0.0705, n 0.0697, 0.0694, 0.0692 and 0.069 [oK W-1] for the TiO2-H2O. The associated n pumping power were 0.633, 0.638, 0.704, 0.757 and 0.807 [W] for the SiC-H2O n while they were 0.645, 0.675, 0.724, 0.755 and 0.798 [W] for the TiO2-H2O. n In addition, for the same operating conditions, the nanofluid-cooled system n outperformed the water-cooled system in terms of the total thermal n resistance (0.069 and 0.11 for nanofluid-cooled and water-cooled systems, n respectively). Based on the results observed in this study, nanofluids n should be considered as the future coolant for electronic devices cooling n systems.

Development of energy harvesting device using piezoelectric material

Using piezoelectric elements to harvest energy from ambient vibration and human motion has been of great interest recently. This study quantifies the amount of energy generated by piezoelectric device from vibration environment and human motion. Nickel metal hydride rechargeable battery has been used to store the energy generated. Throughout the experiment, the discharged 40mAh rechargeable battery can be charged to its cell voltage in less than 10 minutes in resonant vibration. Meanwhile, for human motion energy harvesting experiment, as subject running with 5km/h speed, the discharged 40mAh rechargeable battery can be charged to its cell voltage in one hour.