Universiti Teknologi Petronas

About: Universiti Teknologi Petronas is a education organization based out in Ipoh, Malaysia. It is known for research contribution in the topics: Adsorption & Ionic liquid. The organization has 6127 authors who have published 11284 publications receiving 119400 citations.

Insight review of attached microalgae growth focusing on support material packed in photobioreactor for sustainable biodiesel production and wastewater bioremediation

Abstract: The suspended microalgal cultivation systems targeting the production of lipid for biodiesel is still unable to meet the commercial and economic feasibility due to high energy and cost inputs during the harvesting process. Currently, the attached microalgal cultivation systems are known as an innovative technology to resolve the problematic aspects of suspended cultivation as the biomass can be easily separated from the culture medium with negligible energy input and no chemical need. Accordingly, this paper is focusing on reviewing various support materials utilized by researchers in growing the attached microalgal biomass in either fixed bed or fluidized bed bioreactor to ensure sustainable biomass production prior for biodiesel. An extensive review of the cultivation conditions impacting the growth of attached microalgal biomass is conducted in determining the optimized condition to enhance biomass productivity. The novelty of this paper is presented through in-depth analyses and discussions concerning the kinetic models and mechanisms study in selecting the suitable support materials for attached microalgal growth system. The incorporation of the cultivation condition parameter in predicting the growth of attached microalgae biomass via kinetic study is crucial in proffering new directions for technological developments and microalgal industry application. The ability of attached microalgal biomass in efficiently assimilate the nutrients available in the nutrient-rich wastewater for the microalgae growth is considered as an economical feasible approached. The suitable photobioreactor designs for growing the attached microalgal biomass are also discussed in detail in ensuring the sustainability of attached microalgal biomass production.

Phase change material/heat pipe and Copper foam-based heat sinks for thermal management of electronic systems

Abstract: Phase change material (PCM) has been extensively used for their thermal management but due to low conductivity that hinders the performance of a system. Since heat pipe and porous materials both have high thermal conductivities, they can be used to form hybrid system with PCM which significantly enhance the heat transfer capability of PCM. In current research of heat pipe, copper foam (pore density 40PPI and porosity 93%) and PCM based heat sinks are used to inspect the thermal performance of heat sink with respect to time by varying heat fluxes. ‘‘RT-35HC” PCM, copper foam and gravity assisted heat pipe with and without cooling fan are used in experimental investigation. The results showed after 6000 s when charging ends hybrid cooling (Foam-PCM-HP) with fan have maximum temperature reduction i.e. 47%, 51% and 54% at heat flux of 2, 2.5 and 3 kW/m2 respectively. Similarly, for discharging hybrid cooling with fan showed excellent cooling results at all heat fluxes.

Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems

Abstract: The use of artificial neural networks (ANN) in fault detection analysis is widespread. This paper aims to provide an overview on its application in the field of fault identification and diagnosis (FID), as well as the guiding elements behind their successful implementations in engineering-related applications. In most of the reviewed studies, the ANN architecture of choice for FID problem-solving is the multilayer perceptron (MLP). This is likely due to its simplicity, flexibility, and established usage. Its use managed to find footing in a variety of fields in engineering very early on, even before the technology was as polished as it is today. Recurrent neural networks, while having overall stronger potential for solving dynamic problems, are only suggested for use after a simpler implementation in MLP was attempted. Across various ANN applications in FID, it is observed that preprocessing of the inputs is extremely important in obtaining the proper features for use in training the network, particularly when signal analysis is involved. Normalization is practically a standard for ANN use, and likely many other decision-based learning methods due to its ease of use and high impact on speed of convergence. A simple demonstration of ANN’s ease of use in solving a unique FID problem was also shown.

Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing

Abstract: Development of data-driven virtual flow meter (VFM) using diverse neural network ensembles.Adaptive simulated annealing is used for pruning and combining strategy selection.VFM can provide real-time monitoring for fields with common metering infrastructure.Achieved 4.7% and 2.5% mean absolute errors for gas and liquid flow rate estimations.The proposed method outperforms standard stacking and bagging techniques. Real-time production monitoring in oil and gas industry has become very significant particularly as fields become economically marginal and reservoirs deplete. Virtual flow meters (VFMs) are intelligent systems that infer multiphase flow rates from ancillary measurements and are attractive and cost-effective solutions to meet monitoring demands, reduce operational costs, and improve oil recovery efficiency. Current VFMs are very challenging to develop and very expensive to maintain, most of which were developed for wells with dedicated physical meters where there exists an abundance of well test data. This study proposes a VFM system based on ensemble learning for fields with common metering infrastructure where data generated is very limited. The proposed method generates diverse neural network (NN) learners by manipulating training data, NN architecture and learning trajectory. Adaptive simulated annealing optimization is proposed to select the best subset of learners and the optimal combining strategy. The proposed method was evaluated using actual well test data and managed to achieve a remarkable performance with average errors of 4.7% and 2.4% for liquid and gas flow rates respectively. The accuracy of the developed VFM was also analyzed using cumulative deviation plot where the predictions are within a maximum deviation of 15%. Furthermore, the proposed ensemble method was compared to standard bagging and stacking and remarkable improvements have been observed in both accuracy and ensemble size. The proposed VFM is expected to be easier to develop and maintain than model-driven VFMs since only well test samples are required to tune the model. It is hoped that the developed VFM can augment and backup physical meters, improve data reconciliation, and assist in reservoir management and flow assurance ultimately leading to a more efficient oil recovery and less operating and maintenance costs.