79 research outputs found
Molecular dynamics simulations of CO2 clathrate hydrate in the presence of organic components
No full textAs the major greenhouse gas emission, releasing CO2 through human activities has already devastating consequences on the planet. In this context, hydrate-based (HB) techniques in favour of CO2 capture, sequestration, or utilization (CCSU) are perceived to be a novel option to arrest increasing concentrations of CO2 in the atmosphere. The end uses of captured CO2 encompass its utilization for different realms of industry such as food and beverage manufacturing plants; water desalination; metal fabrication plants; and secondary refrigeration. To offset the cost of CO2 capture as well as generating revenue, the increasing effectiveness of aforesaid techniques is crucial. Although HB approaches are faced with several limitations, the solution would be the inclusion of organic promoters which are classified as environmentally-friendly substances. However, the microscopic influences of such components on CO2 hydrates are mostly unexplored. This work highlights the CO2 clathrate hydrate stability and decomposition in the existence of organic additives through classical molecular dynamics (MD) simulations. The results can help to understand the molecular mechanisms involved in such CO2 hydrate systems which may also aid to find the more efficient organic promoters for HB applications
Extended and Unscented Kalman Filters for Artificial Neural Network Modelling of a Nonlinear Dynamical System
No full textRecently, artificial neural networks, especially feedforward neural networks, have been widely used for the identification and control of nonlinear dynamical systems. However, the determination of a suitable set of structural and learning parameter value of the feedforward neural networks still remains a difficult task. This paper is concerned with the use of extended Kalman filter and unscented Kalman filter based feed forward neural networks training algorithms. The comparisons of the performances of both algorithms are discussed and illustrated using a simulated example. The simulation results show that in terms of mean squared errors, unscented Kalman filter algorithm is superior to the extended Kalman filter and backpropagation algorithms since there are improvements between 2.45–21.48% (for training) and 8.35–29.15% (for testing). This indicates that unscented Kalman filter based feedforward neural networks learning could be a good alternative in artificial neural network models based applications for nonlinear dynamical systems
State of the Art in the Development of Adaptive Soft Sensors based on Just-in-Time Models
No full textAbstractData-driven soft sensors have gained popularity due to availability of the recorded historical plant data. The success stories of the implementations of soft sensors, however, involved some practical difficulties. Even if a good soft sensor is successfully developed, its predictive performance will gradually deteriorate after a certain time due to changes in the state of plants and process characteristics, such as catalyst deactivation and sensor and process drifts due to equipment ageing, fouling, clogging and wear, changes of raw materials and so on. To get soft sensor automatically updated, different kinds of methods have been introduced, such as Kalman filter, moving window average, recursive and ensemble methods. However, these methods have some drawbacks which motivate the development and implementation of just-in-time (JIT) model based adaptive soft sensor. This paper aims to report the current status of adaptive soft sensors based on just-in-time modelling approach. Critical review and discussion on the original and modified algorithms of the JIT modelling approach are presented. Proposed topics for future research and development are also outlined to provide a road map on the developing improved and more practical adaptive soft sensors based on JIT models
An integrated approach to artificial neural network based process modelling
No full textANN technology exploded into the world of process modelling and control in the late 1980’s. The technology shows great promise and is seen as a technology that could provide models for most systems without the need to understand the fundamental behaviour or relationships among the process variables. Today, ANN applications have been applied successfully in a number of areas of process modelling and control, with the best-established applications being in the area of inferential measurements or soft sensors.Unfortunately, ‘the free lunch did not have much meat’. Overtime, people focused more on the true capabilities and power of ANN, the ability to model nonlinear relationships in data without having to define the form of the nonlinearity. However, there is often a tendency to merely plug in the data, turn the ANN training software on, and blindly accept the results. This is probably inevitable since, to date, there are no textbooks or scientific journal papers providing an integrated and systematic approach for ANN model development addressing pre-modelling, training and postmodelling stages. Therefore, addressing issues in those three phases of ANN model development is essential to support and to improve further applications of ANN technology in the area of process modelling and control.The model development issues in pre-modelling and training phases were addressed by reviewing current practice and existing techniques. For each issue, a novel method was proposed to improve the performance of ANN models. The new approaches were tested in a variety of benchmarking studies using artificial samples and coal property datasets from power station boilers.The research work in the post-modelling stage analysis which emphasises on taking the lid off black box model, proposes a novel technique to extract knowledge from the models and simultaneously obtain better understanding of the process. Postmodelling phase issues were addressed thoroughly including construction of prediction limit, sensitivity analysis and development of mathematical representation of the trained ANN model.Confidence intervals of the ANN models were analysed to construct the prediction boundary of the model. This analysis provides useful information related to interpolation and extrapolation of the model. It also highlighted how good the ANN models can be used for extrapolation purposes.An effort based on sensitivity analysis of hidden layers is also proposed to understand the behaviours of the ANN models. Using this technique, knowledge and information are retrieved from the developed models. A comparative study of the proposed techniques and the current practice was also presented.The last topic addressed in this thesis is knowledge extraction of ANN models using mathematical analysis of the hidden layers. The proposed analysis is applied in order to open the black box of the ANN models and is implemented to simulated and real historical plant data so that useful information from those data and better understanding of the process are obtained.All in all, efforts have been made in this thesis to minimise the use of abstract mathematical language and in some cases, simplify the language so that ANN modelling theory can be understood by a wider range of audience, especially the new practitioners in ANN based modelling and control. It is hoped that the insight provided in the dissertation will provide an integrated approach to pre-modelling, training and post-modelling stages of ANN models. This ‘new guideline’ of ANN model development is unique and beneficial, providing a systematic framework for the preparation, design, evaluation and implementation of ANN models in process modelling and control in particular and prediction / forecasting tool in general
Influences of Indonesian coals on the performance of a coal-fired power plant with an integrated post combustion CO2 removal system: A comparative simulation study
No full textThe process of electric power generation from coal with and without post-combustion CO2 capture system was simulated using Aspen HYSIS ® flowsheet simulation. The simulated plant was used to investigate the effects of different ranks of Indonesian coals on CO2 emission and plant efficiency. Simulation results on the plant efficiency penalties are agreeable with the existing studies on coal-fired power plant with an integrated CO2 capture in Indonesia, China, Australia, US and most of Organization for Economic Co-operation and Development (OECD) countries. The results also indicated that the ideal fuel for power generation is anthracite since it results in the least CO2 emission and efficiency penalty. However, due to abundant reserves of lignite and sub-bituminous, most of Indonesian power plants are fuelled with these types of coals. Therefore, more efforts should be directed to minimize efficiency penalty by improving the CO2 capture systems either by using more efficient solvents or minimizing the energy usage in solvent regeneration or both
The interaction between outflow dynamics and removal of NH4+-N in a vertical flow constructed wetlands treating septage
No full textANOPHELES SUNDAICUS VEKTOR MALARIA DI DAERAH PANTAI BEKAS HUTAN MANGROVE DI KECAMATAN PADANG CERMIN, KABUPATEN LAMPUNG SELATAN, INDONESIA
No full textThis study was carried out from January 1992 to December 1993 in Sidodadi village of Padang Cermin Subdistrict of South Lampung Regency. The objective of the study is to confirm malaria vector in the study area. The potential vectors species of mangrove deforested coastal areas were caught indoors and outdoors by using night human-landing collection. One species i.e Anopheles sundaicus which was caught in October 1993 was infected with malaria parasites of both Plasmodium falciparum and P. vivax, as incriminated by enzyme-linked immunosorbent assays (ELISAs) done on mosquito. In that month, the sporozoite rate was 1.4% and the man biting rate (MBR) was 47.8 mosquitos/night-person. The transmission level was very high with an inoculation rate of 66.9% or every two days. This vector species was found throughout the year with a peak abundance in September to December. There is no correlation between the rainy season and the seasonal population density (r = - 0,37)
Reviews on drag reducing polymers
No full textPolymers are effective drag reducers owing to their ability to suppress the formation of turbulent eddies at low concentrations. Existing drag reduction methods can be generally classified into additive and non-additive techniques. The polymer additive based method is categorized under additive techniques. Other drag reducing additives are fibers and surfactants. Non-additive techniques are associated with the applications of different types of surfaces: riblets, dimples, oscillating walls, compliant surfaces and microbubbles. This review focuses on experimental and computational fluid dynamics (CFD) modeling studies on polymer-induced drag reduction in turbulent regimes. Other drag reduction methods are briefly addressed and compared to polymer-induced drag reduction. This paper also reports on the effects of polymer additives on the heat transfer performances in laminar regime. Knowledge gaps and potential research areas are identified. It is envisaged that polymer additives may be a promising solution in addressing the current limitations of nanofluid heat transfer applications
Surface tension profiles of nanofluid containing surfactant during microwave irradiation
No full text© Published under licence by IOP Publishing Ltd. Manipulation of the surface tension is useful in improving heat and mass transfer performances of nanofluids in thermal systems. In our previous study, the effect of microwave irradiation on the reduction of surface tension of nanofluids (Fe 2 O 3 ) was found even after it was turned off. In this study, a synergistic effect of microwave irradiation and surfactant addition (SDS) was investigated to obtain further surface tension reduction of nanofluid. Experimental results indicate that surfactant addition is effective for wider particle number density in reducing surface tension, and the reduction level strongly depends on the surfactant concentration. On the other hand, effect of the number density on the surface tension reduction is less significant for the same concentration of surfactant. From the obtained data, a combination of microwave irradiation and surfactant addition shows potential to be used as a promising method to manipulate surface tension of nanofluids
Mechanism of Microwave Heating through Molecular Orbital Method and Bubble Size Profiles
No full textAbstractIn this study, mechanism of microwave heating on water was investigated through a combination of the experimental and simulation works on bubble formation. Fine bubbles were firstly observed and confirmed at the temperature below the boiling temperature of water using a reactor equipped with DLS system. It was hypothesized that thermal non-equilibrium condition such as a hot spot was formed under microwave irradiation. Secondly, the initial stage potential of bubble nucleation (clathrate), which consists of water molecules, was calculated through a molecular orbital method. From the experimental and simulation results, it was found that high energy was generated by the bubble collapse, reconstruction of water molecule clathrate, and repetition of clathrate formation and collapsing cause higher heating efficiency of microwave. Thus, it can be deduced that microwave heating is greatly influenced by clathrate formation and the collapse
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