The management of a chemical inventory is necessary for safety purposes as well as for fulfilling regulatory compliance. In most academic laboratories, the management of chemicals is still being done manually, which is time-consuming. As a result, data are not updated and expired chemicals are unintentionally used. This research proposes that UHF Class 1 Gen 2 Radio Frequency Identification (RFID) technology be used in the development of a chemical inventory information system to ease chemical tracking as well as to shorten the inventory process time. An information system integrating RFID data and a web-based rule identification interface was developed. WAMP 2.2.17, PHP 5.3.5 and MySQL 5.5.8 were downloaded and a programming language was written to check the expiration date of the chemicals as well as to produce alert notification status. Wireless technology through GSM modem helped in producing alert messages using the Short Message System (SMS) of the nearly expired chemicals to the handphone of the person in charge in real time.
In this paper, a mathematical model is developed based on mass and momentum balance for carbon dioxide absorption into aqueous ammonia solution. The model is simplified based on the assumption that the CO2 absorption into aqueous ammonia is a pseudo-first-order reaction. Laplace transform method is applied in order to solve the partial differential model equation. Finally, the CO2 molar flux is expressed as a function of partial pressure of CO2, concentration of aqueous ammonia, temperature and gas-liquid contact area. Variation of CO2 molar flux with partial pressure of CO2 and temperature is discussed and a comparison is performed with experimental data from literature. Variation of CO2 molar flux is also shown with gas-liquid contact area. The calculated flux from the model follows the same trend as that of the experimental data reported in literature and the accuracy is within the accepted limit. The mathematical model is very helpful to predict the CO2 molar flux as a function of partial pressure of CO2, concentration of aqueous ammonia, temperature and gas-liquid contact area.
In this paper, we introduce T-DepExp system to simulate the transitive dependence based coalition formation (CF). It is a multi-agent based simulation (MABS) tool that aims to enhance cooperation between agents through transitive dependence. Previously, the transitive dependence was introduced by An and his colleagues for expressing the indirect dependence between agents in their cooperation. However, it did not receive much attention. Although it has a few problems need to be addressed, we try to propose our own mechanism to increase the efficiency of the transitive dependence based CF. To simulate MAS dependence relationship, we have included two fundamental dependence relationships in this MABS tool, which are AND-Dependence and OR-Dependence. In addition, the architecture of the T-DepExp system is presented and discussed. It allows possible integration of other features such as budget mechanism and trust model. Subsequently, hypothesis for the experiments and experimental setup are explained. The overall system will be demonstrated for its functionality and the experimental results will also be discussed.
Quality Function Deployment (QFD) is a structured methodology that uses customer and technical
requirements for designers and manufacturers to provide better products. Many researchers combine or
integrate the technique of QFD with other methodologies such as Theory Inventive of Problem Solving
(TRIZ) or Design for Manufacture and Assembly (DFMA) to optimise product design innovation and
improvement. The combined methodologies are even used to solve process problems. Initial literature
review of the application of stand-alone QFD poised several problems. Combining QFD with other
techniques, such as TRIZ and DFMA, has helped to address these issues and forms the basis of future
research. The integrated methods can solve main contradictory problems more precisely from product
demand analysis to product design, production and application. Review work of the literature, specifically
that on research and development of QFD, TRIZ and DFMA, showed that the said methodologies have
been widely and successfully implemented in several practical applications such as resolving conflicts
between customer and technical/engineering requirements and reducing production cost. This review work
provides an in-depth analysis of identifying and finding issues of strengths, weaknesses and outcomes
of the QFD when combined with TRIZ and also of QFD integrated with DFMA.
Cloud Computing provides a solution to enterprise applications in resolving their services at all level of Software, Platform, and Infrastructure. The current demand of resources for large enterprises and their specific requirement to solve critical issues of services to their clients like avoiding resources contention, vendor lock-in problems and achieving high QoS (Quality of Service) made them move towards the federated cloud. The reliability of the cloud has become a challenge for cloud providers to provide resources at an instance request satisfying all SLA (Service Level Agreement) requirements for different consumer applications. To have better collation among cloud providers, FLA (Federated Level Agreement) are given much importance to get consensus in terms of various KPI’s (Key Performance Indicator’s) of the individual cloud providers. This paper proposes an FLASLA Aware Cloud Collation Formation algorithm (FS-ACCF) considering both FLA and SLA as major features affecting the collation formation to satisfy consumer request instantly. In FS-ACCF algorithm, fuzzy preference relationship multi-decision approach was used to validate the preferences among cloud providers for forming collation and gaining maximum profit. Finally, the results of FS-ACCF were compared with S-ACCF (SLA Aware Collation Formation) algorithm for 6 to 10 consecutive requests of cloud consumers with varied VM configurations for different SLA parameters like response time, process time and availability.
The present paper deals with the novel approach for clustering using the image feature of stabilization diagram for automated operational modal analysis in parametric model which is stochastic subspace identification (SSI)-COV. The evolution of automated operational modal analysis (OMA) is not an easy task, since traditional methods of modal analysis require a large amount of intervention by an expert user. The stabilization diagram and clustering tools are introduced to autonomously distinguish physical poles from noise (spurious) poles which can neglect any user interaction. However, the existing clustering algorithms require at least one user-defined parameter, the maximum within-cluster distance between representations of the same physical mode from different system orders and the supplementary adaptive approaches have to be employed to optimize the selection of cluster validation criteria which will lead to high demanding computational effort. The developed image clustering process is based on the input image of the stabilization diagram that has been generated and displayed separately into a certain interval frequency. and standardized image features in MATLAB was applied to extract the image features of each generated image of stabilisation diagrams. Then, the generated image feature extraction of stabilization diagrams was used to plot image clustering diagram and fixed defined threshold was set for the physical modes classification. The application of image clustering has proven to provide a reliable output results which can effectively identify physical modes in stabilization diagrams using image feature extraction even for closely spaced modes without the need of any calibration or user-defined parameter at start up and any supplementary adaptive approach for cluster validation criteria.
The development and application of advanced materials i.e. nanomaterials are important for the technology revolution and economic progress of the country. However, the potential health risk arising from nanomaterials become a major concern. Given the fact that both particulate and molecular identity of nanomaterials is responsible for the biological effects, the effects of nanomaterial exposure cannot be predicted based on the current understanding of their bulk properties. The lack of nanomaterials data for safety assessment become a major challenge to implement safe work practice at nanomaterials related industries. To resolve the aforementioned problem, a conceptual framework for hazard assessment of nanomaterials is presented in this study. Bayesian Network (BN) is used to support hazard assessment according to the guideline issued by the Department of Occupational Safety and Health (DOSH) Malaysia. The understanding of the hazard is crucial to encourage the development of an action plan to ensure the safety aspect while processing and handling nanomaterials.
Working at heights has always been a concern for the process industry and construction industry. According to recent statistics, falls from heights are the leading cause for at least a third of all construction accidents, based on the accident reports from Malaysia, United States of America (USA) and Great Britain. Therefore, the aim of this research is to investigate the contributing factors for fatal accidents due to falls from heights based on published official data by government agencies such as Department of Occupational Safety and Health (DOSH) in Malaysia and Occupational Safety and Health and Administration (OSHA) in the USA. Thorough risk analysis of accidents due to falls from heights is needed as a step to improve the safety of workers and reduce the number of fatalities. The methodology used was developed through an in-depth literature review from relevant publications that discussed falls from heights investigations ranging from individual characters to management commitment and site conditions. The data is available and accessible to the public via OSHA and DOSH and can be obtained by browsing their websites. The data were analysed based on the relevant understanding of the description mentioned in the accident reports and its relationship with the contributing factors. Eventually, from the analysis, the factors which affect more the numbers of fatal accidents due to falls from heights were obtained. In this study, 105 fatal accidents due to falls from heights from Malaysia data and 101 fatal accidents from USA data were selected through a precise selecting process and analysed to determine which factor was the highest contributor to increasing the number of fatal workplace accidents due to falls from heights. Results indicate that a lack of effective management is the highest contributor to falls from height fatal accidents.
Pressure relief system is a system to prevent overpressure inside protected equipment that exceeds its maximum allowable working pressure (MAWP) to disposal treatment. Relief system is designed on two different plant case studies, which are dimethyl-ether and ethylbenzene plants by using conventional design procedure. Nevertheless, the conventional design steps are not considering cost optimization of plant installed with relief system. Thus, the design pressure of protected equipment, piping diameter, and disposal treatment is set to be manipulated variables to determine the cost minimization. Pressure drops of inlet piping and backpressure are as constraint variables due to standard requirements. The standards state that inlet piping pressure drops should be below 3% of set pressure and outlet piping pressure drop to set pressure percentage based on range to determined types of the relief valve to be used. From that, optimum plant design with consideration of pressure relief system’s installation can be achieved by calculating the total cost of plant designed with relief system when set pressures are changed. As a result, dimethyl-ether plant shows a minimum point of the total cost at 170% of set pressure increment, which is lower than its original design. On the other hand, ethylbenzene plant gives optimum point at original design as the total cost is higher at set pressure increment.
Vapor cloud explosion is one of the major threats to Floating, Production, Storage and Offloading (FPSO) facilities due to its congested and confined nature. Reduction in explosion overpressure can be achieved by improving the ventilation in FPSO. During early design stage of FPSO, designers consider providing grated process decks to improve the ventilation. However, there is limited research on the comparison of the explosion overpressure between the grated deck and the traditional plated deck. In this study, Vapor Cloud Explosion perspective of plated versus grated process deck in typical FPSO was evaluated by utilizing Det Norske Veritas’s (DNV) SAFETI OFFSHORE modelling tool. Representative leak scenarios were selected based on frequency analysis of major accident hazards associated with typical FPSO facility. This study revealed that the overpressure exceedance frequency in plated process deck was higher than the grated process deck for the selected scenario. This serves as quantitative guidance for designers to select an inherently safer type of decks in FPSOs from explosion perspective during the preliminary design stage. However, a detailed Computational Fluid Dynamics (CFD) study is recommended to get an insight of dangers associated with the presence of plated and grated process decks in FPSO, by considering all the parameters and conditions applicable.
Maintenance acts as a significant role in smoothening the operations in power plants. Risk and failure are some of the common problems in power plant leading to unexpected outages such as boiler shutdown or tube leakage. The rectification of these problems requires ceasing operations of the boiler which leads to a loss in the revenue annually. Therefore, this work was focused on prioritizing the maintenance activities and optimize the operational duration and cost by implementing risk-based maintenance (RBM) and particle swarm optimization (PSO). Previous literature implores that, RBM is commonly used in oil and gas industries to predict the risk or failure of the equipment. In this work, the RBM method was adopted accordingly to the power plant industries. The methodology is segregated into two main phases. First, the ranking and prioritization maintenance activities were performed using RBM. Then, the optimization of the operational duration and cost were simulated by PSO approached in MATLAB. The main outcome of this research is to act as a reference in adopting the best approaches to improve the power plant performance.
In natural gas processing, carbon dioxide is one of the major streams contaminate from the gas reservoir and must be removed, as it reduces the energy content of sales gas. In order to remove huge amounts of carbon dioxide (CO2), gas hydrate is used to capture CO2. However, the high formation of hydrate will inhibit clogs in gas pipelines. Hence, this research presents literature on hydrate formation, prediction of methane-carbon dioxide (CH4-CO2) gas mixture, by using simulation packages of Aspen Hysys 7.2, K-factor, VMG SIM Ver. 6 and BR & E ProMax 2.0. Peng-Robinson model was used in all simulation packages. Simulation results obtained were then compared with experimental data from previous literature sources. The simulation results showed that the higher the concentration of CO2 in CH4-CO2 gas mixture, the lower the pressure required for CO2 hydrate to form throughout the predicted temperature range from -20°C to 20°C.
In household products, specific chemical ingredients are used to satisfy the desired target properties as required by the consumers. However, some of these ingredients may result in safety risks and adverse health effects. Early consideration of safety and health aspects during product design is vital to minimize the impact on consumers. Safety and health aspects have not been strongly emphasized before in many product design methodologies. Therefore, a systematic methodology is proposed to assess the safety and health effects of the potential ingredients, before they are used in the product formulation. The chemical ingredient candidate may be a novel ingredient or a typical ingredient used in formulated product design. In this work, a computer-aided molecular design (CAMD) technique was used to design the novel ingredient candidates with the integration of safety and health aspects. Then, the safety and health performance of the ingredient candidates were assessed by inherent safety and health sub-indexes. Each safety or health parameter was assigned with a score, based on the degree of the potential hazards. A higher score was given to the ingredients with higher safety risk or more severe health effect, and vice versa. The result of the safety and health assessment based on the score allocation had contributed to the selection of chemical ingredient. This new approach ensures that the selected ingredient possesses desirable properties as well as low safety and health effects. A case study on surfactant design is presented to illustrate the incorporation of safety and health aspects into product design methodologies.
Composite repairs have been increasingly applied for maintenance and rehabilitation of piping, pipelines and vessels in the oil and gas industry, thus there is a growing need to monitor their in-service integrity, repair lifetime extension and prevent loss of containment of the product. There are many challenges of inspecting composite repairs including accessibility, inhomogeneous and anisotropic structure of composites, probability of detection, lack of adequate standards and diversity of composite materials amongst others. The current practice for inspection and monitoring of composites repair on oil and gas piping and pipelines is usually conducted based on International Standards Organisation (ISO) 24817 whereby visual inspection is generally performed to observe any irregularities on the surface like discolouration, cracks, chalking and blistering. This will usually be followed through with a coin tap test and Barcol hardness testing. Upon any findings of anomalies, further investigation is then performed using advance non-destructive testing (NDT) inspection technique to determine the integrity of the wrap, depending on the type and severity of defects. ISO 24817 has stated the general techniques that can be used to inspect the composites overwrap repairs including Ultrasound Technique, Radiography and Acoustic Emission. However, Petroliam Nasional Berhad (PETRONAS) has performed a series of assessments on various inspection techniques to seek suitable inspection methods for the composite wrap system, composites/substrate interface and/ or substrate. A total of 10 NDT techniques had been evaluated thus far including Laser Shearography, X-Ray, Microwave technique, Dynamic Response Spectroscopy (DRS), Acoustic Emission (AE), Computed Radiography (CRT), Pulse Eddy Current, Metal Magnetic Memory (MMM). This research summarises an overview of the effectiveness of the evaluated techniques and findings of the evaluation.
The potential of a major accident may significantly increase when change is unprepared especially in a temporary and emergency change. An unplanned change may lead to the emergence of new hazards which eventually lead towards severe impact on human, property, environment and business reputation. Management of Change (MOC) with integrated risk analysis is an important Process Safety Management (PSM) elements involving planning and controlling risks and hazards that come with the proposed change. However, lacking systematic technique for easy adoption of this element has delayed its application in plant. Corresponding to these weaknesses, an integrated MOC management system focusing on the temporary and emergency change is presented in this study. Results of this study comprise MOC process framework and MOC management system (MOCMS) which act as a guidance and documentation inventory tool. Implementation of this technique and system at the selected plant as a case study is examined and discussed. The system is beneficial to industries to manage underlying risks in a temporary and emergency change which ease the tracking of MOC case inventory to improve risk controls in changes.
Leaks and breakdowns of pipelines can lead to catastrophic failures and cause economical losses worldwide. Currently, condition monitoring has become a challenging process because of various reasons such as fluctuating external conditions, natural hazards. Pipelines are installed in severe conditions and are subjected to degradation mainly due to corrosion and metal loss. This study attempted to classify different types of metal loss faults using historical inspection data of oil and gas fields. For this purpose, Support Vector Machines (SVM) were employed to classify and predict various types of metal loss faults which were affecting the life condition of a crude oil pipeline. The historical inspection data was acquired from a crude oil pipeline located in Sudan. Different types of SVM models were trained and quadratic SVM type was selected for the present study due to its high prediction accuracy. The performance evaluation of the proposed SVM model was done using the confusion matrix. The developed SVM model provides promising results with a prediction accuracy of 93.0%. As a result, the fault detection rate (FDR) for all faults is found to be 90.4%, while the misclassification rate (MR) is 9.6%. The prediction of metal loss fault type may help in condition assessment and maintenance schedule to take prior actions for the better life of pipeline which reduces the degradation rate of a pipeline.
The information on the combustion properties of local timber is crucial in Malaysia as the archival material related to this subject matter is found to be very limited in scope and incomplete. The heat release rate (HRR) is the most precious variable of combustion properties as it provides the key to understand and quantify the hazard in fires. Thus, this work is to verify the reliability of the HRR obtained from cone calorimeter tests conducted upon six Malaysian wood species: Shorea laevis, Vatica rassak, Koompassia malaccensis, Heritiera, Shorea parvifolia and Cratoxylum arborescens. The single factor one-way analysis of variance (ANOVA) was used to investigate statistically significant differences between the means of the HRR dataset of each species during the combustion tests at three different heat fluxes. Later, the confidence interval estimation was occupied to determine the range around the HRR dataset, where the means of the data was likely to be found. The intraclass correlation coefficient (ICC) test was also implemented to assess the reliability of the heat release rate data obtained from the cone calorimeter test. From the surveillance, the P-values of all the six species were higher than α = 0.05, insinuating that the difference between the means of the dataset was not statistically significant. The confidence interval values consisting of the upper bound and lower bound limits indicate that the certainty that these ranges contain the true mean of the heat release rate dataset is 95%. Finally, the fact that heat release data received from the cone calorimeter test were highly reliable to statistically calculate the variation in measurements taken by a single instrument under the same condition confirmed by the ICC’s values of 0.82 to 0.99 that reflect good to excellent correlations.
Graphene oxide (GO) is a reliable additive used to improve the wax crystal inhibition performance of pour point depressant (PPD). In understanding the lateral size effect on wax crystal inhibition, PPD emulsions containing graphene oxide of different lateral sizes were prepared. The parameters of pour point reduction (PPR) and dissipated wax crystallisation enthalpy were considered in the assessment of wax inhibition performance. PPR was measured using a pour point tester in accordance to ASTM D-97, while the enthalpy dissipation was evaluated via differential scanning calorimetry (DSC) under cooling cycle. The study revealed that the addition of GO lowered the wax crystallisation enthalpy, as indicated by the lesser amount of precipitated wax present in the model oil. The enthalpy underwent a decrease from 25.04 to 23.97 Jg-1 with decreasing GO lateral size. It is evident from the pour point test that the use of different GO lateral sizes significantly affects the wax inhibition, as verified through the highest PPR of 10o C displayed by the EJGO5 sample (with addition of the smallest lateral size of 1 µm). In short, manipulating the GO lateral size in sonicated samples boosted the PPR up to 100% compared to the unsonicated sample (EJGO1). Furthermore, smaller lateral size provides more nucleation site for wax co-crystallisation, hence preventing the neighbouring wax crystals from attaching and forming a looser crystal structure. The thermal characteristics of the PPD emulsions were also examined through DSC technique, which revealed that the emulsions thermal properties were unaffected by the addition of GO.
The new emergence and re-emergence of arbovirus infections transmitted by Aedes mosquitoes have been spreading across Southeast Asia, Central Africa, United States, tropical Oceania and has become a major of public health concern. These arbovirus diseases were found to have a similar vector, symptoms, and environments. The situation is complex due to no specific vaccine or treatments being available for the diseases. Therefore, vector control is currently the best defense against arbovirus diseases, but with its own challenges such as the difficulty in controlling scattered breeding sites and biological behavior. Herein, we present a literature review of studies on current techniques proposed to combat dengue transmission that can fill a crucial gap in vector control programs, which is the inability of conventional control methods to eliminate and destroy cryptic breeding sites. In particular, we focused on the concept of autodissemination, which is a self-delivery technique by manipulating the behavior of mosquitoes, carrying the insecticide and disseminating it to cryptic breeding sites. This technique has shown promising results in some countries and can be considered as an additional tool in a vector control program. Therefore, we conducted Boolean searches in several electronic databases including Google Scholar, PubMed, SciELO and ScienceDirect to identify relevant published data regarding dengue and autodissemination techniques.
Familial hypercholesterolaemia (FH) is a genetic disease that causes the elevation of lowdensity lipoprotein cholesterol (LDL-C), which subsequently leads to premature coronary heart disease (CHD). Features which have been reported to be associated with FH include lipids level, tendon xanthomata, and history of CHD. The Ordinal Logistic Regression model using the classification of FH patients with the Dutch Lipid Clinic Network Criteria (DLCN) as the dependent variable (where 1=Possible, 2=Probable, 3=Definite) was developed and evaluated for different types of link functions. The FH patients (n = 449) were recruited from health screening programmes conducted in hospitals and clinics in Malaysia from 2010 to 2018. Results indicate there is a significant association between FH categories with demographic factors (ethnicity and smoking) and physical symptoms (corneal arcus and xanthomata). The Ordinal Logistic Regression using Cauchit link function has lower Akaike Information Criterion (AIC) value, higher Nagelkerke’s R-Square and classification accuracy compared to Probit and Logit link function, diastolic blood pressure, corneal arcus and xanthomata were found to be significant covariates of FH.