1,615 research outputs found
Alternative sweetener from curculigo fruits
This study gives an overview on the advantages of Curculigo Latifolia as an alternative sweetener and a health product. The purpose of this research is to provide another option to the people who suffer from diabetes. In this research, Curculigo Latifolia was chosen, due to its unique properties and widely known species in Malaysia. In order to obtain the sweet protein from the fruit, it must go through a couple of procedures. First we harvested the fruits from the Curculigo trees that grow wildly in the garden. Next, the Curculigo fruits were dried in the oven at 50 0C for 3 days. Finally, the dried fruits were blended in order to get a fine powder. Curculin is a sweet protein with a taste-modifying activity of converting sourness to sweetness. The curculin content from the sample shown are directly proportional to the mass of the Curculigo fine powder. While the FTIR result shows that the sample spectrum at peak 1634 cm–1 contains secondary amines. At peak 3307 cm–1 contains alkynes
How explicit are the barriers to failure in safety arguments?
Safety cases embody arguments that demonstrate how safety properties of a system are upheld. Such cases implicitly document the barriers that must exist between hazards and vulnerable components of a system. For safety certification, it is the analysis of these barriers that provide confidence in the safety of the system. The explicit representation of hazard barriers can provide additional insight for the design and evaluation of system safety. They can be identified in a hazard analysis to allow analysts to reflect on particular design choices. Barrier existence in a live system can be mapped to abstract barrier representations to provide both verification of barrier existence and a basis for quantitative measures between the predicted barrier behaviour and performance of the actual barrier. This paper explores the first stage of this process, the binding between explicit mitigation arguments in hazard analysis and the barrier concept. Examples from the domains of computer-assisted detection in mammography and free route airspace feasibility are examined and the implications for system certification are considered
A log mining approach for process monitoring in SCADA
SCADA (Supervisory Control and Data Acquisition) systems are used for controlling and monitoring industrial processes. We propose a methodology to systematically identify potential process-related threats in SCADA. Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process. To detect such threats, we propose a semi-automated approach of log processing. We conduct experiments on a real-life water treatment facility. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular process workflow
On the role of Prognostics and Health Management in advanced maintenance systems
The advanced use of the Information and Communication Technologies is evolving the way that systems are managed and maintained. A great number of techniques and methods have emerged in the light of these advances allowing to have an accurate and knowledge about the systems’ condition evolution and remaining useful life. The advances are recognized as outcomes of an innovative discipline, nowadays discussed under the term of Prognostics and Health Management (PHM). In order to analyze how maintenance will change by using PHM, a conceptual model is proposed built upon three views. The model highlights: (i) how PHM may impact the definition of maintenance policies; (ii) how PHM fits within the Condition Based Maintenance (CBM) and (iii) how PHM can be integrated into Reliability Centered Maintenance (RCM) programs. The conceptual model is the research finding of this review note and helps to discuss the role of PHM in advanced maintenance systems.EU Framework Programme Horizon 2020, 645733 - Sustain-Owner - H2020-MSCA-RISE-201
Data-Based Semi-Automatic Hazard Identification for More Comprehensive Identification of Hazardous Scenarios
As chemical process plants have become more involved and complex, the likelihood of hazardous incidents has increased simultaneously. That is, the more complex a facility’s systems, the more factors engineers must consider. This results in a higher likelihood of potential hazards being overlooked; thus, the possibility of incidents occurring increases.
Many companies and organizations are struggling to identify their weaknesses and reduce hazardous issues by developing hazard identification (HAZID) tools, particularly for large and complex processes. Even though a considerable number of companies merely pursue this objective to conform to government regulations, their efforts play a critical role in improving their reputations and financial profits. Therefore, the advancement of HAZID tools in the process industries has taken significant strides over the last 40 years.
Despite the substantial development of HAZID methods, traditional HAZID tools need further development because of their weaknesses in identifying possible hazards. In other words, it is evident that unintended incidents that occasionally occur in the chemical process industry require more enhanced HAZID methodologies. Therefore, this study attempts to ascertain the drawbacks of existing HAZID tools so that a new HAZID methodology, data-based semi-automatic hazard identification (DAHAZID), is proposed.
Considering potential HAZID methodologies, this study seeks to identify possible scenarios with a semi-automatic and systemic approach. Based on the two traditional HAZID tools, Hazard Operability study (HAZOP) and Failure Mode, Effects, and Criticality Analysis (FMECA), the DAHAZID method will minimize the limitations of each individual method. Additionally, rather than depending on the HAZID tools to achieve the connectivity of the process system, this study will consider connections with other new technologies in advance. Then, this method can be integrated with proper guidelines regarding process design and safety analysis. To examine its usefulness, the method will be applied to two case studies, and its outcome will be compared to the actual result, performed previously by a traditional HAZOP meeting.
Hopefully, this research can contribute to the further development of the process safety field in practice
Computer-aided HAZOP of batch processes
The modern batch chemical processing plants have a tendency of increasing
technological complexity and flexibility which make it difficult to control the
occurrence of accidents. Social and legal pressures have increased the demands
for verifying the safety of chemical plants during their design and operation.
Complete identification and accurate assessment of the hazard potential in the
early design stages is therefore very important so that preventative or protective
measures can be integrated into future design without adversely affecting
processing and control complexity or capital and operational costs. Hazard and
Operability Study (HAZOP) is a method of systematically identifying every
conceivable process deviation, its abnormal causes and adverse hazardous
consequences in the chemical plants. [Continues.
An investigation into hazard-centric analysis of complex autonomous systems
This thesis proposes a hypothesis that a conventional, and essentially manual, HAZOP process can be
improved with information obtained with model-based dynamic simulation, using a Monte Carlo
approach, to update a Bayesian Belief model representing the expected relations between cause and
effects – and thereby produce an enhanced HAZOP. The work considers how the expertise of a
hazard and operability study team might be augmented with access to behavioural models,
simulations and belief inference models. This incorporates models of dynamically complex system
behaviour, considering where these might contribute to the expertise of a hazard and operability study
team, and how these might bolster trust in the portrayal of system behaviour. With a questionnaire
containing behavioural outputs from a representative systems model, responses were collected from a
group with relevant domain expertise. From this it is argued that the quality of analysis is dependent
upon the experience and expertise of the participants but this might be artificially augmented using
probabilistic data derived from a system dynamics model. Consequently, Monte Carlo simulations of
an improved exemplar system dynamics model are used to condition a behavioural inference model
and also to generate measures of emergence associated with the deviation parameter used in the study.
A Bayesian approach towards probability is adopted where particular events and combinations of
circumstances are effectively unique or hypothetical, and perhaps irreproducible in practice.
Therefore, it is shown that a Bayesian model, representing beliefs expressed in a hazard and
operability study, conditioned by the likely occurrence of flaw events causing specific deviant
behaviour from evidence observed in the system dynamical behaviour, may combine intuitive
estimates based upon experience and expertise, with quantitative statistical information representing
plausible evidence of safety constraint violation. A further behavioural measure identifies potential
emergent behaviour by way of a Lyapunov Exponent. Together these improvements enhance the
awareness of potential hazard cases
Combining qualitative and quantitative reasoning to support hazard identification by computer
This thesis investigates the proposition that use must be made of quantitative
information to control the reporting of hazard scenarios in automatically generated
HAZOP reports.
HAZOP is a successful and widely accepted technique for identification of process
hazards. However, it requires an expensive commitment of time and personnel near
the end of a project. Use of a HAZOP emulation tool before conventional HAZOP
could speed up the examination of routine hazards, or identify deficiencies I in the
design of a plant.
Qualitative models of process equipment can efficiently model fault propagation in
chemical plants. However, purely qualitative models lack the representational power
to model many constraints in real plants, resulting in indiscriminate reporting of
failure scenarios.
In the AutoHAZID computer program, qualitative reasoning is used to emulate
HAZOP. Signed-directed graph (SDG) models of equipment are used to build a graph
model of the plant. This graph is searched to find links between faults and
consequences, which are reported as hazardous scenarios associated with process
variable deviations. However, factors not represented in the SDG, such as the fluids in
the plant, often affect the feasibility of scenarios.
Support for the qualitative model system, in the form of quantitative judgements to
assess the feasibility of certain hazards, was investigated and is reported here. This
thesis also describes the novel "Fluid Modelling System" (FMS) which now provides
this quantitative support mechanism in AutoHAZID. The FMS allows the attachment
of conditions to SDG arcs. Fault paths are validated by testing the conditions along
their arcs. Infeasible scenarios are removed.
In the FMS, numerical limits on process variable deviations have been used to assess
the sufficiency of a given fault to cause any linked consequence. In a number of case
studies, use of the FMS in AutoHAZID has improved the focus of the automatically
generated HAZOP results.
This thesis describes qualitative model-based methods for identifying process hazards
by computer, in particular AutoHAZID. It identifies a range of problems where the
purely qualitative approach is inadequate and demonstrates how such problems can be
tackled by selective use of quantitative information about the plant or the fluids in it.
The conclusion is that quantitative knowledge is' required to support the qualitative
reasoning in hazard identification by computer
Towards securing SCADA systems against process-related threats
We propose a tool-assisted approach to address process-related threats on SCADA systems. Process-related threats have not been addressed before in a systematic manner. Our approach consists of two steps: threat analysis and threat\ud
mitigation. For the threat analysis, we combine two methodologies (PHEA and HAZOP) to systematically identify process-related threats. The threat mitigation is supported by our tool, MELISSA, that helps to detect incidents (attacks or user mistakes). MELISSA uses SCADA system logs and visualization techniques to highlight potential incidents. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular SCADA process work-flow
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