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Related Concept Videos

Fault Types01:18

Fault Types

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When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Bus Impedance Matrix01:24

Bus Impedance Matrix

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Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
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Therapeutic Drug Monitoring: Drug Analysis Methods01:26

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Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Quantitative FTA using Monte Carlo analyses in a pharmaceutical plant.

Mayre Aparecida Borges da Costa1, Amanda Lemette T Brandão2, Jorge G F Santos3

  • 1Departamento de Análises Clínicas e Toxicológicas (DACT), Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, Avenida Carlos Chagas Filho, 376, Rio de Janeiro 21941-902 RJ, Brasil.

European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences
|February 17, 2020
PubMed
Summary

This study integrated Fault Tree Analysis (FTA) and Monte Carlo simulations to evaluate pharmaceutical plant faults. The methods predict plant behavior and ensure medicine quality and operational safety.

Keywords:
Fault tree analysis (FTA)Monte CarloPharmaceutical plantQuantitative risks analysis

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Area of Science:

  • Pharmaceutical Engineering
  • Quality Risk Management
  • Process Safety

Background:

  • Pharmaceutical pilot plants require robust fault evaluation for consistent medicine production.
  • Quality risk management is crucial for ensuring the safety and efficacy of pharmaceutical products.
  • Traditional methods may need enhancement for comprehensive fault analysis in complex manufacturing.

Purpose of the Study:

  • To evaluate faults in a multipurpose pharmaceutical pilot plant for polymer particle production.
  • To integrate Fault Tree Analysis (FTA) with Monte Carlo simulations for quantitative risk assessment.
  • To apply quality risk management tools to enhance pharmaceutical production safety.

Main Methods:

  • Dividing the plant into four key processes: material receipt/sampling, purified water treatment, reaction, and lyophilization/purification.
  • Constructing Fault Tree Analyses (FTA) for critical failures within each process, considering impacts on final medicine quality.
  • Utilizing Boolean algebra for FTA reduction and Monte Carlo simulations with exponential distribution for quantitative frequency assessment.

Main Results:

  • Identification of critical failure points within the pharmaceutical pilot plant's processes.
  • Quantitative assessment of fault frequencies using Monte Carlo simulations based on literature reliability data.
  • Establishment of a predictive model for plant behavior based on failure rate data.

Conclusions:

  • The integrated FTA and Monte Carlo approach provides a reliable method for predicting pharmaceutical plant behavior.
  • Effective quality management relies on immediate correction of high failure rates to maintain operational safety.
  • This methodology supports proactive risk management in pharmaceutical manufacturing to ensure product quality and safety.