A technical and systems engineering approach to optimising process plant availability and profitability
- Characterise system performance based on historical data.
- Understand the basics of goal setting in complex stochastic systems.
- Create phenomenological models of technical components.
- Simulate, visualise and quantify the behavior of process engineering systems.
- Derive measures to improve system performance and test them virtually.
Background and purpose
Competitiveness in the globalised process industries requires the simultaneous achievement of high technical availability and low maintenance costs. Achieving these apparently contradicting goals requires the optimisation of the production and maintenance systems.
A technical, systems engineering approach to optimising the availability and profitability of process plants is introduced. Application of the taught principles will enable the participant to make optimal decisions in complex stochastic systems.
Using a software solution, the failure behavior of technical components are simulated and their influence on the system performance is visualized and quantified. This approach enables improvement measures to be tested virtually, company resources to be invested more wisely, and business competitiveness to be measurably and significantly increased.
- Specialists and managers who deal directly or indirectly with the availability and profitability of process engineering systems.
- Experts and auditors who wish to supplement their existing know-how with newer methods.
Each participant will receive a copy of the presentation material and a certificate of attendance. Please ensure to bring a calculator to the seminar.
Materials and Systems Reliability Engineer
RAMS Mentat GmbH
Mr. Kelleher founded RAMS Mentat GmbH after many years of industrial experience (1999 to 2021) in diverse fields of safety and reliability engineering at reknowned companies in Australia, England and Germany, including: The Welding Institute, ExxonMobil, QinetiQ Aerostructures, Bayer and Covestro.
Dates and locations
The next seminar – virtual or (preferably) in person – is currently being planned for early 2022 in Dormagen, Germany. Do not hesitate to register your interest.
RAMS Mentat GmbH
41540 Dormagen, Germany
Tel. +49 (0) 2133 778 0198
Mob. +49 (0) 151 1767 0778
Day 1: 09:00 to 16:00
Day 2: 09:00 to 16:00
Welcome, organization, seminar goals
- The changing chemicals industry; increasing challenges for plant operators and the need for optimal decisions.
Principles of systems reliability engineering
- Properties and behaviour of complex stochastic systems.
- Define, measure and estimate system performance.
- Target setting in complex stochastic systems.
- Company culture as a key driver for continuous improvement.
The RAMS Mentat approach and its Use Cases
- The need for a complex solution system.
- In the design of new systems or the expansion of existing systems.
- In the continuous improvement of existing systems.
- Integration with existing business processes.
Probability basics and discrete event simulation
- Discrete and continuous random variables.
- Histograms, probability plots, probability density functions and cumulative distribution functions.
- Continuous distributions, selection and parameter estimation.
Modeling of stochastic, discretely evolving systems
- Discrete event simulation.
- Deterministic and stochastic models.
Terms and definitions in reliability engineering
- Useful formulae.
- Failure and degradation of technical components.
- Maintenance tasks and generalised maintenance processes.
Statistical analysis of failure and survival data
- Single component and fleet data.
- Dealing with censored data.
- Libraries of failure and repair data.
Modeling the failure behavior of technical components
- Phenomenological models of technical components.
- Investigate the effect of various maintenance strategies on component availability and system performance.
- Forecast spare part demand and optimise inventories.
Modeling and optimization of process plants
- Configuration of system components.
- Model flows, storage tanks, supply chains and operational logic.
- Visualise system behavior and quantify system performance.
- Identify and test measures to improve system performance.
- Open discussion and experience exchange.