Estimation of the Optimal Statistical Quality Control Sampling Time Intervals Using a Residual Risk Measure

Background: An open problem in clinical chemistry is the estimation of the optimal sampling time intervals for the application of statistical quality control (QC) procedures that are based on the measurement of control materials. This is a probabilistic risk assessment problem that requires reliability analysis of the analytical system, and the estimation of the risk caused by the measurement error. Methodology/Principal Findings: Assuming that the states of the analytical system are the reliability state, the maintenance state, the critical-failure modes and their combinations, we can define risk functions based on the mean time of the states, their measurement error and the medically acceptable measurement error. Consequently, a residual risk measure rr can be defined for each sampling time interval. The rr depends on the state probability vectors of the analytical system, the state transition probability matrices before and after each application of the QC procedure and the state mean time matrices. As optimal sampling time intervals can be defined those minimizing a QC related cost measure while the rr is acceptable. I developed an algorithm that estimates the rr for any QC sampling time interval of a QC procedure applied to analytical systems with an arbitrary number of critical-failure modes, assuming any failure time and measurement error probability density function for each mode. Furthermore, given the acceptable rr, it can estimate the optimal QC sampling time intervals

Comparative LCA technology improvement opportunities for a 1.5 MW wind turbine in the context of an offshore wind farm

Wind energy is playing an increasingly important role in the development of cleaner and more efficient energy technologies leading to projections in reliability and performance of future wind turbine designs. This paper presents life cycle assessment (LCA) results of design variations for a 1.5 MW wind turbine due to the potential for advances in technology to improve the performance of a 1.5 MW wind turbine. Five LCAs have been conducted for design variants of a 1.5 MW wind turbine. The objective is to evaluate potential environmental impacts per kilowatt hour of electricity generated for a 114 MW onshore wind farm. Results for the baseline turbine show that higher contributions to impacts were obtained in the categories Ozone Depletion Potential, Marine Aquatic Eco-toxicity Potential, Human Toxicity Potential and Terrestrial Eco-toxicity Potential compared to Technology Improvement Opportunities (TIOs) 1 to 4. Compared to the baseline turbine, TIO 1 showed increased impact contributions to Abiotic Depletion Potential, Acidification Potential, Eutrophication Potential, Global Warming Potential and Photochemical Ozone Creation Potential, and TIO 2 showed an increase in contributions to Abiotic Depletion Potential, Acidification Potential and Global Warming Potential. Additionally, lower contributions to all the environmental categories were observed for TIO 3 while increased contributions towards Abiotic Depletion Potential and Global Warming Potential were noted for TIO 4. A comparative LCA study of wind turbine design variations for a particular power rating has not been explored in the literature. This study presents new insight into the environmental implications related with projected wind turbine design advancements

Optimal spare parts management for vessel maintenance scheduling

Condition-based monitoring is used as part of predictive maintenance to collect real-time information on the healthy status of a vessel engine, which allows for a more accurate estimation of the remaining life of an engine or its parts, as well as providing a warning for a potential failure of an engine part. An engine failure results in delays and down-times in the voyage of a vessel, which translates into additional cost and penalties. This paper studies a spare part management problem for maintenance scheduling of a vessel operating on a given route that is defined by a sequence of port visits. When a warning on part failure is received, the problem decides when and to which port each part should be ordered, where the latter is also the location at which the maintenance operation would be performed. The paper describes a mathematical programming model of the problem, as well as a shortest path dynamic programming formulation for a single part which solves the problem in polynomial time complexity. Simulation results are presented in which the models are tested under different scenarios

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Comparison of multi-objective optimisation tools for building performance simulation with TRNSYS 18

Recent progress in computer science has led to applications of simulation-based optimisation methods for building design. This application-focused paper compares two generic optimisation tools: Multi-Objective Building Performance Optimisation (MOBO) and Design Analysis Kit for Optimisation and Terascale Applications (DAKOTA). The workflow and coupling of each tool with TRNSYS 18 software are presented. Results show that computing times were comparable, and both tools display similar optimal solutions. MOBO, specifically developed for building performance optimisation, is a user-friendly software, whereas DAKOTA requires a steep learning curve for non-programmers. Conversely, DAKOTA provides flexibility in interfacing the simulation software and defining the optimisation settings