Cost optimization of maintenance scheduling for wind turbines with aging components

A major part of the wind turbine operation cost is resulted from the maintenance of its components. This thesis deals with the theory, algorithms, and applications concerning minimization of the maintenance cost of wind power turbines, using mathematical modelling to find the optimal schedules of preventive maintenance activities for multi-component systems.\ua0 \ua0 The main contributions of this thesis are covered by the four papers appended. The unifying goal of these papers is to produce new optimization models resulting in effective and fast algorithms for preventive maintenance time schedules. The features of the multi-component systems addressed in our project are: aging components, long-term, and short-term planning, planning for a wind power farm, end of the lifetime of the wind farm, maintenance contracts, and condition monitoring data.\ua0 \ua0 For the long-term maintenance planning problem, this thesis contains an optimization framework that recognizes different phases of the wind turbine lifetime. For short-term planning problem, this thesis contains two modeling frameworks, which both focus on the planning of the next preventive maintenance activities. Our virtual experiments show that the developed optimization models adopt realistic assumptions and can be accurately solved in seconds. One of these two frameworks is further extended so that available condition monitoring data can be incorporated for regular updates of the components\u27 hazard functions. In collaboration with the Swedish Wind Power Technology Center at Chalmers and its member companies, we test this method with real-world wind farm data. Our case studies demonstrate that this framework may result in remarkable savings due to the smart scheduling of preventive maintenance activities by monitoring the ages of the components as well as operation data of the wind turbines. \ua0 \ua0 We believe that in the future, the proposed optimization model for short-term planning based on the component age and condition monitoring data can be used as a key module in a maintenance scheduling app

Optimal Maintenance Schedule for a Wind Power Turbine with Aging Components

Wind power is one of the most important sources of renewable energy available today. A large part of the cost of wind energy is due to the cost of maintaining wind power equipment. When a wind turbine component fails to function, it might need to be replaced under circumstances that are less than ideal. This is known as corrective maintenance. To minimize unnecessary costs, a more active maintenance policy based on the life expectancy of the key components is preferred. Optimal scheduling of preventive maintenance activities requires advanced mathematical modeling. In this paper, an optimal preventive maintenance algorithm is designed using the renewal-reward theorem. In the multi-component setting, our approach involves a new idea of virtual maintenance that allows us to treat each replacement event as a renewal event even if some components are not replaced by new ones. The proposed optimization algorithm is applied to a four-component model of a wind turbine, and the optimal maintenance plans are computed for various initial conditions. The modeling results clearly show the benefit of PM planning compared to a pure CM strategy (about 30% lower maintenance cost)

Optimal scheduling of the next preventive maintenance activity for a wind farm

Global warming has been attributed to increased greenhouse gas emission concentrations in the atmosphere through the burning of fossil fuels. Renewable energy, as an alternative, is capable of displacing energy from fossil fuels. Wind power is abundant, renewable, and produces almost no greenhouse gas during operation. A large part of the cost of operations is due to the cost of maintaining the wind power equipment, especially for offshore wind farms. How to reduce the maintenance cost is what this article focus on. This article presents a binary linear optimisation model whose solution may suggest wind turbine owners which components, and when, should undergo the next preventive maintenance (PM). The scheduling strategy takes into account eventual failure events of the multi-component system, in that after the failed system is repaired, the previously scheduled PM plan should be updated treating the restored components to be as good as new. The optimisation model NextPM is tested through three numerical case studies. The first study addresses the illuminating case of a single component system. The second study analyses the case of seasonal variations of set-up costs, as compared to the constant set-up cost setting. Among other things, this analysis reveals a dramatic cost reduction achieved by the NextPM model as compared to the the pure CM strategy. In these two case studies, the cost are reduced by around 35%. The third case study compares the NextPM model with another optimisation model preventive maintenance scheduling problem with interval costs(PMSPIC) which was the major source of inspiration for this article. This comparison demonstrates that the NextPM model is accurate and much more effective. In conclusion, the NextPM model is both accurate and fast to solve. The algorithm stemming from the proposed model can be used as a key module in a maintenance scheduling app.Comment: 16 pages, 2 figures. Submitted to Wind Energy Scienc

Involvement of Reduced Microbial Diversity in Inflammatory Bowel Disease

A considerable number of studies have been conducted to study the microbial profiles in inflammatory conditions. A common phenomenon in inflammatory bowel disease (IBD) is the reduction of the diversity of microbiota, which demonstrates that microbial diversity negatively correlates with disease severity in IBD. Increased microbial diversity is known to occur in disease remission. Species diversity plays an important role in maintaining the stability of the intestinal ecosystem as well as normal ecological function. A reduction in microbial diversity corresponds to a decrease in the stability of the ecosystem and can impair ecological function. Fecal microbiota transplantation (FMT), probiotics, and prebiotics, which aim to modulate the microbiota and restore its normal diversity, have been shown to be clinically efficacious. In this study, we hypothesized that a reduction in microbial diversity could play a role in the development of IBD

MiR-218 Inhibits Invasion and Metastasis of Gastric Cancer by Targeting the Robo1 Receptor

MicroRNAs play key roles in tumor metastasis. Here, we describe the regulation and function of miR-218 in gastric cancer (GC) metastasis. miR-218 expression is decreased along with the expression of one of its host genes, Slit3 in metastatic GC. However, Robo1, one of several Slit receptors, is negatively regulated by miR-218, thus establishing a negative feedback loop. Decreased miR-218 levels eliminate Robo1 repression, which activates the Slit-Robo1 pathway through the interaction between Robo1 and Slit2, thus triggering tumor metastasis. The restoration of miR-218 suppresses Robo1 expression and inhibits tumor cell invasion and metastasis in vitro and in vivo. Taken together, our results describe a Slit-miR-218-Robo1 regulatory circuit whose disruption may contribute to GC metastasis. Targeting miR-218 may provide a strategy for blocking tumor metastasis

Mathematical optimization models for long-term maintenance scheduling of wind power systems

During the life of a wind farm, various types of costs arise. A large share of the operational cost for a wind farm is due to maintenance of the wind turbine equipment; these costs are especially pronounced for offshore wind farms and they provide business opportunities in the wind energy industry. An effective scheduling of the maintenance activities may reduce the costs related to maintenance.\ua0 We combine mathematical modelling of preventive maintenance scheduling with corrective maintenance strategies. We further consider different types of contracts between the wind farm owner and a maintenance or insurance company, and during different phases of the turbines\u27 lives and the contract periods. Our combined preventive and corrective maintenance models are then applied to relevant combinations of the phases of the turbines\u27 lives and the contract types.\ua0 Our case studies show that even with the same initial criteria, the optimal maintenance schedules differ between different phases of time as well as between contract types. One case study reveals a 40% cost reduction and a significantly higher production availability---1.8% points---achieved byour optimization model as compared to a pure corrective maintenance strategy. Another study shows that the number of planned preventive maintenance occasions for a wind farm decreases with an increasing level of an insurance contract regarding reimbursement of costs for broken components

Optimal preventive maintenance schedule for a wind turbine with aging components

Wind power is one of the most important sources of renewable energy. A large part of the wind energy cost is due to the cost of maintaining the wind power equipment. To further reduce the maintenance cost, one can improve the design of the wind turbine components. One can also reduce the maintenance costs by optimal scheduling of the component replacements. The latter task is the main motivation for this paper.\ua0 When a wind turbine component fails to function, it might need to be replaced under less than ideal circumstances. This is known as corrective maintenance. To minimize the unnecessary costs, a more active maintenance policy based on the life expectancy of the key components is preferred. Optimal scheduling of preventive maintenance activities requires advanced mathematical modeling.\ua0 In this paper, an optimization model is developed using the renewal-reward theorem. In the multi-component setting, our approach involves a new idea of virtual maintenance which allows us to treat each replacement event as a renewal event even if some components are not replaced by new ones.\ua0 The proposed optimization algorithm is applied to a four-component model of a wind turbine and the optimal maintenance plans are computed for various initial conditions. The modeling results showed clearly the benefit of PM planning compared to pure CM strategy (about 8.5\% lower maintenance cost). When we compare it with another state-of-art optimization model, it shows similar scheduling with a much faster CPU time. The comparison demonstrated that our model is both fast and accurate

Optimal preventive maintenance scheduling for wind turbines under condition monitoring

We suggest a mathematical model for computing and regularly updating the next preventive maintenance plan for a wind farm. Our optimization criterium takes into account the current ages of the key components, the major maintenance costs including eventual energy production losses as well as the available data monitoring the condition of the wind turbines. We illustrate our approach with a case study based on data collected from several wind farms located in Sweden. Our results show that preventive maintenance planning gives some effect, if the wind turbine components in question live significantly shorter than the turbine itself

Exclusive Enteral Nutrition Induces Remission in Pediatric Crohn’s Disease via Modulation of the Gut Microbiota

Exclusive enteral nutrition (EEN) has been proven to be effective and safe in treating pediatric Crohn’s disease (CD). EEN induces pediatric CD remission possibly through three pathways: (1) direct anti-inflammatory effects, (2) improved epithelial barrier function, and (3) modulation of the gut microbiota. Recent studies have demonstrated that modulation of the gut microbiota plays a major role in EEN-induced remission. Variations of microbial components, which directly influence the diversity and metabolic functions of the gut microbiota, are closely associated with the immunological conditions of the gut and the susceptibility to diseases. The reduction of proinflammatory microbial components and harmful microbial metabolites after EEN treatment greatly decreases the inflammatory injuries of the gut

Molecular basis for the PAM expansion and fidelity enhancement of an evolved Cas9 nuclease.

Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems have been harnessed as powerful genome editing tools in diverse organisms. However, the off-target effects and the protospacer adjacent motif (PAM) compatibility restrict the therapeutic applications of these systems. Recently, a Streptococcus pyogenes Cas9 (SpCas9) variant, xCas9, was evolved to possess both broad PAM compatibility and high DNA fidelity. Through determination of multiple xCas9 structures, which are all in complex with single-guide RNA (sgRNA) and double-stranded DNA containing different PAM sequences (TGG, CGG, TGA, and TGC), we decipher the molecular mechanisms of the PAM expansion and fidelity enhancement of xCas9. xCas9 follows a unique two-mode PAM recognition mechanism. For non-NGG PAM recognition, xCas9 triggers a notable structural rearrangement in the DNA recognition domains and a rotation in the key PAM-interacting residue R1335; such mechanism has not been observed in the wild-type (WT) SpCas9. For NGG PAM recognition, xCas9 applies a strategy similar to WT SpCas9. Moreover, biochemical and cell-based genome editing experiments pinpointed the critical roles of the E1219V mutation for PAM expansion and the R324L, S409I, and M694I mutations for fidelity enhancement. The molecular-level characterizations of the xCas9 nuclease provide critical insights into the mechanisms of the PAM expansion and fidelity enhancement of xCas9 and could further facilitate the engineering of SpCas9 and other Cas9 orthologs