Model for microbiologically influenced corrosion potential assessment for the oil and gas industry

Corrosion is one of the major causes of failure in onshore and offshore oil and gas operations. Microbiologically influenced corrosion (MIC) is inherently more complex to predict, detect and measure because, for instance, the presence of biofilm and/or bacterial products is not sufficient to indicate active microbiological corrosion. The major challenge for current MIC models is to correlate factors that influence corrosion (i.e. chemical, physical, biological and molecular variables) with the potential of having MIC. Previous work has proposed the potential for MIC as a simple product of multiple factors, without fully considering the synergy or the interference among the factors. The present work proposes a network-based approach to analyse and predict MIC potential considering the complex interactions among a total of 60 influencing factors and 20 screening parameters. The proposed model has the ability to capture the complex interdependences and the synergic interactions of the factors used to assess MIC potential and uses an object-oriented approach based on a Bayesian Network. The model has been tested and verified using real data from a pipeline leakage incident that was a result of MIC. The proposed model constitutes a significant step in deepening the understanding of when MIC occurs and its predictability.

List of acronyms: APB: acid producing bacteria; Aw: water activity; BN: Bayesian network; MIC: microbiologically influenced corrosion; MMMs: molecular microbiological methods; NRB: nitrate-reducing bacteria; OOBN: object-oriented Bayesian network; PWRI: produced water re-injection; SPs: screening parameters; SRB: sulphate-reducing bacteria; SRPs: sulphate-reducing prokaryotes; TDSs: total dissolved solids     

Augmented semantic segmentation for the digitization of grinding tools based on deep learning

In order to analyze various process characteristics, grinding simulations can be used, which need accurate models of the tool and the individual grains. For this purpose, grinding tools can be digitized. To identify characteristic grains from a large number of measurements, each individual grain has to be analyzed and separated from the bond manually. Therefore, a deep learning-based methodology was developed to achieve a high segmentation accuracy of the grain boundaries efficiently. Additionally, a data augmentation approach was investigated to limit the data necessary for learning. The model transferability was quantified by analyzing different states of tool wear.     

In situ analysis of the strain evolution during welding using low transformation temperature filler materials

ABSTRACT

Compared to conventional welding consumables using low transformation temperature (LTT) filler materials is an innovative method to mitigate tensile residual stresses due to delayed martensite transformation of the weld. For the effective usage of LTT filler materials, a deeper understanding of the complex processes that lead to the final residual stress state during multi-pass welding is necessary. Transformation kinetics and the strain evolution of multi-pass welds during welding were investigated in situ at the beamline HEMS@PETRAIII, Germany. Compared to conventional welds, the total strain was reduced and compression strain was achieved when using LTT filler materials. For an optimal use of the LTT effect in the root of multi-pass welds, the alloying concept must be adapted taking care of dilution.     

Dietary differences among commercially important fishes in Lake Tanganyika assessed using stable isotope analysis

The pelagic fishery of Lake Tanganyika, which is largely made up of the three species Lates stappersii, Limnothrissa miodon, and Stolothrissa tanganicae, has been in decline, and there is no clear understanding of the primary underlying causes. It has been suggested that climate change has altered the primary productivity of the system, but detailed knowledge of the system's food web is required to elucidate the effect on higher trophic levels. The aim of this study is to determine the diet of the three commercially important fish species. Muscle tissue samples for stable isotope analysis were taken from February through April 2017, supplemented with stomach samples from L. stappersii for use in stomach content analysis. The stomach analysis showed an ontogenetic change in the diet composition of L. stappersii, shifting from copepods to fish larvae, supplemented with shrimp, to whole fish prey as the fish grew larger. Stable carbon and nitrogen isotope values from fish muscle were consistent with this observation, and also seemed to indicate that fish of similar sizes had similar diets, irrespective of species. This suggests that the diet of the pelagic fish species might be better explained by size, rather than species. The isotope data revealed a short range of δ¹⁵N values from primary consumers to fish, which may help explain the high fisheries productivity of Lake Tanganyika, and the strong impact of primary productivity changes on fisheries yield.     

Mass balance model of Lake Volta fisheries: The use of Ecopath model

A mass balance model of trophic interactions among ten key functional producer and consumer groups in Lake Volta was constructed using the Ecopath model to study the energy flows and species interactions in the lake. The present study was based on secondary and primary data on fish catch, diet composition, phytoplankton and zooplankton biomasses, collected in 2015 and 2016. Additional information on growth parameters of major species required for balancing the Ecopath model was obtained from sampling and FishBase. The functional groups were detritus, phytoplankton, zooplankton, benthos, prey fish, Tilapia, Bagrus, Chrysichthys, Alestes and Synodontis species. Four trophic levels were identified in the Lake Volta ecosystem, with the energy flow occurring mainly within the first three trophic levels. The calculated ecotrophic efficiency value of the primary producers (phytoplankton: 0.17; detritus: 0.22) indicated they were least exploited, compared to the secondary producers, zooplankton (0.80) and benthos (0.50). All secondary consumers had ecotrophic efficiency values higher than the primary producers, indicating they are exploited in the ecosystem. The main energy flows in the lake were from phytoplankton and detritus at trophic level I, and Bagrus species, the top predator, at a level of 3.30. The network analysis, illustrating a connectance index of 0.43 and an omnivory index of 0.06, in the lake system indicated the ecosystem is unstable, somewhat immature and still in a developing stage.     

Simple tool to evaluate the impact of daylight on building energy consumption

This paper presents a simple building simulation tool for integrated daylight and thermal analysis. The tool is capable of importing the thermal and visual properties for different glazings and shading positions from the Window Information System (WIS) program. A coupled ray-tracing and radiosity methodology is used to derive the daylight levels for different sky conditions. Both detailed daylight distribution for a particular day and time and hourly discrete values on a yearly basis may be obtained. For an integrated simulation the hourly daylight levels are fed into an existing simple thermal simulation program capable of calculating energy demand and the indoor environment. Straightforward control systems for general and task lighting systems have been implemented together with a shading control strategy that adjusts the shading according to the indoor operative temperature, the risk of glare and the profile angle of the sun. The implemented daylight calculation method allows for shades from the window recess and overhang, and for distant shades blocking the sky vault. Comparisons with the ray-tracing program Radiance show that the accuracy of this approach is adequate for predicting the energy implications of photoresponsive lighting control. The amount of input is small, which makes the tool useful for integrated daylight optimisation in the early design process.     

Replacing the ({beta}{alpha})-unit 8 of E.coli TIM with its chicken homologue leads to a stable and active hybrid enzyme

In order to investigate how structural modifications interferewith protein stability, we modified a (ß)-unit inE.coli triosephosphate isomerase (TIM), a typical (ß)-barrelprotein, assuming that the pseudosymmetrical ß-barrelcan be divided into eight successive loop/ß-strand/loop/-helixmotifs. We replaced the eighth (ß)-unit of E.coliTIM with the corresponding chicken (ß)-unit. The substitution,involving the replacement of 10 of the 23 residues of this (ß)-unit, was evaluated first by modelling, then experimentally.Modelling by bomology suggests how the amino add replacementsmight be accommodated in the hybrid E.coli/chicken TIM (ETCM8CHI).Both natural and hybrid recombinant TIMs, overproduced in E.coli,were purified to homogeneity and characterized as to their stabilityand kinetics. Our kinetic studies show that the modificationperformed here leads to an active enzyme. The stability studiesindicate that the stability of ETIM8CHI is comparable to thatof the wild type TIM.