The offshore oil and gas industry is experiencing increasing water cuts as the reservoirs mature. The increase in produced water stresses the currently deployed deoiling technologies, resulting in more oil in the discharged water. Deploying membrane filtration to reduce the hydrocarbon concentration inherits additional complications related to fouling of the membranes: A process where the accumulation of material within and on the membrane surface adds additional flow resistance. This paper reviews and analyses the fouling detection, removal, prevention, dynamical and static modeling, with emphasis on how the membrane process can be manipulated from a process control perspective. The majority of the models rely on static descriptions or are limited to a narrow range of operating conditions which limits the usability of the models. This paper concludes that although the membrane filtration has been successfully applied and matured in many other industrial areas, challenges regarding cost-effective mitigation of fouling in the offshore deoiling applications, still exist. Fouling-based modeling combined with online parameter identification could potentially expand the operating range of the models and facilitate advanced control design to address transient performance and scheduling of fouling removal methods, resulting in cost-effective operation of membrane filtration systems. With the benefits of membrane filtration, it is predicted that membrane technology will be incorporated in produced water treatment, if the zero-discharge policies are enforced globally.

Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of people's opinions. Tweets data can be efficiently used to infer people's opinions for marketing or social studies. This paper proposes a Tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general people's opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific political candidates, i.e., two primary minister candidates - Julia Gillard and Tony Abbot. Our experimental results demonstrate the effectiveness of the system.

Due to the increasing energy demands, the offshore energy business has boomed in recent decades. Sub-sea pipeline and power transmission cable installations are commonly applied worldwide. Any potential breakages can cause equipment damage and also damage the environment. The majority of the offshore pipeline inspections are currently committed using Towed or Remotely Operated Vehicle (ROV) systems. It is well-known that the ROVs are very time-consuming and expensive to operate, with respect to the fact that they require a relatively large support ship to accommodate the equipment as well as very skilled pilot and crews.

This article reports the development of an underwater imaging system and its trial on a moored surface buoy for in situ plankton monitoring of coastal waters. The imager features shadowless white light illumination by an orthogonal lamellar lighting design, resulting in high-quality underwater darkfield color imaging of planktonic particles in the size range of ∼200 μ m to 40 mm and effective reduction of zooplankton phototaxis. Through raft and buoy trials, 46 804 plankton and suspending particle images have been annotated through a human–machine mutually assisted effort into a data set with 90 categories. In the meanwhile, a deep learning model based on a triclassification VGGNet-11 and multiclassification ResNet-18 convolutional neuron networks in a two-staged hierarchy has also been trained and developed. The model has been applied with human supervision to semiautomatically analyze a total of 1 545 187 images obtained from a buoy trial for six months from late spring to early winter of 2020. The high temporal resolution results well documented the variation of the mesoplankton community structure in two time series of 38 days in summer and 54 days in autumn of the target sea region. In addition, the dominant species in the trial period and a zooplankton outbreak that had threatened the safety of the nearby nuclear power plants were quantitatively analyzed. The system is expected to become a new entry into the toolkit for marine surface buoy platforms to upscale their capabilities for more comprehensive in situ plankton monitoring.