Nanotechnology and global energy demand: challenges and prospects for a paradigm shift in the oil and gas industry.

The exploitation of new hydrocarbon discoveries in meeting the present global energy demand is a function of the availability and application of new technologies. The relevance of new technologies is borne out of the complex subsurface architecture and conditions of offshore petroleum plays. Conventional techniques, from drilling to production, for exploiting these discoveries may require adaption for such subsurface conditions as they fail under conditions of high pressure and high temperature. The oil and gas industry over the past decades has witnessed increased research into the use of nanotechnology with great promise for drilling operations, enhanced oil recovery, reservoir characterization, production, etc. The prospect for a paradigm shift towards the application of nanotechnology in the oil and gas industry is constrained by evolving challenges with its progression. This paper gave a review of developments from nano-research in the oil and gas industry, challenges and recommendations

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

The effect of contact angles and capillary dimensions on the burst frequency of super hydrophilic and hydrophilic centrifugal microfluidic platforms, a CFD study.

This paper employs the volume of fluid (VOF) method to numerically investigate the effect of the width, height, and contact angles on burst frequencies of super hydrophilic and hydrophilic capillary valves in centrifugal microfluidic systems. Existing experimental results in the literature have been used to validate the implementation of the numerical method. The performance of capillary valves in the rectangular and the circular microfluidic structures on super hydrophilic centrifugal microfluidic platforms is studied. The numerical results are also compared with the existing theoretical models and the differences are discussed. Our experimental and computed results show a minimum burst frequency occurring at square capillaries and this result is useful for designing and developing more sophisticated networks of capillary valves. It also predicts that in super hydrophilic microfluidics, the fluid leaks consistently from the capillary valve at low pressures which can disrupt the biomedical procedures in centrifugal microfluidic platforms

A conceptual comparison of service blueprinting and business process modeling notation (BPMN)

The modeling of business processes has become a central aspect in how businesses understand, support, and communicate about their processes. Two prominent approaches are service blueprinting and business process modeling notation (BPMN). Service blueprinting supports customer service processes whilst BPMN helps understand a firm's processes with particular focus on how information and communications technology supports processes, and also for process automation. To fully support services through an organization's processes, there needs to be a complete understanding of how these two process representations relate. Hitherto only a partial comparison has been undertaken by Milton and Johnson in 2012. Therefore we ask the question, what are the specific similarities and differences between these two approaches? To answer this question, we employed the method of conceptual evaluation to conduct a two-way conceptual comparison of service blueprinting and BPMN. We found specific similarities and differences between the two modeling approaches. Understanding how to represent service blueprint concepts in BPMN is important for supporting service-processes with information technology and for automating aspects of those processes. Furthermore, knowing the limitations of how service blueprints support BPMN means that mapping internal processes to service processes can be done with minimal loss in semantics

To optimize gas flaring in Kirkuk refinery in various seasons via artificial intelligence techniques

Abstract Unavoidable flaring in downstream oil industry causes pollutant emission in large amounts which is potentially harmful to nearby cities or farms. Hence one must manage exhaust toxic gases to raise enough in atmosphere or redirect from such places. Since Kirkuk refinery in north Iraq is next-door to agricultural farms on west yet to residential areas on east optimizing its layout for flare stacks is something acute. In this work we wrote codes in MATLAB software to simulate incomplete rather than complete oxidation as well as pollutant generation reactions. Then we made use of FLEUENT software to simulate pollutant propagation in Kirkuk oil purifier complex yet also farther to city as well as farms with respect to seasonal air currents on lowest troposphere layer. Finally, we set neural network approach to train on simulation data thereafter to unify outcomes to turn into a fast technique for layout optimization. Results show that optimization process efficiency relies on air current velocities as well as its direction. At intermediate air flow rates optimum layout includes only a selective portion of existent flare stacks. Outcomes also illustrate that heuristic techniques that have stronger local search such as particle swarm or artificial immune system can improve flare layout in seasons with intermediate air currents here summer plus early months in autumn while approaches with weak local search like Monte Carlo are more appropriate in winter for which we have no or low air flows in Kirkuk governorate

Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing

Functional emulation of biological synapses using electronic devices is regarded as the first step toward neuromorphic engineering and artificial neural networks (ANNs). Electrolyte-gated transistors (EGTs) are mixed ionic-electronic conductivity devices capable of efficient gate-channel capacitance coupling, biocompatibility, and flexible architectures. Electrolyte gating offers significant advantages for the realization of neuromorphic devices/architectures, including ultralow-voltage operation and the ability to form parallel-interconnected networks with minimal hardwired connectivity. In this review, the most recent developments in EGT-based electronics are introduced with their synaptic behaviors and detailed mechanisms, including short-/long-term plasticity, global regulation phenomena, lateral coupling between device terminals, and spatiotemporal correlated functions. Analog memory phenomena allow for the implementation of perceptron-based ANNs. Due to their mixed-conductivity phenomena, neuromorphic circuits based on EGTs allow for facile interfacing with biological environments. We also discuss the future challenges in implementing low power, high speed, and reliable neuromorphic computing for large-scale ANNs with these neuromorphic devices. The advancement of neuromorphic devices that rely on EGTs highlights the importance of this field for neuromorphic computing and for novel healthcare technologies in the form of adaptable or trainable biointerfacing