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The presence of principal ions in the water injected is essential for enhanced oil recovery by formation of water-wet state in carbonates. This study reaffirms this and presents an evaluation of the positive influence of both divalent as wells as monovalent ions on wettability alteration mechanisms during low salinity waterflooding using brines of varying ionic composition, referred to as “smart brines”. Zeta potentiometric analysis and reservoir simulation studies were conducted with diluted and smart brines that were prepared by varying the composition of principal ions. Surface charge of oil-saturated whole core samples of rock in the presence of various diluted and smart brines was estimated by zeta potential measurements. A comprehensive analysis of zeta potentiometric and reservoir simulation studies was done to establish and investigate the linkage between the recovery mechanism and the incremental recovery achieved. It is noted that zeta potential increases with the increasing level of dilution and it can be attributed to electric double-layer mechanism. On the contrary, simulation studies implied a different mechanism where an increase in effluent’s pH and Ca2+ mole fraction along with decrease in moles of minerals and saturation index implied rock dissolution was dominant mechanism. Moreover, the effect of mineral dissolution beyond the injection block is highly doubtful. This study demonstrates that an integrated approach from both zeta potentiometric and simulation studies can be used to provide insights into the underlying science of interactions at pore scale during a low salinity waterflood using smart brines. With the aid of an adequately designed upscaling procedure and protocol, the laboratory results can be further used towards developing field-scale models to obtain with realistic recovery factors with optimized brine composition and salinity.

Abstract Third zone of a distance relay is sensitive to system stressed conditions, such as stable and unstable power swing, voltage instability and load encroachment. Mal-operation of a distance relay under these conditions can cause power system blackout and should be prevented for power system security. An integrated approach based on two indices is introduced to improve the relay performance during system stressed conditions. The first index is based on estimated dc offset magnitude from the current signal using half cycle Discrete Fourier Transform and the second index is based on energy of first level detail coefficients obtained using discrete wavelet transform. It can be hard for a distance relay to discriminate between a three-phase fault in the third zone and stressed system events as both are balanced phenomena. Therefore, during the occurrence of a transient event if the conventional impedance calculation method detects it as a fault, it will be rightly detected as a three-phase fault only if both the indices lie above their respective thresholds. Using these two indices as a combined supervisory logic, three-phase fault in the third zone can be rapidly and reliably detected and discriminated from system stressed conditions, and thus avoid false operation of distance relay during stressed system conditions. Current based and wavelet techniques are, respectively, sensitive to noise and sampling frequency. However, with the proposition of adaptive threshold setting in the wavelet approach the robustness of this method against noise and sampling frequency is guaranteed. The proposed method is reliable during CT saturation condition as well. The proposed method is based on local end information so system condition can be rapidly scrutinized. Results for different power system events are analyzed using New England 39-bus, 10-machine test system modelled in EMTDC/PSCAD software. Comparative assessment with conventional approach confirms that this algorithm can meet the accuracy standards of the industrial relays due to better reliability.

Cellulose nanofibers (CNFs) and their applications have recently gained significant attention due to the attractive and unique combination of their properties including excellent mechanical properties, surface chemistry, biocompatibility, and most importantly, their abundance from sustainable and renewable resources. Although there are some commercial production plants, mostly in developed countries, the optimum CNF production is still restricted due to the expensive initial investment, high mechanical energy demand, and high relevant production cost. This paper discusses the development of the current trend and most applied methods to introduce energy-efficient approaches for the preparation of CNFs. The production of cost-effective CNFs represents a critical step for introducing bio-based materials to industrial markets and provides a platform for the development of novel high value applications. The key factor remains within the process and feedstock optimization of the production conditions to achieve high yields and quality with consistent production aimed at cost effective CNFs from different feedstock.

AbstractAn active power filter (APF) is proposed which eliminates harmonics, compensates reactive power and neutral current in a three‐phase four‐wire electric system supplying non‐linear loads. An insulated‐gate‐bipolar‐transistor (IGBT)‐basedpulse‐width modulation (PWM) voltage source inverter (VSI) with a DC bus capacitor is employed as an APF. A hysteresis‐rule‐based carrierless PWM current control technique is used to generate switching signals to the devices of the APF. Three single‐phase diode rectifiers supplying resistive‐capacitive loads are considered as a three‐phase non‐linear load. A detailed dynamic model of the scheme is developed to study transient and steady‐state behaviour of the APF. The proposed APF is found capable to compensate reactive power, neutral currents and to reduce the harmonics below specific recommendations of IEEE‐519 standard.

Artificial neural networks can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. Taking advantage of the characteristics of a generalized neuron (GN), that requires much smaller training data and shorter training time, a GN-based adaptive power system stabilizer (GNAPSS) is proposed. It consists of a GN as an identifier, which predicts the plant dynamics one step ahead, and a GN as a controller to damp low frequency oscillations. Results of studies with a GN-based PSS on a five-machine power system show that it can provide good damping of both local and inter-area modes of oscillations over a wide operating range and significantly improve the dynamic performance of the system.