Preparation and properties of novel polyurethane insulating coatings based on glycerin‐terminated urethane prepolymers and blocked isocyanate

Novel polyurethane insulating coatings were prepared from the reaction of glycerin‐terminated polyurethane prepolymers (GPUPs) and a blocked isocyanate curing agent (BIC). The GPUPs were prepared from the reaction of one equivalent of polycaprolactone polyol (CAPA 210) with an excess amount of 4,4′‐methylene bis(phenyl isocyanate) (MDI) and subsequent reaction of the NCO‐terminated polyurethane with glycerin. The BIC was prepared from the reaction of trimethylol propane (TMP), toluene diisocyanate (TDI) and N‐methylaniline (NMA). The polyols and curing agent were characterized by conventional methods while the curing condition was optimized via gel content measurements. The curing kinetics of the polyurethane coating were investigated and the kinetic parameters derived. The crosslink densities of the samples were determined via the equilibrium swelling method, using the Flory–Rehner equation. The relationships between the crosslink density and the electrical, physical, mechanical and dynamic mechanical properties of the coatings were also studied. Copyright © 2005 Society of Chemical Industry     

逆向反射镜光度测量

为了正确评价逆向反射镜的加工质量与使用效果，且将二者有机地统一起来，本文提出应根据测试目的来选择合适的光度测量方法，并进一步分析了不同测试目的将导致测量装置的组成、几何条件（距离，孔径角，工作姿态等）、光度条件（光源色温、稳定性与均匀性，接收器线性、灵敏度与光谱修正等）、测量参数（反光强度系数、逆反射系数、逆亮度系数等）等各不相同。     

Survival of rotavirus SA-11 on vegetables

Survival of rotavirus on lettuce, radishes, and carrots was studied to evaluate the potential of rotavirus transmission by vegetables irrigated with wastewater. The vegetables were contaminated with rotavirus SA-11 and stored at 4°C and room temperature in covered and uncovered containers to simulate post harvest conditions. Virus decay rates were greater on radishes and carrots than lettuce. Decay rates of rotavirus on lettuce, radish, and carrot ranged from ?0·057 to ?0·479 (log₁₀ pfu/day). Rotavirus SA-11 survived on lettuce, radish, and carrot for 25 to 30 days at 4°C but at room temperature survival was very different for the various vegetables varying from 5 to 25 days. Greatest survival was always observed on the lettuce. These data suggest that rotaviruses can survive long enough on contaminated vegetables as to be transmitted by this vehicle.     

Heat-integration retrofit study of a petroleum refinery

The operation of a petroleum refinery at TÜPRA . Tütünçiftlik was assessed using the pinch-design method. By making use of heat integration in the heat-exchange network, appreciable amounts of energy can be saved as a result of a capital investment having a pay-back period of about 6·5 months.     

A novel interference suppression scheme for global navigation satellite systems using antenna array

This paper considers interference suppression and multipath mitigation in Global Navigation Satellite Systems (GNSSs). In particular, a self-coherence anti-jamming scheme is introduced which relies on the unique structure of the coarse/acquisition (C/A) code of the satellite signals. Because of the repetition of the C/A-code within each navigation symbol, the satellite signals exhibit strong self-coherence between chip-rate samples separated by integer multiples of the spreading gain. The proposed scheme utilizes this inherent self-coherence property to excise interferers that have different temporal structures from that of the satellite signals. Using a multiantenna navigation receiver, the proposed approach obtains the optimal set of beamforming coefficients by maximizing the cross correlation between the output signal and a reference signal, which is generated from the received data. It is demonstrated that the proposed scheme can provide high gains toward all satellites in the field of view, while suppressing strong interferers. By imposing constraints on the beamformer, the proposed method is also capable of mitigating multipath that enters the receiver from or near the horizon. No knowledge of either the transmitted navigation symbols or the satellite positions is required.     

KINEMATIC VISCOSITY-TEMPERATURE BEHAVIOR OF HEAVY TBP-FRACTIONS (455°C+) OF ARABIAN CRUDE OILS

A generalized kinematic viscosity-temperature correlation for undefined liquid heavy petroleum fractions has been developed to represent the data for a wide range of temperature from 100°C to 200°C. The correlation is based on the experimental kinematic viscosity data of true boiling point fractions of four Arabian crude oils. The characterization property required for estimation is 50% boiling point. The proposed correlation fits the experimental data with an overall absolute error of 6.1%. Experimental measurements of kinematic viscosity of heavy true boiling point fractions of Arabian crude oils were also obtained in order to develop the proposed correlation.     

An extended minimax disparity to determine the OWA operator weights

This paper contributes to extend the minimax disparity to determine the ordered weighted averaging (OWA) model based on linear programming. It introduces the minimax disparity approach between any distinct pairs of the weights and uses the duality of linear programming to prove the feasibility of the extended OWA operator weights model. The paper finishes with an open problem.     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.