Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets

The concept of cryogenic energy storage (CES) is to store energy in the form of liquid gas and vaporize it when needed to drive a turbine. Although CES on an industrial scale is a relatively new approach, the technology is well known and essentially part of any air separation unit that utilizes cryogenic separation. In this work, the operational benefits of adding CES to an existing air separation plant are assessed. Three new potential opportunities are investigated: (1) increasing the plant's flexibility for load shifting, (2) storing purchased energy and selling it back to the market during higher‐price periods, and (3) creating additional revenue by providing operating reserve capacity. A mixed‐integer linear programming scheduling model is developed and a robust optimization approach is applied to model the uncertainty in reserve demand. The proposed model is applied to an industrial case study, which shows significant potential economic benefits. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1547–1558, 2015     

Investigating the Effect of WC on the Hardness and Wear Behaviour of Surface Modified AA 6063

The objective of this work is to reinforce Tungsten Carbide (WC) onto the surface of AA 6063 aluminium alloy using Gas Tungsten Arc (GTA) as heat source and investigate the hardness and wear properties. Based on number of trials, optimum GTA heat source parameters are finalized with reference to the proper fusion of base metal. It is found that the hardness is reduced after the application of heat. After the reinforcement of WC, the hardness returns back to the base metal value. In order to further to improve the properties, the reinforced alloy is heat Treated. The Microhardness of the reinforced and aged AA 6063 is increased by 50% with respect to base metal. The wear resistance of the reinforced surface is improved by 52% with respect to base metal. Characterization techniques like SEM, EDX and XRD are done and the presence of WC is confirmed.     

Automatic segmentation of optic disc in retinal fundus images using semi-supervised deep learning

Diseases of the eye require manual segmentation and examination of the optic disc by ophthalmologists. Though, image segmentation using deep learning techniques is achieving remarkable results, it leverages on large-scale labeled datasets. But, in the field of medical imaging, it is challenging to acquire large labeled datasets. Hence, this article proposes a novel deep learning model to automatically segment the optic disc in retinal fundus images by using the concepts of semi-supervised learning and transfer learning. Initially, a convolutional autoencoder (CAE) is trained to automatically learn features from a large number of unlabeled fundus images available from the Kaggle’s diabetic retinopathy (DR) dataset. The autoencoder (AE) learns the features from the unlabeled images by reconstructing the input images and becomes a pre-trained network (model). After this, the pre-trained autoencoder network is converted into a segmentation network. Later, using transfer learning, the segmentation network is trained with retinal fundus images along with their corresponding optic disc ground truth images from the DRISHTI GS1 and RIM-ONE datasets. The trained segmentation network is then tested on retinal fundus images from the test set of DRISHTI GS1 and RIM-ONE datasets. The experimental results show that the proposed method performs on par with the state-of-the-art methods achieving a 0.967 and 0.902 dice score coefficient on the test set of the DRISHTI GS1 and RIM-ONE datasets respectively. The proposed method also shows that transfer learning and semi-supervised learning overcomes the barrier imposed by the large labeled dataset. The proposed segmentation model can be used in automatic retinal image processing systems for diagnosing diseases of the eye.

     

A Study of Added SiC Powder in Kerosene for the Blind Square Hole Machining of CFRP Using Electrical Discharge Machining

Silicon - Carbon Fiber Reinforced Polymers (CFRPs) have been applied potentially for various application components owing to their lightweight and better mechanical properties. However, the...