Zero-direct-carbon-emission aluminum production by solid oxide membrane-based electrolysis process

The traditional aluminum production process (Hall-Héroult process) involves electrolyzing the alumina dissolved in the molten cryolite salt. This process is energy intensive and emits massive amounts of CO2 and other greenhouse gases. The market demand of aluminum and the environmental impact of the current aluminum production process justify research and development of alternative electrolytic processes for aluminum production that can both reduce the cost and eliminate adverse environment impacts. Solid oxide membrane (SOM) based electrolysis process is an innovative technology that has been demonstrated to successfully produce many energy-intensive metals directly from their oxides in an efficient, economical and environmentally sound way. During the SOM electrolysis process, an oxygen-ion-conducting SOM tube made of ytteria-stabilized zirconia (YSZ) separates the pre-selected molten flux with dissolved metal oxide from the inert anode assembly inside the YSZ tube. When the applied DC potential between the cathode and the anode exceeds the dissociation potential of desired metal oxide, the metal is reduced at the cathode while oxygen ions migrate through the YSZ membrane and are oxidized at the anode. Employing the inert anode allows the oxygen to be collected at the anode as a value added byproduct. In this work, a zero-direct-carbon-emission aluminum production process utilizing SOM electrolysis is presented. The molten flux used in the electrolysis process is optimized through careful measurements of its physio-chemical properties. The liquidus temperature, volatilization rate, alumina solubility, aluminum solubility, YSZ membrane degradation rate and electrical conductivity of various flux compositions were measured, and the flux chosen for SOM electrolysis was a eutectic MgF2-CaF2 system containing optimized amounts of YF3, CaO and Al2O3. Laboratory scale SOM electrolysis employing the inert anode were performed at 1100 ~ 1200oC to demonstrate the feasibility of producing and collecting aluminum while producing pure oxygen as a byproduct. The aluminum product was characterized by scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDS). An equivalent circuit model for the electrolysis process was developed in order to identify the polarization losses in the SOM electrolysis cell.2016-12-21T00:00:00

On scheduling input queued cell switches

Output-queued switching, though is able to offer high throughput, guaranteed delay and fairness, lacks scalability owing to the speed up problem. Input-queued switching, on the other hand, is scalable, and is thus becoming an attractive alternative. This dissertation presents three approaches toward resolving the major problem encountered in input-queued switching that has prohibited the provision of quality of service guarantees. First, we proposed a maximum size matching based algorithm, referred to as min-max fair input queueing (MFIQ), which minimizes the additional delay caused by back pressure, and at the same time provides fair service among competing sessions. Like any maximum size matching algorithm, MFIQ performs well for uniform traffic, in which the destinations of the incoming cells are uniformly distributed over all the outputs, but is not stable for non-uniform traffic. Subse-quently, we proposed two maximum weight matching based algorithms, longest normalized queue first (LNQF) and earliest due date first matching (EDDFM), which are stable for both uniform and non-uniform traffic. LNQF provides fairer service than longest queue first (LQF) and better traffic shaping than oldest cell first (OCF), and EDDEM has lower probability of delay overdue than LQF, LNQF, and OCF. Our third approach, referred to as store-sort-and-forward (SSF), is a frame based scheduling algorithm. SSF is proved to be able to achieve strict sense 100% throughput, and provide bounded delay and delay jitter for input-queued switches if the traffic conforms to the (r, T) model

On the Depth of Deep Neural Networks: A Theoretical View

People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound. In margin bound, expected error is upper bounded by empirical margin error plus Rademacher Average (RA) based capacity term. First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth. This indicates negative impact of depth on test performance. Second, we show that deeper networks tend to have larger representation power (measured by Betti numbers based complexity) than shallower networks in multi-class setting, and thus can lead to smaller empirical margin error. This implies positive impact of depth. The combination of these two results shows that for DNN with restricted number of hidden units, increasing depth is not always good since there is a tradeoff between positive and negative impacts. These results inspire us to seek alternative ways to achieve positive impact of depth, e.g., imposing margin-based penalty terms to cross entropy loss so as to reduce empirical margin error without increasing depth. Our experiments show that in this way, we achieve significantly better test performance.Comment: AAAI 201