Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature. However, the usage of GPs in real-world application is limited due to their high computational cost at inference time. In this paper, we introduce a new framework, kernel distillation, for kernel matrix approximation. The idea adopts from knowledge distillat...

Compositions measurement is a vitally critical issue for the modelling and control of distillation process. The product compositions of distillation columns are traditionally measured using indirect techniques via inferring tray compositions from its temperature or by using an online analyser. These techniques were reported as inefficient and relatively slow methods. In this paper, an alternati...

The paper considers the effect of feed plate location on the interactions in a seven plate binary distillation column. The mathematical model of the distillation column is deduced based on the equations of mass and energy balances for each stage, detailed model for both reboiler and condenser, and heat transfer equations. The Dynamic Relative Magnitude Criterion, DRMC is used to assess the inte...

The structure of the thermally coupled distillation systems offers some control challenges arising from the transfer of vapor (or liquid) streams between the columns. In particular, the presence of recycle streams for coupled schemes has influenced the notion that control problems might be expected during the operation of those systems with respect to the rather well-known behavior of conventio...

This study aimed to develop a new processing method for the effective use of rice shochu distillation remnants. We examined the inhibitory effects on the growth of human lung carcinoma cells in the medium of rice shochu distillation remnants with various fungi. Interestingly, high inhibitory effects on the growth of human lung carcinoma cells in the medium of rice shochu distillation remnants w...

We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have developed a “distillation” procedure to recursively reduce the size of an initial training set using a criterion that ensures the maximization of ...

In this paper, we propose a novel algorithm for the rigorous design of distillation columns that integrates a process simulator in a generalized disjunctive programming formulation. The optimal distillation column, or column sequence, is obtained by selecting, for each column section, among a set of column sections with different number of theoretical trays. The selection of thermodynamic model...

Distillation is not only the most energy consumer but also the most frequently-adopted separation operation in chemical industries since it separates a mixture only by the difference among vapor pressures (boiling points) of components. So distillation is often called as the king of separation processes. Therefore, research and development of technologies on energy savings of distillation is an...

We show the equivalence of two approaches to the design of entanglement distillation protocols. The first approach is based on local unitary operations that yield permutations of tensor products of Bell states. The second approach is based on stabilizer codes.

We describe a protocol for distilling maximally entangled bipartite states between random pairs of parties from those sharing a tripartite W state |W=(1/sqrt[3])(|100+|010+|001)(ABC), and show that the total distillation rate E(t)(infinity) [the total number of Einstein-Podolsky-Rosen (EPR) pairs distilled per W, irrespective of who shares them] may be done at a higher rate than EPR distillatio...