Solid-liquid extraction is carried out from vegetable substrates in industrial continuous counter-current extractor. A batch study was conducted to obtain the extraction kinetic and the bulk phase saturation. An extraction model is proposed and compared with experimental data. This model is based on the general diffusion model including a shape factor of solid particle. A methodology for the ba...

Rapid response to remove (or reduce) the detrimental effects of accidents has always been an important safety issue for the chemical industries. A systematic strategy is presented in this paper to synthesize emergency response procedures in any given batch system. Specifically, two distinct sets of automata are first constructed offline to model the plant behaviors and the control specification...

A simple experiment such as the batch distillation of an ethanol–water binary mixture can be performed in a 3-h laboratory session using a very rudimentary apparatus consisting of a still pot and two thermocouples. Yet, the results can lead to very interesting insights to distillation and chemical engineering thermodynamics. The present article describes how one can exploit this simple laborato...

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that realtime query processing and in-stream processing is the immediate need in many practical applications. In recent years, this idea got a lot of traction and a whole bunch of solutions like Twitter’s Storm, Yahoo’s S4, Cloudera’s Impala, A...

We propose Batch-Expansion Training (BET), a framework for running a batch optimizer on a gradually expanding dataset. As opposed to stochastic approaches, batches do not need to be resampled i.i.d. at every iteration, thus making BET more resource efficient in a distributed setting, and when disk-access is constrained. Moreover, BET can be easily paired with most batch optimizers, does not req...

Pattern reordering is proposed as an alternative to sequential and batch processing for online category learning. Upon detecting that the categorization of a new input pattern is ambiguous, the input is postponed for a predefined time, after which it is reexamined and categorized for good. This approach is shown to improve the categorization performance over purely sequential processing, while ...