Computational fluid dynamic simulations of coal-fired utility boilers: An engineering tool

The objective of this study was to develop an engineering tool by which the combustion behavior of coals in coal-fired utility boilers can be predicted. We presented in this paper that computational fluid dynamic (CFD) codes can successfully predict performance of- and emission from- full-scale pulverized-coal utility boilers of various types, provided that the model parameters required for the simulation are properly chosen and validated. For that purpose we developed a methodology combining measurements in a 50 kW pilot-scale test facility with CFD simulations using the same CFD code configured for both test and full-scale furnaces. In this method model parameters of the coal processes are extracted and validated. This paper presents the importance of the validation of the model parameters which are used in CFD codes. Our results show very good fit of CFD simulations with various parameters measured in a test furnace and several types of utility boilers. The results of this study demonstrate the viability of the present methodology as an effective tool for optimization coal burning in full-scale utility boilers.     

Selection of a representative value function in robust multiple criteria sorting

We introduce the concept of a representative value function in robust ordinal regression applied to multiple criteria sorting problems. The proposed approach can be seen as an extension of UTADIS^GMS, a new multiple criteria sorting method that aims at assigning actions to p pre-defined and ordered classes. The preference information supplied by the decision maker (DM) is composed of desired assignments of some reference actions to one or several contiguous classes—they are called assignment examples. The robust ordinal regression builds a set of general additive value functions compatible with the assignment examples and results in two assignments: necessary and possible. The necessary assignment specifies the range of classes to which the action can be assigned considering all compatible value functions simultaneously. The possible assignment specifies, in turn, the range of classes to which the action can be assigned considering any compatible value function individually. In this paper, we propose a way of selecting a representative value function among the set of compatible ones. We identify a few targets which build on results of the robust ordinal regression and could be attained by a representative value function. They concern enhancement of differences between possible assignments of two actions. In this way, the selected function highlights the most stable part of the robust sorting, and can be perceived as representative in the sense of robustness preoccupation. We envisage two possible uses of the representative value function in decision support systems. The first one is an explicit exhibition of the function along with the results of the UTADIS^GMS method, in order to help the DM to understand the robust sorting. The other is an autonomous use, in order to supply the DM with sorting obtained by an example-based procedure driven by the chosen function. Three case studies illustrating the use of a representative value function in real-world decision problems are presented. One of those studies is devoted to the comparison of the introduced concept of representativeness with alternative procedures for determining a single value function, which we adapted to sorting problems, because they were originally proposed for ranking problems.     

Robust ordinal regression in preference learning and ranking

Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking.     

Rapid Assessment of Frying Performance Using Small Size Samples of Oils/Fats

A rapid, effective test mimicking actual frying was developed to assess the frying performance of oils and fats using small size samples. To a small volume of the oil to be tested, a formulated food consisting of gelatinized potato starch, glucose and silica gel (4:1:1 w/w) were added and content heated at 185 ± 5 °C with mixing for 2 h. Thermo-oxidative degradation of the oil was assessed by the measurement of the total amount of polar components and their composition, including degradation of tocopherols. The developed fast test accurately mimics actual frying done using an institutional fryer as assessed by the accumulation and composition of total polar components and the amount of residual tocopherols. The validity of the test was assessed using the following oils: regular canola, high oleic– low linolenic canola, and high oleic sunflower. Comparison of data between the fast frying test and institutional frying revealed a lack of significant differences. The developed frying test provides reliable quantitative and qualitative data describing the performance of the frying oil/fat. The rapid frying procedure allows assessment of the frying performance of oils at the early stages of development where usually only small amounts of the sample are available and when a large number of samples have to be tested assessing effects of oil additives.     

Structure and morphology of linear polyethylene extrudates induced by elongational flow

This study focuses on the structure, morphology, and properties of linear polyethylene (PE) profiles manufactured by continuous extrusion. High level of chain orientation was achieved using specific flow and processing conditions. An extrusion die with semihyperbolic convergency was used to generate high percentage of elongational flow and chain extension. Simultaneously, high extrusion pressure and relatively low melt temperature led to flow‐induced crystallization of PE extended chains. The structure of PE tapes consists of crystal aggregates with different level of orientation and crystallinity. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Investigations on electrochemical α-methoxylation of selected dipeptides

Voltamperometric studies on the indirect electrochemical α-methoxylation of Boc-Pro-Gly-OMe and Boc-Val-Gly-OMe in MeOH in the presence of NaCl or NaBr as the mediator suggested that the first reaction step was a direct N-halogenation of the dipeptide by active chlorine or bromine adsorbed on the electrode surface. The kind of mediator (NaCl or NaBr), its concentration, the current density, and the applied electric charge had a significant influence on the reaction course. In the case of Boc-Pro-Gly-OMe, the use of sodium bromide was necessary to obtain a relatively high ratio of α-monomethoxylation to α,α-dimethoxylation. For Boc-Val-Gly-OMe, the selectivity for α-monomethoxylation was close to 100%, independently of the mediator. Optimisation of the selected electrolysis parameters allowed us to significantly improve the yield and selectivity of the α-methoxylation of Boc-Pro-Gly-OMe (Kardassis et al. Tetrahedron 54:3471, 1998) and to obtain good results in the α-methoxylation of Boc-Val-Gly-OMe.