Scientific rationale toward cupola design

Numerous improvements in cupola design and operation have been made in the past to increase productivity, spout temperature and fuel efficiency. However, these improvements have been based on practice and experience. The present work establishes a scientific rationale for cupola design and operation using a mathematical model. The improved performance of a divided-blast cupola over the conventional one has been successfully explained by the model. Performance of a cupola, as influenced by the important design parameter--the distance of separation between the two rows of tuyeres and operational parameters, such as size of coke and metallic charge, blast rate and charge level--was analyzed. For a divided-blast cupola, an optimum distance of 800-900 mm separation between the two rows of tuyeres was found to be ideal, irrespective of the size of cupola.

Scientific and technological aspects of fixed bed biomass gasification

In recent years new emphasis has been placed on problems of the environmental aspects of waste disposal, especially investigating alternatives to landfill, sea dumping and incineration. There is also a strong emphasis on clean, economic and efficient processes for electric power generation. These two topics may at first appear unrelated. Nevertheless, the technological advances are now such that a solution to both can be combined in a novel approach to power generation based on waste-derived fuels, including refuse-derived fuel (RDF) and sludge power (SP) by utilising a slagging gasifier and advance fuel technology (AFT). The most appropriate gasification technique for such waste utilisation is the British Gas/Lurgi (BGL) high pressure, fixed bed slagging gasifier where operation on a range of feedstocks has been well-documented. This gasifier is particularly amenable to briquette fuel feeding and, operating in an integrated gasification combined cycle mode (IGCC), is particularly advantageous. Here, the author details how this technology has been applied to Britain's first AFT-IGCC Power Station which is now under development at Fife Energy Ltd., in Scotland, the former British Gas Westfield Development Centre.

Mining Unit Tests for Discovery and Migration of Math APIs

Today's programming languages are supported by powerful third-party APIs. For a given application domain, it is common to have many competing APIs that provide similar functionality. Programmer productivity therefore depends heavily on the programmer's ability to discover suitable APIs both during an initial coding phase, as well as during software maintenance. The aim of this work is to support the discovery and migration of math APIs. Math APIs are at the heart of many application domains ranging from machine learning to scientific computations. Our approach, called MATHFINDER, combines executable specifications of mathematical computations with unit tests (operational specifications) of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code comprised of API methods to compute the expression by mining unit tests of the API methods. We present a sequential version of our unit test mining algorithm and also design a more scalable data-parallel version. We perform extensive evaluation of MATHFINDER (1) for API discovery, where math algorithms are to be implemented from scratch and (2) for API migration, where client programs utilizing a math API are to be migrated to another API. We evaluated the precision and recall of MATHFINDER on a diverse collection of math expressions, culled from algorithms used in a wide range of application areas such as control systems and structural dynamics. In a user study to evaluate the productivity gains obtained by using MATHFINDER for API discovery, the programmers who used MATHFINDER finished their programming tasks twice as fast as their counterparts who used the usual techniques like web and code search, IDE code completion, and manual inspection of library documentation. For the problem of API migration, as a case study, we used MATHFINDER to migrate Weka, a popular machine learning library. Overall, our evaluation shows that MATHFINDER is easy to use, provides highly precise results across several math APIs and application domains even with a small number of unit tests per method, and scales to large collections of unit tests.