Eco-efficiency and dairy processing

The dairy industry is a global industry that provides significant nutritional benefit to many cultures. in australia the industry is especially important economically, being a large export earner, as well as a vital domestic sector. in recent years the sector has come under increased competitive pressure and has restructured to cope with the changes. the industry recently undertook an eco-efficiency project to investigate where business and environmental improvements might be found. the project involved collecting and collating previous project data and surveying 38 companies in different dairy operations, from market milk to dried products. after the survey, 10 sites in two states were visited to discuss eco-efficiency issues in detail with key players. From the surveys, visits and data compilation, a comprehensive manual was prepared to help interested companies find relevant eco-efficiency data easily and assist them in the implementation process. ten fact sheets were also produced covering the topics of water management, water recycling and re-use, refrigeration optimisation, boiler optimisation, biogas, the use of treated wastewater, yield optimisation and product recovery, optimisation of ciP systems, chemical use and membranes the project highlighted the large amount of technical and engineering expertise within the sector that could result in eco-efficiency outcomes and also identified the opportunities that exist for changes to occur in some operations to save energy, input raw materials and water.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Mutation analysis to verify feature matrices for isolating errors in simulation models

Software simulation models are computer programs that need to be verified and debugged like any other software. In previous work, a method for error isolation in simulation models has been proposed. The method relies on a set of feature matrices that can be used to determine which part of the model implementation is responsible for deviations in the output of the model. Currrently these feature matrices have to be generated by hand from the model implementation, which is a tedious and error-prone task. In this paper, a method based on mutation analysis, as well as prototype tool support for the verification of the manually generated feature matrices is presented. The application of the method and tool to a model for wastewater treatment shows that the feature matrices can be verified effectively using a minimal number of mutants.

Critical feature analysis of a radiotherapy machine

The software implementation of the emergency shutdown feature in a major radiotherapy system was analyzed, using a directed form of code review based on module dependences. Dependences between modules are labelled by particular assumptions; this allows one to trace through the code, and identify those fragments responsible for critical features. An `assumption tree' is constructed in parallel, showing the assumptions which each module makes about others. The root of the assumption tree is the critical feature of interest, and its leaves represent assumptions which, if not valid, might cause the critical feature to fail. The analysis revealed some unexpected assumptions that motivated improvements to the code.