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Neural network analysis for prediction of interactions in cement / waste systems

Objective



The presence of impurities can have damaging effects on the quality of products made with portland cement, which the difficult to predict. Nevertheless, introduction of impurities into cement systems is inherent to recycling of industrial by-products by utilization in cement-based building materials and in treatment of industrial wastes by cement-based solidification prior to disposal. The consequences of design of cement/waste products without proper consideration of the potential for complex interactions between cementing components and impurities, are: handling difficulties, failure to set, improper strength development, and deterioration over time. These hazards, and the difficulty in predicting their occurrence, have hindered both utilization and/or solidification of industrial by-products, because potential benefits are outweighed by the expense of failures, even for applications where there is no risk to human health or the environment (e.g., road construction with inert residues). The use of conventional modelling and data analysis techniques for prediction of interactions and final properties in cementlwaste systems is limited by the complexity and poor understanding of the mechanisms involved. Neural network analysis is a promising technique for finding relationships in complex non-linear systems SUC]I as blended cements containing waste. A neural network is a mathematical model which is capable of parallel processing of a variety of inputs to identify patterns in large data sets of many variables, and to make predictions based on these patterns. An inevitable consequence of neural network analysis is that any relationships identified between variables in a dataset are non-mechanistic. While this might appear to be a drawback, the advantage over conventional data analysis techniques is that "unpredictable" relationships can be identified without a preliminary mechanistic hypothesis. Interpretation of such relationships may then lead to new mechanistic insights. A rev iew of the state-of-the-art has shown that neural networks have found useful application in problems of similar complexity, but the application to cement systems containing waste is new. The primary objective of the proposed work is to examine the use of neural network analysis for predicting interactions in, and final properties of, cement/waste systems. Identification of predictive relationships will facilitate effective design of cement/waste products for utilisation or disposal and allow selection of the most informative test methods for product evaluation. Discovery of new relationships between properties of cement/waste products is expected to result in new indications concerning contaminant immobilization mechanisms, which will be useful information for other fundamental modelling approaches (e.g. thermodynamic modelling), and in development of new binder systems. In the longer term (i.e., not as a part of the proposed RTD project), the findings from this project are expected ta lead to development of a knowledge-based diagnostic system for predicting interactions between components of complex cementing systems, and final product properties. Such a product will be useful for operation of solidification facilities, where it could be used in design of cement/waste products. It would enable more rapid response to tender opportunities, eliminate or reduce the time and expense required by laboratory treatability studies, allow process refinement during full-scale processing based on feedback of quality analysis and control data, and reduce the requirement for and subjectivity associated with human expertise. It could also be used to predict product final properties and allow assessment of their suitability for utilization in construction applications or specific disposal scenarios. The existence of such a diagnostic system will decrease the environmental risk associated with disposal of hazardous wastes, and promote the utilization of industrial by-products in cement-basecd building materials

Funding Scheme

CSC - Cost-sharing contracts

Coordinator

IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Address
Imperial College Road, Exhibition Road
SW7 2BU London
United Kingdom

Participants (7)

British Nuclear Fuels PLC
United Kingdom
Address

CA20 1PG B Sellafield - Seascale
Energiesysteme Nord GmbH
Germany
Address
Walkerdamm 17
24103 Kiel
Euroresiduos SA
Spain
Address
102,Cl. Coso 102
50001 Casa Zaragoza
THE PROVOST, FELLOWS AND SCHOLARS OF THE COLLEGE OF THE HOLY AND UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN HEREINAFTER TRINITY COLLEGE DUBLIN
Ireland
Address
College Green
2 Dublin
UNIVERSITY OF ROME "LA SAPIENZA"
Italy
Address
Via Eudossiana 18
00184 Roma
Universidad de Cantabria
Spain
Address
S/n,avenida De Los Castros
39005 Santander
University of Surrey
United Kingdom
Address

GU2 5XH Guildford