No abstract

A fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO 3 , and C 3 S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If -Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.

No abstract

Production of porous anorthite ceramics from mixtures of paper processing residues and three different clays are investigated. Suitability of three different clays such as enriched clay, commercial clay and fireclay for manufacturing of anorthite based lightweight refractory bricks was studied. Porous character to the ceramic was provided by addition of paper processing residues (PPR). Samples with 30-40 wt% PPR fired at 1200-1400• C contained anorthite (CaO·Al 2 O 3 ·2SiO 2 ) as major phase and some minor secondary phases such as mullite (3Al 2 O 3 ·2SiO 2 ) or gehlenite (2CaO·Al 2 O 3 ·SiO 2 ), depending on the calcite to clay ratio. Anorthite formation for all clay types was quite successful in samples with 30-40 wt% of paper residues fired at 1300• C. A higher firing temperature of 1400• C was needed for the fireclay added samples to produce a well sintered product with large pores. Gehlenite phase occurred mostly at lower temperatures and in samples containing higher amount of calcium (50 wt% PPR). Compressive strength of compacted and fired pellets consisting of mainly anorthite ranged from 8 to 43 MPa.

In this paper, results of a project aimed at modelling the compressive strength of cement mortar under standard curing conditions are reported. Plant data were collected for 6 months for the chemical and physical properties of the cement that were used in model construction and testing. The training and testing data were separated from the complete original data set by the use of genetic algorithms (GAs). A GAartificial neural network (ANN) model based on the training data of the cement strength was created. Testing of the model was also done within low average error levels (2.24%). The model was subjected to sensitivity analysis to predict the response of the system to different values of the factors affecting the strength. The plots obtained after sensitivity analysis indicated that increasing the amount of C 3 S, SO 3 and surface area led to increased strength within the limits of the model. C 2 S decreased the strength whereas C 3 A decreased or increased the strength depending on the SO 3 level. Because of the limited data range used for training, the prediction results were good only within the same range. The utility of the model is in the potential ability to control processing parameters to yield the desired strength levels and in providing information regarding the most favourable experimental conditions to obtain maximum compressive strength. D